Neural-Networks-example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Neural network\n",
    "\n",
    "* Example implementation of a neural network: multilayer perceptron\n",
    "* Derive surface salinity from surface temperature, surface, longitude, latitude and time \n",
    "* Training data: ROMS model of the Ligurian Sea"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load all necessary modules. Some modules might need to be installed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "using Statistics\n",
    "using NCDatasets\n",
    "using Flux\n",
    "using Flux: throttle\n",
    "using PyPlot\n",
    "using MLDataUtils"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Download and inspect the input data file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[31mNCDataset: ocean_avg_part.nc\u001b[39m\n",
       "Group: /\n",
       "\n",
       "\u001b[31mDimensions\u001b[39m\n",
       "   tracer = 2\n",
       "   s_rho = 32\n",
       "   s_w = 33\n",
       "   boundary = 4\n",
       "   eta_rho = 150\n",
       "   xi_rho = 276\n",
       "   eta_psi = 149\n",
       "   xi_psi = 275\n",
       "   eta_u = 150\n",
       "   xi_u = 275\n",
       "   eta_v = 149\n",
       "   xi_v = 276\n",
       "   ocean_time = 4\n",
       "\n",
       "\u001b[31mVariables\u001b[39m\n",
       "\u001b[32m  Akk_bak\u001b[39m  \n",
       "    Attributes:\n",
       "     long_name            = \u001b[36mbackground vertical mixing coefficient for turbulent energy\u001b[39m\n",
       "     units                = \u001b[36mmeter2 second-1\u001b[39m\n",
       "\n",
       "\u001b[32m  Akp_bak\u001b[39m  \n",
       "    Attributes:\n",
       "     long_name            = \u001b[36mbackground vertical mixing coefficient for length scale\u001b[39m\n",
       "     units                = \u001b[36mmeter2 second-1\u001b[39m\n",
       "\n",
       "\u001b[32m  Akt_bak\u001b[39m   (2)\n",
       "    Datatype:    Float64\n",
       "    Dimensions:  tracer\n",
       "    Attributes:\n",
       "     long_name            = \u001b[36mbackground vertical mixing coefficient for tracers\u001b[39m\n",
       "     units                = \u001b[36mmeter2 second-1\u001b[39m\n",
       "\n",
       "\u001b[32m  Akv_bak\u001b[39m  \n",
       "    Attributes:\n",
       "     long_name            = \u001b[36mbackground vertical mixing coefficient for momentum\u001b[39m\n",
       "     units                = \u001b[36mmeter2 second-1\u001b[39m\n",
       "\n",
       "\u001b[32m  Cs_r\u001b[39m   (32)\n",
       "    Datatype:    Float64\n",
       "    Dimensions:  s_rho\n",
       "    Attributes:\n",
       "     long_name            = \u001b[36mS-coordinate stretching curves at RHO-points\u001b[39m\n",
       "     valid_min            = \u001b[36m-1.0\u001b[39m\n",
       "     valid_max            = \u001b[36m0.0\u001b[39m\n",
       "     field                = \u001b[36mCs_r, scalar\u001b[39m\n",
       "\n",
       "\u001b[32m  Cs_w\u001b[39m   (33)\n",
       "    Datatype:    Float64\n",
       "    Dimensions:  s_w\n",
       "    Attributes:\n",
       "     long_name            = \u001b[36mS-coordinate stretching curves at W-points\u001b[39m\n",
       "     valid_min            = \u001b[36m-1.0\u001b[39m\n",
       "     valid_max            = \u001b[36m0.0\u001b[39m\n",
       "     field                = \u001b[36mCs_w, scalar\u001b[39m\n",
       "\n",
       "\u001b[32m  FSobc_in\u001b[39m   (4)\n",
       "    Datatype:    Float64\n",
       "    Dimensions:  boundary\n",
       "    Attributes:\n",
       "     long_name            = \u001b[36mfree-surface inflow, nudging inverse time scale\u001b[39m\n",
       "     units                = \u001b[36msecond-1\u001b[39m\n",
       "\n",
       "\u001b[32m  FSobc_out\u001b[39m   (4)\n",
       "    Datatype:    Float64\n",
       "    Dimensions:  boundary\n",
       "    Attributes:\n",
       "     long_name            = \u001b[36mfree-surface outflow, nudging inverse time scale\u001b[39m\n",
       "     units                = \u001b[36msecond-1\u001b[39m\n",
       "\n",
       "\u001b[32m  Falpha\u001b[39m  \n",
       "    Attributes:\n",
       "     long_name            = \u001b[36mPower-law shape barotropic filter parameter\u001b[39m\n",
       "\n",
       "\u001b[32m  Fbeta\u001b[39m  \n",
       "    Attributes:\n",
       "     long_name            = \u001b[36mPower-law shape barotropic filter parameter\u001b[39m\n",
       "\n",
       "\u001b[32m  Fgamma\u001b[39m  \n",
       "    Attributes:\n",
       "     long_name            = \u001b[36mPower-law shape barotropic filter parameter\u001b[39m\n",
       "\n",
       "\u001b[32m  Lm2CLM\u001b[39m  \n",
       "    Attributes:\n",
       "     long_name            = \u001b[36m2D momentum climatology processing switch\u001b[39m\n",
       "     flag_values          = \u001b[36mInt32[0, 1]\u001b[39m\n",
       "     flag_meanings        = \u001b[36m.FALSE. .TRUE.\u001b[39m\n",
       "\n",
       "\u001b[32m  Lm3CLM\u001b[39m  \n",
       "    Attributes:\n",
       "     long_name            = \u001b[36m3D momentum climatology processing switch\u001b[39m\n",
       "     flag_values          = \u001b[36mInt32[0, 1]\u001b[39m\n",
       "     flag_meanings        = \u001b[36m.FALSE. .TRUE.\u001b[39m\n",
       "\n",
       "\u001b[32m  LnudgeM2CLM\u001b[39m  \n",
       "    Attributes:\n",
       "     long_name            = \u001b[36m2D momentum climatology nudging activation switch\u001b[39m\n",
       "     flag_values          = \u001b[36mInt32[0, 1]\u001b[39m\n",
       "     flag_meanings        = \u001b[36m.FALSE. .TRUE.\u001b[39m\n",
       "\n",
       "\u001b[32m  LnudgeM3CLM\u001b[39m  \n",
       "    Attributes:\n",
       "     long_name            = \u001b[36m3D momentum climatology nudging activation switch\u001b[39m\n",
       "     flag_values          = \u001b[36mInt32[0, 1]\u001b[39m\n",
       "     flag_meanings        = \u001b[36m.FALSE. .TRUE.\u001b[39m\n",
       "\n",
       "\u001b[32m  LnudgeTCLM\u001b[39m   (2)\n",
       "    Datatype:    Int32\n",
       "    Dimensions:  tracer\n",
       "    Attributes:\n",
       "     long_name            = \u001b[36mtracer climatology nudging activation switch\u001b[39m\n",
       "     flag_values          = \u001b[36mInt32[0, 1]\u001b[39m\n",
       "     flag_meanings        = \u001b[36m.FALSE. .TRUE.\u001b[39m\n",
       "\n",
       "\u001b[32m  LsshCLM\u001b[39m  \n",
       "    Attributes:\n",
       "     long_name            = \u001b[36msea surface height climatology processing switch\u001b[39m\n",
       "     flag_values          = \u001b[36mInt32[0, 1]\u001b[39m\n",
       "     flag_meanings        = \u001b[36m.FALSE. .TRUE.\u001b[39m\n",
       "\n",
       "\u001b[32m  LtracerCLM\u001b[39m   (2)\n",
       "    Datatype:    Int32\n",
       "    Dimensions:  tracer\n",
       "    Attributes:\n",
       "     long_name            = \u001b[36mtracer climatology processing switch\u001b[39m\n",
       "     flag_values          = \u001b[36mInt32[0, 1]\u001b[39m\n",
       "     flag_meanings        = \u001b[36m.FALSE. .TRUE.\u001b[39m\n",
       "\n",
       "\u001b[32m  LtracerSponge\u001b[39m   (2)\n",
       "    Datatype:    Int32\n",
       "    Dimensions:  tracer\n",
       "    Attributes:\n",
       "     long_name            = \u001b[36mhorizontal diffusivity sponge activation switch\u001b[39m\n",
       "     flag_values          = \u001b[36mInt32[0, 1]\u001b[39m\n",
       "     flag_meanings        = \u001b[36m.FALSE. .TRUE.\u001b[39m\n",
       "\n",
       "\u001b[32m  LtracerSrc\u001b[39m   (2)\n",
       "    Datatype:    Int32\n",
       "    Dimensions:  tracer\n",
       "    Attributes:\n",
       "     long_name            = \u001b[36mtracer point sources and sink activation switch\u001b[39m\n",
       "     flag_values          = \u001b[36mInt32[0, 1]\u001b[39m\n",
       "     flag_meanings        = \u001b[36m.FALSE. .TRUE.\u001b[39m\n",
       "\n",
       "\u001b[32m  LuvSponge\u001b[39m  \n",
       "    Attributes:\n",
       "     long_name            = \u001b[36mhorizontal viscosity sponge activation switch\u001b[39m\n",
       "     flag_values          = \u001b[36mInt32[0, 1]\u001b[39m\n",
       "     flag_meanings        = \u001b[36m.FALSE. .TRUE.\u001b[39m\n",
       "\n",
       "\u001b[32m  LuvSrc\u001b[39m  \n",
       "    Attributes:\n",
       "     long_name            = \u001b[36mmomentum point sources and sink activation switch\u001b[39m\n",
       "     flag_values          = \u001b[36mInt32[0, 1]\u001b[39m\n",
       "     flag_meanings        = \u001b[36m.FALSE. .TRUE.\u001b[39m\n",
       "\n",
       "\u001b[32m  LwSrc\u001b[39m  \n",
       "    Attributes:\n",
       "     long_name            = \u001b[36mmass point sources and sink activation switch\u001b[39m\n",
       "     flag_values          = \u001b[36mInt32[0, 1]\u001b[39m\n",
       "     flag_meanings        = \u001b[36m.FALSE. .TRUE.\u001b[39m\n",
       "\n",
       "\u001b[32m  M2nudg\u001b[39m  \n",
       "    Attributes:\n",
       "     long_name            = \u001b[36m2D momentum nudging/relaxation inverse time scale\u001b[39m\n",
       "     units                = \u001b[36mday-1\u001b[39m\n",
       "\n",
       "\u001b[32m  M2obc_in\u001b[39m   (4)\n",
       "    Datatype:    Float64\n",
       "    Dimensions:  boundary\n",
       "    Attributes:\n",
       "     long_name            = \u001b[36m2D momentum inflow, nudging inverse time scale\u001b[39m\n",
       "     units                = \u001b[36msecond-1\u001b[39m\n",
       "\n",
       "\u001b[32m  M2obc_out\u001b[39m   (4)\n",
       "    Datatype:    Float64\n",
       "    Dimensions:  boundary\n",
       "    Attributes:\n",
       "     long_name            = \u001b[36m2D momentum outflow, nudging inverse time scale\u001b[39m\n",
       "     units                = \u001b[36msecond-1\u001b[39m\n",
       "\n",
       "\u001b[32m  M3nudg\u001b[39m  \n",
       "    Attributes:\n",
       "     long_name            = \u001b[36m3D momentum nudging/relaxation inverse time scale\u001b[39m\n",
       "     units                = \u001b[36mday-1\u001b[39m\n",
       "\n",
       "\u001b[32m  M3obc_in\u001b[39m   (4)\n",
       "    Datatype:    Float64\n",
       "    Dimensions:  boundary\n",
       "    Attributes:\n",
       "     long_name            = \u001b[36m3D momentum inflow, nudging inverse time scale\u001b[39m\n",
       "     units                = \u001b[36msecond-1\u001b[39m\n",
       "\n",
       "\u001b[32m  M3obc_out\u001b[39m   (4)\n",
       "    Datatype:    Float64\n",
       "    Dimensions:  boundary\n",
       "    Attributes:\n",
       "     long_name            = \u001b[36m3D momentum outflow, nudging inverse time scale\u001b[39m\n",
       "     units                = \u001b[36msecond-1\u001b[39m\n",
       "\n",
       "\u001b[32m  Tcline\u001b[39m  \n",
       "    Attributes:\n",
       "     long_name            = \u001b[36mS-coordinate surface/bottom layer width\u001b[39m\n",
       "     units                = \u001b[36mmeter\u001b[39m\n",
       "\n",
       "\u001b[32m  Tnudg\u001b[39m   (2)\n",
       "    Datatype:    Float64\n",
       "    Dimensions:  tracer\n",
       "    Attributes:\n",
       "     long_name            = \u001b[36mTracers nudging/relaxation inverse time scale\u001b[39m\n",
       "     units                = \u001b[36mday-1\u001b[39m\n",
       "\n",
       "\u001b[32m  Tobc_in\u001b[39m   (2 × 4)\n",
       "    Datatype:    Float64\n",
       "    Dimensions:  tracer × boundary\n",
       "    Attributes:\n",
       "     long_name            = \u001b[36mtracers inflow, nudging inverse time scale\u001b[39m\n",
       "     units                = \u001b[36msecond-1\u001b[39m\n",
       "\n",
       "\u001b[32m  Tobc_out\u001b[39m   (2 × 4)\n",
       "    Datatype:    Float64\n",
       "    Dimensions:  tracer × boundary\n",
       "    Attributes:\n",
       "     long_name            = \u001b[36mtracers outflow, nudging inverse time scale\u001b[39m\n",
       "     units                = \u001b[36msecond-1\u001b[39m\n",
       "\n",
       "\u001b[32m  Vstretching\u001b[39m  \n",
       "    Attributes:\n",
       "     long_name            = \u001b[36mvertical terrain-following stretching function\u001b[39m\n",
       "\n",
       "\u001b[32m  Vtransform\u001b[39m  \n",
       "    Attributes:\n",
       "     long_name            = \u001b[36mvertical terrain-following transformation equation\u001b[39m\n",
       "\n",
       "\u001b[32m  Znudg\u001b[39m  \n",
       "    Attributes:\n",
       "     long_name            = \u001b[36mfree-surface nudging/relaxation inverse time scale\u001b[39m\n",
       "     units                = \u001b[36mday-1\u001b[39m\n",
       "\n",
       "\u001b[32m  Zob\u001b[39m  \n",
       "    Attributes:\n",
       "     long_name            = \u001b[36mbottom roughness\u001b[39m\n",
       "     units                = \u001b[36mmeter\u001b[39m\n",
       "\n",
       "\u001b[32m  Zos\u001b[39m  \n",
       "    Attributes:\n",
       "     long_name            = \u001b[36msurface roughness\u001b[39m\n",
       "     units                = \u001b[36mmeter\u001b[39m\n",
       "\n",
       "\u001b[32m  dstart\u001b[39m  \n",
       "    Attributes:\n",
       "     long_name            = \u001b[36mtime stamp assigned to model initilization\u001b[39m\n",
       "     units                = \u001b[36mdays since 1858-11-17 00:00:00\u001b[39m\n",
       "\n",
       "\u001b[32m  dt\u001b[39m  \n",
       "    Attributes:\n",
       "     long_name            = \u001b[36msize of long time-steps\u001b[39m\n",
       "     units                = \u001b[36msecond\u001b[39m\n",
       "\n",
       "\u001b[32m  dtfast\u001b[39m  \n",
       "    Attributes:\n",
       "     long_name            = \u001b[36msize of short time-steps\u001b[39m\n",
       "     units                = \u001b[36msecond\u001b[39m\n",
       "\n",
       "\u001b[32m  el\u001b[39m  \n",
       "    Attributes:\n",
       "     long_name            = \u001b[36mdomain length in the ETA-direction\u001b[39m\n",
       "     units                = \u001b[36mmeter\u001b[39m\n",
       "\n",
       "\u001b[32m  f\u001b[39m   (276 × 150)\n",
       "    Datatype:    Float64\n",
       "    Dimensions:  xi_rho × eta_rho\n",
       "    Attributes:\n",
       "     long_name            = \u001b[36mCoriolis parameter at RHO-points\u001b[39m\n",
       "     units                = \u001b[36msecond-1\u001b[39m\n",
       "     grid                 = \u001b[36mgrid\u001b[39m\n",
       "     location             = \u001b[36mface\u001b[39m\n",
       "     coordinates          = \u001b[36mlon_rho lat_rho\u001b[39m\n",
       "     field                = \u001b[36mcoriolis, scalar\u001b[39m\n",
       "\n",
       "\u001b[32m  gamma2\u001b[39m  \n",
       "    Attributes:\n",
       "     long_name            = \u001b[36mslipperiness parameter\u001b[39m\n",
       "\n",
       "\u001b[32m  grid\u001b[39m  \n",
       "    Attributes:\n",
       "     cf_role              = \u001b[36mgrid_topology\u001b[39m\n",
       "     topology_dimension   = \u001b[36m2\u001b[39m\n",
       "     node_dimensions      = \u001b[36mxi_psi eta_psi\u001b[39m\n",
       "     face_dimensions      = \u001b[36mxi_rho: xi_psi (padding: both) eta_rho: eta_psi (padding: both)\u001b[39m\n",
       "     edge1_dimensions     = \u001b[36mxi_u: xi_psi eta_u: eta_psi (padding: both)\u001b[39m\n",
       "     edge2_dimensions     = \u001b[36mxi_v: xi_psi (padding: both) eta_v: eta_psi\u001b[39m\n",
       "     node_coordinates     = \u001b[36mlon_psi lat_psi\u001b[39m\n",
       "     face_coordinates     = \u001b[36mlon_rho lat_rho\u001b[39m\n",
       "     edge1_coordinates    = \u001b[36mlon_u lat_u\u001b[39m\n",
       "     edge2_coordinates    = \u001b[36mlon_v lat_v\u001b[39m\n",
       "     vertical_dimensions  = \u001b[36ms_rho: s_w (padding: none)\u001b[39m\n",
       "\n",
       "\u001b[32m  h\u001b[39m   (276 × 150)\n",
       "    Datatype:    Float64\n",
       "    Dimensions:  xi_rho × eta_rho\n",
       "    Attributes:\n",
       "     long_name            = \u001b[36mbathymetry at RHO-points\u001b[39m\n",
       "     units                = \u001b[36mmeter\u001b[39m\n",
       "     grid                 = \u001b[36mgrid\u001b[39m\n",
       "     location             = \u001b[36mface\u001b[39m\n",
       "     coordinates          = \u001b[36mlon_rho lat_rho\u001b[39m\n",
       "     field                = \u001b[36mbath, scalar\u001b[39m\n",
       "\n",
       "\u001b[32m  hc\u001b[39m  \n",
       "    Attributes:\n",
       "     long_name            = \u001b[36mS-coordinate parameter, critical depth\u001b[39m\n",
       "     units                = \u001b[36mmeter\u001b[39m\n",
       "\n",
       "\u001b[32m  lat_psi\u001b[39m   (275 × 149)\n",
       "    Datatype:    Float64\n",
       "    Dimensions:  xi_psi × eta_psi\n",
       "    Attributes:\n",
       "     long_name            = \u001b[36mlatitude of PSI-points\u001b[39m\n",
       "     units                = \u001b[36mdegree_north\u001b[39m\n",
       "     standard_name        = \u001b[36mlatitude\u001b[39m\n",
       "     field                = \u001b[36mlat_psi, scalar\u001b[39m\n",
       "\n",
       "\u001b[32m  lat_rho\u001b[39m   (276 × 150)\n",
       "    Datatype:    Float64\n",
       "    Dimensions:  xi_rho × eta_rho\n",
       "    Attributes:\n",
       "     long_name            = \u001b[36mlatitude of RHO-points\u001b[39m\n",
       "     units                = \u001b[36mdegree_north\u001b[39m\n",
       "     standard_name        = \u001b[36mlatitude\u001b[39m\n",
       "     field                = \u001b[36mlat_rho, scalar\u001b[39m\n",
       "\n",
       "\u001b[32m  lat_u\u001b[39m   (275 × 150)\n",
       "    Datatype:    Float64\n",
       "    Dimensions:  xi_u × eta_u\n",
       "    Attributes:\n",
       "     long_name            = \u001b[36mlatitude of U-points\u001b[39m\n",
       "     units                = \u001b[36mdegree_north\u001b[39m\n",
       "     standard_name        = \u001b[36mlatitude\u001b[39m\n",
       "     field                = \u001b[36mlat_u, scalar\u001b[39m\n",
       "\n",
       "\u001b[32m  lat_v\u001b[39m   (276 × 149)\n",
       "    Datatype:    Float64\n",
       "    Dimensions:  xi_v × eta_v\n",
       "    Attributes:\n",
       "     long_name            = \u001b[36mlatitude of V-points\u001b[39m\n",
       "     units                = \u001b[36mdegree_north\u001b[39m\n",
       "     standard_name        = \u001b[36mlatitude\u001b[39m\n",
       "     field                = \u001b[36mlat_v, scalar\u001b[39m\n",
       "\n",
       "\u001b[32m  lon_psi\u001b[39m   (275 × 149)\n",
       "    Datatype:    Float64\n",
       "    Dimensions:  xi_psi × eta_psi\n",
       "    Attributes:\n",
       "     long_name            = \u001b[36mlongitude of PSI-points\u001b[39m\n",
       "     units                = \u001b[36mdegree_east\u001b[39m\n",
       "     standard_name        = \u001b[36mlongitude\u001b[39m\n",
       "     field                = \u001b[36mlon_psi, scalar\u001b[39m\n",
       "\n",
       "\u001b[32m  lon_rho\u001b[39m   (276 × 150)\n",
       "    Datatype:    Float64\n",
       "    Dimensions:  xi_rho × eta_rho\n",
       "    Attributes:\n",
       "     long_name            = \u001b[36mlongitude of RHO-points\u001b[39m\n",
       "     units                = \u001b[36mdegree_east\u001b[39m\n",
       "     standard_name        = \u001b[36mlongitude\u001b[39m\n",
       "     field                = \u001b[36mlon_rho, scalar\u001b[39m\n",
       "\n",
       "\u001b[32m  lon_u\u001b[39m   (275 × 150)\n",
       "    Datatype:    Float64\n",
       "    Dimensions:  xi_u × eta_u\n",
       "    Attributes:\n",
       "     long_name            = \u001b[36mlongitude of U-points\u001b[39m\n",
       "     units                = \u001b[36mdegree_east\u001b[39m\n",
       "     standard_name        = \u001b[36mlongitude\u001b[39m\n",
       "     field                = \u001b[36mlon_u, scalar\u001b[39m\n",
       "\n",
       "\u001b[32m  lon_v\u001b[39m   (276 × 149)\n",
       "    Datatype:    Float64\n",
       "    Dimensions:  xi_v × eta_v\n",
       "    Attributes:\n",
       "     long_name            = \u001b[36mlongitude of V-points\u001b[39m\n",
       "     units                = \u001b[36mdegree_east\u001b[39m\n",
       "     standard_name        = \u001b[36mlongitude\u001b[39m\n",
       "     field                = \u001b[36mlon_v, scalar\u001b[39m\n",
       "\n",
       "\u001b[32m  mask_psi\u001b[39m   (275 × 149)\n",
       "    Datatype:    Float64\n",
       "    Dimensions:  xi_psi × eta_psi\n",
       "    Attributes:\n",
       "     long_name            = \u001b[36mmask on psi-points\u001b[39m\n",
       "     flag_values          = \u001b[36m[0.0, 1.0]\u001b[39m\n",
       "     flag_meanings        = \u001b[36mland water\u001b[39m\n",
       "     grid                 = \u001b[36mgrid\u001b[39m\n",
       "     location             = \u001b[36mnode\u001b[39m\n",
       "     coordinates          = \u001b[36mlon_psi lat_psi\u001b[39m\n",
       "\n",
       "\u001b[32m  mask_rho\u001b[39m   (276 × 150)\n",
       "    Datatype:    Float64\n",
       "    Dimensions:  xi_rho × eta_rho\n",
       "    Attributes:\n",
       "     long_name            = \u001b[36mmask on RHO-points\u001b[39m\n",
       "     flag_values          = \u001b[36m[0.0, 1.0]\u001b[39m\n",
       "     flag_meanings        = \u001b[36mland water\u001b[39m\n",
       "     grid                 = \u001b[36mgrid\u001b[39m\n",
       "     location             = \u001b[36mface\u001b[39m\n",
       "     coordinates          = \u001b[36mlon_rho lat_rho\u001b[39m\n",
       "\n",
       "\u001b[32m  mask_u\u001b[39m   (275 × 150)\n",
       "    Datatype:    Float64\n",
       "    Dimensions:  xi_u × eta_u\n",
       "    Attributes:\n",
       "     long_name            = \u001b[36mmask on U-points\u001b[39m\n",
       "     flag_values          = \u001b[36m[0.0, 1.0]\u001b[39m\n",
       "     flag_meanings        = \u001b[36mland water\u001b[39m\n",
       "     grid                 = \u001b[36mgrid\u001b[39m\n",
       "     location             = \u001b[36medge1\u001b[39m\n",
       "     coordinates          = \u001b[36mlon_u lat_u\u001b[39m\n",
       "\n",
       "\u001b[32m  mask_v\u001b[39m   (276 × 149)\n",
       "    Datatype:    Float64\n",
       "    Dimensions:  xi_v × eta_v\n",
       "    Attributes:\n",
       "     long_name            = \u001b[36mmask on V-points\u001b[39m\n",
       "     flag_values          = \u001b[36m[0.0, 1.0]\u001b[39m\n",
       "     flag_meanings        = \u001b[36mland water\u001b[39m\n",
       "     grid                 = \u001b[36mgrid\u001b[39m\n",
       "     location             = \u001b[36medge2\u001b[39m\n",
       "     coordinates          = \u001b[36mlon_v lat_v\u001b[39m\n",
       "\n",
       "\u001b[32m  nAVG\u001b[39m  \n",
       "    Attributes:\n",
       "     long_name            = \u001b[36mnumber of time-steps between time-averaged records\u001b[39m\n",
       "\n",
       "\u001b[32m  nHIS\u001b[39m  \n",
       "    Attributes:\n",
       "     long_name            = \u001b[36mnumber of time-steps between history records\u001b[39m\n",
       "\n",
       "\u001b[32m  nRST\u001b[39m  \n",
       "    Attributes:\n",
       "     long_name            = \u001b[36mnumber of time-steps between restart records\u001b[39m\n",
       "     cycle                = \u001b[36monly latest two records are maintained\u001b[39m\n",
       "\n",
       "\u001b[32m  ndefAVG\u001b[39m  \n",
       "    Attributes:\n",
       "     long_name            = \u001b[36mnumber of time-steps between the creation of average files\u001b[39m\n",
       "\n",
       "\u001b[32m  ndefHIS\u001b[39m  \n",
       "    Attributes:\n",
       "     long_name            = \u001b[36mnumber of time-steps between the creation of history files\u001b[39m\n",
       "\n",
       "\u001b[32m  ndtfast\u001b[39m  \n",
       "    Attributes:\n",
       "     long_name            = \u001b[36mnumber of short time-steps\u001b[39m\n",
       "\n",
       "\u001b[32m  nl_tnu2\u001b[39m   (2)\n",
       "    Datatype:    Float64\n",
       "    Dimensions:  tracer\n",
       "    Attributes:\n",
       "     long_name            = \u001b[36mnonlinear model Laplacian mixing coefficient for tracers\u001b[39m\n",
       "     units                = \u001b[36mmeter2 second-1\u001b[39m\n",
       "\n",
       "\u001b[32m  nl_visc2\u001b[39m  \n",
       "    Attributes:\n",
       "     long_name            = \u001b[36mnonlinear model Laplacian mixing coefficient for momentum\u001b[39m\n",
       "     units                = \u001b[36mmeter2 second-1\u001b[39m\n",
       "\n",
       "\u001b[32m  ntimes\u001b[39m  \n",
       "    Attributes:\n",
       "     long_name            = \u001b[36mnumber of long time-steps\u001b[39m\n",
       "\n",
       "\u001b[32m  ntsAVG\u001b[39m  \n",
       "    Attributes:\n",
       "     long_name            = \u001b[36mstarting time-step for accumulation of time-averaged fields\u001b[39m\n",
       "\n",
       "\u001b[32m  ocean_time\u001b[39m   (4)\n",
       "    Datatype:    Float64\n",
       "    Dimensions:  ocean_time\n",
       "    Attributes:\n",
       "     long_name            = \u001b[36maveraged time since initialization\u001b[39m\n",
       "     units                = \u001b[36mseconds since 1858-11-17 00:00:00\u001b[39m\n",
       "     calendar             = \u001b[36mgregorian\u001b[39m\n",
       "     field                = \u001b[36mtime, scalar, series\u001b[39m\n",
       "\n",
       "\u001b[32m  omega\u001b[39m   (276 × 150 × 33 × 4)\n",
       "    Datatype:    Float32\n",
       "    Dimensions:  xi_rho × eta_rho × s_w × ocean_time\n",
       "    Attributes:\n",
       "     long_name            = \u001b[36mtime-averaged S-coordinate vertical momentum component\u001b[39m\n",
       "     units                = \u001b[36mmeter3 second-1\u001b[39m\n",
       "     time                 = \u001b[36mocean_time\u001b[39m\n",
       "     grid                 = \u001b[36mgrid\u001b[39m\n",
       "     location             = \u001b[36mface\u001b[39m\n",
       "     coordinates          = \u001b[36mlon_rho lat_rho s_w ocean_time\u001b[39m\n",
       "     field                = \u001b[36momega, scalar, series\u001b[39m\n",
       "     _FillValue           = \u001b[36m1.0e37\u001b[39m\n",
       "\n",
       "\u001b[32m  pm\u001b[39m   (276 × 150)\n",
       "    Datatype:    Float64\n",
       "    Dimensions:  xi_rho × eta_rho\n",
       "    Attributes:\n",
       "     long_name            = \u001b[36mcurvilinear coordinate metric in XI\u001b[39m\n",
       "     units                = \u001b[36mmeter-1\u001b[39m\n",
       "     grid                 = \u001b[36mgrid\u001b[39m\n",
       "     location             = \u001b[36mface\u001b[39m\n",
       "     coordinates          = \u001b[36mlon_rho lat_rho\u001b[39m\n",
       "     field                = \u001b[36mpm, scalar\u001b[39m\n",
       "\n",
       "\u001b[32m  pn\u001b[39m   (276 × 150)\n",
       "    Datatype:    Float64\n",
       "    Dimensions:  xi_rho × eta_rho\n",
       "    Attributes:\n",
       "     long_name            = \u001b[36mcurvilinear coordinate metric in ETA\u001b[39m\n",
       "     units                = \u001b[36mmeter-1\u001b[39m\n",
       "     grid                 = \u001b[36mgrid\u001b[39m\n",
       "     location             = \u001b[36mface\u001b[39m\n",
       "     coordinates          = \u001b[36mlon_rho lat_rho\u001b[39m\n",
       "     field                = \u001b[36mpn, scalar\u001b[39m\n",
       "\n",
       "\u001b[32m  rdrg\u001b[39m  \n",
       "    Attributes:\n",
       "     long_name            = \u001b[36mlinear drag coefficient\u001b[39m\n",
       "     units                = \u001b[36mmeter second-1\u001b[39m\n",
       "\n",
       "\u001b[32m  rdrg2\u001b[39m  \n",
       "    Attributes:\n",
       "     long_name            = \u001b[36mquadratic drag coefficient\u001b[39m\n",
       "\n",
       "\u001b[32m  rho0\u001b[39m  \n",
       "    Attributes:\n",
       "     long_name            = \u001b[36mmean density used in Boussinesq approximation\u001b[39m\n",
       "     units                = \u001b[36mkilogram meter-3\u001b[39m\n",
       "\n",
       "\u001b[32m  s_rho\u001b[39m   (32)\n",
       "    Datatype:    Float64\n",
       "    Dimensions:  s_rho\n",
       "    Attributes:\n",
       "     long_name            = \u001b[36mS-coordinate at RHO-points\u001b[39m\n",
       "     valid_min            = \u001b[36m-1.0\u001b[39m\n",
       "     valid_max            = \u001b[36m0.0\u001b[39m\n",
       "     positive             = \u001b[36mup\u001b[39m\n",
       "     standard_name        = \u001b[36mocean_s_coordinate_g2\u001b[39m\n",
       "     formula_terms        = \u001b[36ms: s_rho C: Cs_r eta: zeta depth: h depth_c: hc\u001b[39m\n",
       "     field                = \u001b[36ms_rho, scalar\u001b[39m\n",
       "\n",
       "\u001b[32m  s_w\u001b[39m   (33)\n",
       "    Datatype:    Float64\n",
       "    Dimensions:  s_w\n",
       "    Attributes:\n",
       "     long_name            = \u001b[36mS-coordinate at W-points\u001b[39m\n",
       "     valid_min            = \u001b[36m-1.0\u001b[39m\n",
       "     valid_max            = \u001b[36m0.0\u001b[39m\n",
       "     positive             = \u001b[36mup\u001b[39m\n",
       "     standard_name        = \u001b[36mocean_s_coordinate_g2\u001b[39m\n",
       "     formula_terms        = \u001b[36ms: s_w C: Cs_w eta: zeta depth: h depth_c: hc\u001b[39m\n",
       "     field                = \u001b[36ms_w, scalar\u001b[39m\n",
       "\n",
       "\u001b[32m  salt\u001b[39m   (276 × 150 × 32 × 4)\n",
       "    Datatype:    Float32\n",
       "    Dimensions:  xi_rho × eta_rho × s_rho × ocean_time\n",
       "    Attributes:\n",
       "     long_name            = \u001b[36mtime-averaged salinity\u001b[39m\n",
       "     time                 = \u001b[36mocean_time\u001b[39m\n",
       "     grid                 = \u001b[36mgrid\u001b[39m\n",
       "     location             = \u001b[36mface\u001b[39m\n",
       "     coordinates          = \u001b[36mlon_rho lat_rho s_rho ocean_time\u001b[39m\n",
       "     field                = \u001b[36msalinity, scalar, series\u001b[39m\n",
       "     _FillValue           = \u001b[36m1.0e37\u001b[39m\n",
       "\n",
       "\u001b[32m  spherical\u001b[39m  \n",
       "    Attributes:\n",
       "     long_name            = \u001b[36mgrid type logical switch\u001b[39m\n",
       "     flag_values          = \u001b[36mInt32[0, 1]\u001b[39m\n",
       "     flag_meanings        = \u001b[36mCartesian spherical\u001b[39m\n",
       "\n",
       "\u001b[32m  temp\u001b[39m   (276 × 150 × 32 × 4)\n",
       "    Datatype:    Float32\n",
       "    Dimensions:  xi_rho × eta_rho × s_rho × ocean_time\n",
       "    Attributes:\n",
       "     long_name            = \u001b[36mtime-averaged potential temperature\u001b[39m\n",
       "     units                = \u001b[36mCelsius\u001b[39m\n",
       "     time                 = \u001b[36mocean_time\u001b[39m\n",
       "     grid                 = \u001b[36mgrid\u001b[39m\n",
       "     location             = \u001b[36mface\u001b[39m\n",
       "     coordinates          = \u001b[36mlon_rho lat_rho s_rho ocean_time\u001b[39m\n",
       "     field                = \u001b[36mtemperature, scalar, series\u001b[39m\n",
       "     _FillValue           = \u001b[36m1.0e37\u001b[39m\n",
       "\n",
       "\u001b[32m  theta_b\u001b[39m  \n",
       "    Attributes:\n",
       "     long_name            = \u001b[36mS-coordinate bottom control parameter\u001b[39m\n",
       "\n",
       "\u001b[32m  theta_s\u001b[39m  \n",
       "    Attributes:\n",
       "     long_name            = \u001b[36mS-coordinate surface control parameter\u001b[39m\n",
       "\n",
       "\u001b[32m  u\u001b[39m   (275 × 150 × 32 × 4)\n",
       "    Datatype:    Float32\n",
       "    Dimensions:  xi_u × eta_u × s_rho × ocean_time\n",
       "    Attributes:\n",
       "     long_name            = \u001b[36mtime-averaged u-momentum component\u001b[39m\n",
       "     units                = \u001b[36mmeter second-1\u001b[39m\n",
       "     time                 = \u001b[36mocean_time\u001b[39m\n",
       "     grid                 = \u001b[36mgrid\u001b[39m\n",
       "     location             = \u001b[36medge1\u001b[39m\n",
       "     coordinates          = \u001b[36mlon_u lat_u s_rho ocean_time\u001b[39m\n",
       "     field                = \u001b[36mu-velocity, scalar, series\u001b[39m\n",
       "     _FillValue           = \u001b[36m1.0e37\u001b[39m\n",
       "\n",
       "\u001b[32m  ubar\u001b[39m   (275 × 150 × 4)\n",
       "    Datatype:    Float32\n",
       "    Dimensions:  xi_u × eta_u × ocean_time\n",
       "    Attributes:\n",
       "     long_name            = \u001b[36mtime-averaged vertically integrated u-momentum component\u001b[39m\n",
       "     units                = \u001b[36mmeter second-1\u001b[39m\n",
       "     time                 = \u001b[36mocean_time\u001b[39m\n",
       "     grid                 = \u001b[36mgrid\u001b[39m\n",
       "     location             = \u001b[36medge1\u001b[39m\n",
       "     coordinates          = \u001b[36mlon_u lat_u ocean_time\u001b[39m\n",
       "     field                = \u001b[36mubar-velocity, scalar, series\u001b[39m\n",
       "     _FillValue           = \u001b[36m1.0e37\u001b[39m\n",
       "\n",
       "\u001b[32m  v\u001b[39m   (276 × 149 × 32 × 4)\n",
       "    Datatype:    Float32\n",
       "    Dimensions:  xi_v × eta_v × s_rho × ocean_time\n",
       "    Attributes:\n",
       "     long_name            = \u001b[36mtime-averaged v-momentum component\u001b[39m\n",
       "     units                = \u001b[36mmeter second-1\u001b[39m\n",
       "     time                 = \u001b[36mocean_time\u001b[39m\n",
       "     grid                 = \u001b[36mgrid\u001b[39m\n",
       "     location             = \u001b[36medge2\u001b[39m\n",
       "     coordinates          = \u001b[36mlon_v lat_v s_rho ocean_time\u001b[39m\n",
       "     field                = \u001b[36mv-velocity, scalar, series\u001b[39m\n",
       "     _FillValue           = \u001b[36m1.0e37\u001b[39m\n",
       "\n",
       "\u001b[32m  vbar\u001b[39m   (276 × 149 × 4)\n",
       "    Datatype:    Float32\n",
       "    Dimensions:  xi_v × eta_v × ocean_time\n",
       "    Attributes:\n",
       "     long_name            = \u001b[36mtime-averaged vertically integrated v-momentum component\u001b[39m\n",
       "     units                = \u001b[36mmeter second-1\u001b[39m\n",
       "     time                 = \u001b[36mocean_time\u001b[39m\n",
       "     grid                 = \u001b[36mgrid\u001b[39m\n",
       "     location             = \u001b[36medge2\u001b[39m\n",
       "     coordinates          = \u001b[36mlon_v lat_v ocean_time\u001b[39m\n",
       "     field                = \u001b[36mvbar-velocity, scalar, series\u001b[39m\n",
       "     _FillValue           = \u001b[36m1.0e37\u001b[39m\n",
       "\n",
       "\u001b[32m  w\u001b[39m   (276 × 150 × 33 × 4)\n",
       "    Datatype:    Float32\n",
       "    Dimensions:  xi_rho × eta_rho × s_w × ocean_time\n",
       "    Attributes:\n",
       "     long_name            = \u001b[36mtime-averaged vertical momentum component\u001b[39m\n",
       "     units                = \u001b[36mmeter second-1\u001b[39m\n",
       "     time                 = \u001b[36mocean_time\u001b[39m\n",
       "     standard_name        = \u001b[36mupward_sea_water_velocity\u001b[39m\n",
       "     grid                 = \u001b[36mgrid\u001b[39m\n",
       "     location             = \u001b[36mface\u001b[39m\n",
       "     coordinates          = \u001b[36mlon_rho lat_rho s_w ocean_time\u001b[39m\n",
       "     field                = \u001b[36mw-velocity, scalar, series\u001b[39m\n",
       "     _FillValue           = \u001b[36m1.0e37\u001b[39m\n",
       "\n",
       "\u001b[32m  xl\u001b[39m  \n",
       "    Attributes:\n",
       "     long_name            = \u001b[36mdomain length in the XI-direction\u001b[39m\n",
       "     units                = \u001b[36mmeter\u001b[39m\n",
       "\n",
       "\u001b[32m  zeta\u001b[39m   (276 × 150 × 4)\n",
       "    Datatype:    Float32\n",
       "    Dimensions:  xi_rho × eta_rho × ocean_time\n",
       "    Attributes:\n",
       "     long_name            = \u001b[36mtime-averaged free-surface\u001b[39m\n",
       "     units                = \u001b[36mmeter\u001b[39m\n",
       "     time                 = \u001b[36mocean_time\u001b[39m\n",
       "     grid                 = \u001b[36mgrid\u001b[39m\n",
       "     location             = \u001b[36mface\u001b[39m\n",
       "     coordinates          = \u001b[36mlon_rho lat_rho ocean_time\u001b[39m\n",
       "     field                = \u001b[36mfree-surface, scalar, series\u001b[39m\n",
       "     _FillValue           = \u001b[36m1.0e37\u001b[39m\n",
       "\n",
       "\u001b[31mGlobal attributes\u001b[39m\n",
       "  file                 = \u001b[36mocean_avg.nc\u001b[39m\n",
       "  format               = \u001b[36mnetCDF-3 64bit offset file\u001b[39m\n",
       "  Conventions          = \u001b[36mCF-1.4, SGRID-0.3\u001b[39m\n",
       "  type                 = \u001b[36mROMS/TOMS nonlinear model averages file\u001b[39m\n",
       "  title                = \u001b[36mLigurian Sea\u001b[39m\n",
       "  var_info             = \u001b[36m../varinfo.dat\u001b[39m\n",
       "  rst_file             = \u001b[36mocean_rst.nc\u001b[39m\n",
       "  avg_file             = \u001b[36mocean_avg.nc\u001b[39m\n",
       "  grd_file             = \u001b[36m../LS2v.nc\u001b[39m\n",
       "  ini_file             = \u001b[36m../ocean_his_celine.nc\u001b[39m\n",
       "  frc_file_01          = \u001b[36m../liguriansea_wind_era_2015.nc\u001b[39m\n",
       "  frc_file_02          = \u001b[36m../liguriansea_Tair_era_2015.nc\u001b[39m\n",
       "  frc_file_03          = \u001b[36m../liguriansea_swrad_era_2015.nc\u001b[39m\n",
       "  frc_file_04          = \u001b[36m../liguriansea_cloud_era_2015.nc\u001b[39m\n",
       "  frc_file_05          = \u001b[36m../liguriansea_Pair_era_2015.nc\u001b[39m\n",
       "  frc_file_06          = \u001b[36m../liguriansea_Qair_era_2015.nc\u001b[39m\n",
       "  frc_file_07          = \u001b[36m../liguriansea_rain_era_2015.nc\u001b[39m\n",
       "  bry_file             = \u001b[36m../bc2015-2.nc\u001b[39m\n",
       "  clm_file             = \u001b[36m../clim2015-2.nc\u001b[39m\n",
       "  nud_file             = \u001b[36m../ocean_nud2.nc\u001b[39m\n",
       "  script_file          = \n",
       "  NLM_LBC              = \n",
       "\u001b[36mEDGE:  WEST   SOUTH  EAST   NORTH  \u001b[39m\n",
       "\u001b[36mzeta:  Cha    Cha    Clo    Clo    \u001b[39m\n",
       "\u001b[36mubar:  Fla    Fla    Clo    Clo    \u001b[39m\n",
       "\u001b[36mvbar:  Fla    Fla    Clo    Clo    \u001b[39m\n",
       "\u001b[36mu:     RadNud RadNud Clo    Clo    \u001b[39m\n",
       "\u001b[36mv:     RadNud RadNud Clo    Clo    \u001b[39m\n",
       "\u001b[36mtemp:  RadNud RadNud Clo    Clo    \u001b[39m\n",
       "\u001b[36msalt:  RadNud RadNud Clo    Clo    \u001b[39m\n",
       "\u001b[36mtke:   Rad    Rad    Clo    Clo\u001b[39m\n",
       "  svn_url              = \u001b[36mhttps://www.myroms.org/svn/src/trunk\u001b[39m\n",
       "  svn_rev              = \u001b[36m839M\u001b[39m\n",
       "  code_dir             = \u001b[36m/CECI/home/users/a/b/abarth/src/roms\u001b[39m\n",
       "  header_dir           = \u001b[36m/home/ulg/gher/abarth/Models/LS2v-update20170321\u001b[39m\n",
       "  header_file          = \u001b[36mls2.h\u001b[39m\n",
       "  os                   = \u001b[36mLinux\u001b[39m\n",
       "  cpu                  = \u001b[36mx86_64\u001b[39m\n",
       "  compiler_system      = \u001b[36mifort\u001b[39m\n",
       "  compiler_command     = \u001b[36m/home/ulg/gher/abarth/bin/mpif90\u001b[39m\n",
       "  compiler_flags       = \u001b[36m-heap-arrays -fp-model precise -ip -O3 -free -free -free\u001b[39m\n",
       "  tiling               = \u001b[36m005x005\u001b[39m\n",
       "  history              = \u001b[36mSun Dec 16 22:28:01 2018: ncks -d ocean_time,0,3 ocean_avg.nc --fl_fmt=netcdf4 --deflate 9 ocean_avg_part.nc\u001b[39m\n",
       "\u001b[36mROMS/TOMS, Version 3.7, Sunday - June 11, 2017 -  9:59:32 PM\u001b[39m\n",
       "  ana_file             = \u001b[36mROMS/Functionals/ana_btflux.h, ROMS/Functionals/ana_stflux.h\u001b[39m\n",
       "  CPP_options          = \u001b[36mLS, ANA_BSFLUX, ANA_BTFLUX, ANA_SSFLUX, ASSUMED_SHAPE, AVERAGES, BULK_FLUXES, DJ_GRADPS, DOUBLE_PRECISION, KANTHA_CLAYSON, LONGWAVE, MASKING, MIX_S_TS, MIX_S_UV, MPI, MY25_MIXING, NESTING, NONLINEAR, NONLIN_EOS, N2S2_HORAVG, !ONE_WAY, POWER_LAW, PROFILE, K_GSCHEME, !RST_SINGLE, SALINITY, SOLAR_SOURCE, SOLVE3D, TS_U3HADVECTION, TS_C4VADVECTION, TS_DIF2, UV_ADV, UV_COR, UV_U3HADVECTION, UV_C4VADVECTION, UV_QDRAG, UV_VIS2, VAR_RHO_2D\u001b[39m\n",
       "  NCO                  = \u001b[36m4.7.2\u001b[39m\n"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fname = \"ocean_avg_part.nc\"\n",
    "if !isfile(fname)\n",
    "    download(\"https://dox.ulg.ac.be/index.php/s/rGvTilIGbbRRs0u/download\",fname)\n",
    "end\n",
    "\n",
    "ds = Dataset(fname)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the data from the NetCDF file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "closed NetCDF NCDataset"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n = 1:4\n",
    "lon = nomissing(ds[\"lon_rho\"][:],NaN)\n",
    "lat = nomissing(ds[\"lat_rho\"][:],NaN)\n",
    "temp = nomissing(ds[\"temp\"][:,:,end,n],NaN)\n",
    "salt = nomissing(ds[\"salt\"][:,:,end,n],NaN)\n",
    "zeta = nomissing(ds[\"zeta\"][:,:,n],NaN)\n",
    "\n",
    "ocean_time = nomissing(ds[\"ocean_time\"][n])\n",
    "mask = nomissing(ds[\"mask_rho\"][:,:]) .== 1;\n",
    "close(ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4-element Vector{Int64}:\n",
       " 32\n",
       " 33\n",
       " 34\n",
       " 35"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "doy = dayofyear.(ocean_time)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Replicate the data so that all arrays has the same number of dimensions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(size(time3), size(lon3)) = ((276, 150, 4), (276, 150, 4))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "((276, 150, 4), (276, 150, 4))"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Ntimes = length(ocean_time);\n",
    "lon3 = repeat(lon,inner=(1,1,Ntimes))\n",
    "lat3 = repeat(lat,inner=(1,1,Ntimes));\n",
    "time3 = repeat(reshape(1:Ntimes,(1,1,Ntimes)),inner=(size(lon,1),size(lon,2),1))\n",
    "doy3 = repeat(reshape(doy,(1,1,Ntimes)),inner=(size(lon,1),size(lon,2),1))\n",
    "mask3 = repeat(mask,inner=(1,1,Ntimes));\n",
    "\n",
    "@show size(time3),size(lon3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Helper function for plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "plotvar (generic function with 1 method)"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmap = \"jet\"\n",
    "function plotvar(var; cl = extrema(var[isfinite.(var)]))\n",
    "    pcolor(lon,lat,var[:,:,end]; cmap=cmap); \n",
    "    clim(cl)\n",
    "    colorbar(orientation=\"horizontal\");\n",
    "    contour(lon,lat,Int.(mask),[0.5],colors=\"k\")\n",
    "    gca()[:set_aspect](1/cos(pi * mean(lat)/180))\n",
    "end\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Figure(PyObject <Figure size 640x480 with 2 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotvar(temp[:,:,end],cl = [11,15]); title(\"Temperature\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Figure(PyObject <Figure size 640x480 with 2 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotvar(zeta[:,:,end]); title(\"Sea surface elevation\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "Figure(PyObject <Figure size 640x480 with 2 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotvar(salt[:,:,end]); title(\"Salinity\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Location of pseudo-observations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Figure(PyObject <Figure size 640x480 with 2 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "PyObject <matplotlib.contour.QuadContourSet object at 0x7fd621060640>"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sel = (rand(Float64,size(temp)) .< 0.01) .& mask;\n",
    "sel_last = sel .& (time3 .== maximum(time3))\n",
    "plotvar(salt[:,:,end])\n",
    "plot(lon3[sel_last],lat3[sel_last],\"k.\")\n",
    "contour(lon,lat,Int.(mask),[0.5],colors=\"k\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Figure(PyObject <Figure size 640x480 with 2 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "PyPlot.scatter(lon3[sel_last],lat3[sel_last],10,temp[sel_last]; cmap=cmap);\n",
    "colorbar(orientation=\"horizontal\");\n",
    "contour(lon,lat,mask,[0.5],colors=\"k\");\n",
    "gca()[:set_aspect](1/cos(pi * mean(lat)/180))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "size(X) = (6, 842)\n",
      "size(Y) = (1, 842)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1×842 Matrix{Float32}:\n",
       " 0.0181198  -0.166592  -0.170803  …  -0.134399  -0.150875  -0.140079"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = vcat(lon3[sel]',lat3[sel]',cos.(2*pi*doy3[sel]/365)',sin.(2*pi*doy3[sel]/365)',temp[sel]',zeta[sel]')\n",
    "Y = salt[sel]'\n",
    "\n",
    "@show size(X)\n",
    "@show size(Y)\n",
    "\n",
    "meanX = mean(X,dims=2)\n",
    "meanY = mean(Y,dims=2)\n",
    "\n",
    "X = X .- meanX\n",
    "Y = Y .- meanY\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "#51 (generic function with 1 method)"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = Chain(Dense(size(X,1),4,σ),\n",
    "    Dense(4,4,σ),\n",
    "    Dense(4,4,σ),\n",
    "    Dense(4,3,σ),\n",
    "    Dense(3,1),\n",
    ")\n",
    "loss(x, y) = Flux.mse(m(x), y)\n",
    "evalcb = () -> @show sqrt(loss(X, Y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "#m = gpu(m)\n",
    "#X = gpu(Float32.(X))\n",
    "#Y = gpu(Float32.(Y))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6, 842)"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "size(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sqrt(loss(X, Y)) = 0.3483462720764487\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.3483462720764487"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = batchview((X,Y),size=300);\n",
    "Xbatch,Ybatch = first(dataset)\n",
    "\n",
    "#@show m(Xbatch)\n",
    "#@show loss(Xbatch,Ybatch)\n",
    "\n",
    "evalcb()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: Epoch 1\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sqrt(loss(X, Y)) = 0.3451062333740944\n",
      "sqrt(loss(X, Y)) = 0.33864717850584103\n",
      "sqrt(loss(X, Y)) = 0.3322212764188367\n",
      "sqrt(loss(X, Y)) = 0.3258298443648763\n",
      "sqrt(loss(X, Y)) = 0.3194743053355915\n",
      "sqrt(loss(X, Y)) = 0.3131561708560307\n",
      "sqrt(loss(X, Y)) = 0.3068770463256508\n",
      "sqrt(loss(X, Y)) = 0.30063839653753477\n",
      "sqrt(loss(X, Y)) = 0.29444177930421395\n",
      "sqrt(loss(X, Y)) = 0.2882889266036504\n",
      "sqrt(loss(X, Y)) = 0.282181309116694\n",
      "sqrt(loss(X, Y)) = 0.27612092631016383\n",
      "sqrt(loss(X, Y)) = 0.27011028795717407\n",
      "sqrt(loss(X, Y)) = 0.2641518996842673\n",
      "sqrt(loss(X, Y)) = 0.25824808284096334\n",
      "sqrt(loss(X, Y)) = 0.25240186743104315\n",
      "sqrt(loss(X, Y)) = 0.24661661945627156\n",
      "sqrt(loss(X, Y)) = 0.24089600498735128\n",
      "sqrt(loss(X, Y)) = 0.23524397744891232\n",
      "sqrt(loss(X, Y)) = 0.22966478593351225\n",
      "sqrt(loss(X, Y)) = 0.2241630350623298\n",
      "sqrt(loss(X, Y)) = 0.2187437197158513\n",
      "sqrt(loss(X, Y)) = 0.21341219450050844\n",
      "sqrt(loss(X, Y)) = 0.2081742764978063\n",
      "sqrt(loss(X, Y)) = 0.203036219155666\n",
      "sqrt(loss(X, Y)) = 0.19800478227925866\n",
      "sqrt(loss(X, Y)) = 0.19308724900831964\n",
      "sqrt(loss(X, Y)) = 0.18829143333049053\n",
      "sqrt(loss(X, Y)) = 0.18362573178417932\n",
      "sqrt(loss(X, Y)) = 0.17909913595107999\n",
      "sqrt(loss(X, Y)) = 0.17472121199937032\n",
      "sqrt(loss(X, Y)) = 0.17050214552435905\n",
      "sqrt(loss(X, Y)) = 0.16645269695730708\n",
      "sqrt(loss(X, Y)) = 0.16258416348854782\n",
      "sqrt(loss(X, Y)) = 0.15890835680662277\n",
      "sqrt(loss(X, Y)) = 0.15543748871866445\n",
      "sqrt(loss(X, Y)) = 0.15218408609401615\n",
      "sqrt(loss(X, Y)) = 0.14916084420767245\n",
      "sqrt(loss(X, Y)) = 0.14638042801068113\n",
      "sqrt(loss(X, Y)) = 0.14385529218718485\n",
      "sqrt(loss(X, Y)) = 0.1415974194105815\n",
      "sqrt(loss(X, Y)) = 0.1396180582607846\n",
      "sqrt(loss(X, Y)) = 0.13792739847157323\n",
      "sqrt(loss(X, Y)) = 0.13653422740611454\n",
      "sqrt(loss(X, Y)) = 0.13544553568492163\n",
      "sqrt(loss(X, Y)) = 0.13466504027309492\n",
      "sqrt(loss(X, Y)) = 0.13419372226544202\n",
      "sqrt(loss(X, Y)) = 0.13422592590189975\n",
      "sqrt(loss(X, Y)) = 0.13431173665810076\n",
      "sqrt(loss(X, Y)) = 0.13439147413334177\n",
      "sqrt(loss(X, Y)) = 0.13446358086653767\n",
      "sqrt(loss(X, Y)) = 0.13440875474655173\n",
      "sqrt(loss(X, Y)) = 0.13446477002090557\n",
      "sqrt(loss(X, Y)) = 0.13440627762053498\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: Epoch 2\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 3\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 4\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 5\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 6\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 7\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 8\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 9\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 10\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 11\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 12\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 13\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 14\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 15\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 16\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 17\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 18\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 19\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 20\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 21\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 22\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 23\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 24\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 25\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 26\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 27\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 28\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 29\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 30\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 31\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 32\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 33\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 34\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 35\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 36\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 37\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 38\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 39\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 40\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 41\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 42\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 43\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 44\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 45\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 46\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 47\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 48\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 49\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 50\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 51\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 52\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 53\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 54\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sqrt(loss(X, Y)) = 0.13446137545831355\n",
      "sqrt(loss(X, Y)) = 0.13409783445749276\n",
      "sqrt(loss(X, Y)) = 0.13413396553931803\n",
      "sqrt(loss(X, Y)) = 0.13421985953096902\n",
      "sqrt(loss(X, Y)) = 0.13429983972894027\n",
      "sqrt(loss(X, Y)) = 0.13437273090708304\n",
      "sqrt(loss(X, Y)) = 0.1344248796997546\n",
      "sqrt(loss(X, Y)) = 0.13405643944885598\n",
      "sqrt(loss(X, Y)) = 0.1340915031452136\n",
      "sqrt(loss(X, Y)) = 0.1341759337004974\n",
      "sqrt(loss(X, Y)) = 0.1342546620404715\n",
      "sqrt(loss(X, Y)) = 0.1343267320161845\n",
      "sqrt(loss(X, Y)) = 0.13437805095969896\n",
      "sqrt(loss(X, Y)) = 0.1340087269468634\n",
      "sqrt(loss(X, Y)) = 0.1340432878986092\n",
      "sqrt(loss(X, Y)) = 0.13412697832495807\n",
      "sqrt(loss(X, Y)) = 0.1342049600108606\n",
      "sqrt(loss(X, Y)) = 0.13427629903534044\n",
      "sqrt(loss(X, Y)) = 0.1343268269154131\n",
      "sqrt(loss(X, Y)) = 0.1339574689593661\n",
      "sqrt(loss(X, Y)) = 0.1339911689214245\n",
      "sqrt(loss(X, Y)) = 0.1340740510770439\n",
      "sqrt(loss(X, Y)) = 0.13415073327404423\n",
      "sqrt(loss(X, Y)) = 0.13422127733297318\n",
      "sqrt(loss(X, Y)) = 0.1341682576812144\n",
      "sqrt(loss(X, Y)) = 0.13421344478278174\n",
      "sqrt(loss(X, Y)) = 0.13415602066579696\n",
      "sqrt(loss(X, Y)) = 0.13419516623847375\n",
      "sqrt(loss(X, Y)) = 0.13413599582536231\n",
      "sqrt(loss(X, Y)) = 0.13417454778871857\n",
      "sqrt(loss(X, Y)) = 0.1341143642340135\n",
      "sqrt(loss(X, Y)) = 0.13415238375927968\n",
      "sqrt(loss(X, Y)) = 0.13409128233862289\n",
      "sqrt(loss(X, Y)) = 0.13412881373318938\n",
      "sqrt(loss(X, Y)) = 0.13406653764842594\n",
      "sqrt(loss(X, Y)) = 0.13410360052300563\n",
      "sqrt(loss(X, Y)) = 0.13402878922052755\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: Epoch 55\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 56\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 57\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 58\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 59\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 60\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 61\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 62\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 63\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 64\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 65\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 66\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 67\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 68\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 69\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 70\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 71\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 72\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 73\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 74\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 75\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 76\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 77\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 78\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 79\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 80\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 81\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 82\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 83\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 84\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 85\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 86\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 87\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 88\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 89\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 90\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 91\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sqrt(loss(X, Y)) = 0.13406481954480232\n",
      "sqrt(loss(X, Y)) = 0.13400328171526235\n",
      "sqrt(loss(X, Y)) = 0.13404014936767847\n",
      "sqrt(loss(X, Y)) = 0.1339776957716335\n",
      "sqrt(loss(X, Y)) = 0.1340134512661755\n",
      "sqrt(loss(X, Y)) = 0.13393815612014087\n",
      "sqrt(loss(X, Y)) = 0.13397343212044882\n",
      "sqrt(loss(X, Y)) = 0.13391130589750233\n",
      "sqrt(loss(X, Y)) = 0.13394646167152205\n",
      "sqrt(loss(X, Y)) = 0.1338712732363003\n",
      "sqrt(loss(X, Y)) = 0.13390533817887243\n",
      "sqrt(loss(X, Y)) = 0.13384321683586506\n",
      "sqrt(loss(X, Y)) = 0.13387788199383552\n",
      "sqrt(loss(X, Y)) = 0.13380258371516565\n",
      "sqrt(loss(X, Y)) = 0.13383612571124576\n",
      "sqrt(loss(X, Y)) = 0.1337739332004427\n",
      "sqrt(loss(X, Y)) = 0.13380810341508106\n",
      "sqrt(loss(X, Y)) = 0.13373246619065934\n",
      "sqrt(loss(X, Y)) = 0.1337654194466778\n",
      "sqrt(loss(X, Y)) = 0.1337029409337278\n",
      "sqrt(loss(X, Y)) = 0.13373655921007993\n",
      "sqrt(loss(X, Y)) = 0.13365765355303905\n",
      "sqrt(loss(X, Y)) = 0.13368986498233318\n",
      "sqrt(loss(X, Y)) = 0.13362731708631917\n",
      "sqrt(loss(X, Y)) = 0.13366052929509853\n",
      "sqrt(loss(X, Y)) = 0.13358124289739867\n",
      "sqrt(loss(X, Y)) = 0.13361300762927725\n",
      "sqrt(loss(X, Y)) = 0.13353775488465466\n",
      "sqrt(loss(X, Y)) = 0.13356985292943666\n",
      "sqrt(loss(X, Y)) = 0.13350732830178882\n",
      "sqrt(loss(X, Y)) = 0.13354002410078222\n",
      "sqrt(loss(X, Y)) = 0.1333118587625256\n",
      "sqrt(loss(X, Y)) = 0.13334889567758207\n",
      "sqrt(loss(X, Y)) = 0.13339959353958722\n",
      "sqrt(loss(X, Y)) = 0.13344393706250884\n",
      "sqrt(loss(X, Y)) = 0.1333818005879132\n",
      "sqrt(loss(X, Y)) = 0.13341378553153743\n",
      "sqrt(loss(X, Y)) = 0.13333327538661024\n",
      "sqrt(loss(X, Y)) = 0.1333633181195539\n",
      "sqrt(loss(X, Y)) = 0.13328331332378066\n",
      "sqrt(loss(X, Y)) = 0.13331302377530024\n",
      "sqrt(loss(X, Y)) = 0.1332329610696273\n",
      "sqrt(loss(X, Y)) = 0.13326234830661055\n",
      "sqrt(loss(X, Y)) = 0.1331975084242254\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: Epoch 92\n",
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      "┌ Info: Epoch 94\n",
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      "┌ Info: Epoch 95\n",
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      "┌ Info: Epoch 96\n",
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      "┌ Info: Epoch 97\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 98\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 99\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 100\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 101\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 102\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 103\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 104\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 105\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 106\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 107\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 108\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 109\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 110\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 111\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 112\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 113\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 114\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 115\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 116\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 117\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 118\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 119\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 120\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 121\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 122\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 123\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 124\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 125\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 126\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 127\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 128\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 129\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 130\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 131\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 132\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 133\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 134\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 135\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 136\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sqrt(loss(X, Y)) = 0.13312847466900793\n",
      "sqrt(loss(X, Y)) = 0.13315997079609723\n",
      "sqrt(loss(X, Y)) = 0.1330977373922246\n",
      "sqrt(loss(X, Y)) = 0.133127485522221\n",
      "sqrt(loss(X, Y)) = 0.1330617477849838\n",
      "sqrt(loss(X, Y)) = 0.13299157394080005\n",
      "sqrt(loss(X, Y)) = 0.13302201493103855\n",
      "sqrt(loss(X, Y)) = 0.13295850466137635\n",
      "sqrt(loss(X, Y)) = 0.13298708573926532\n",
      "sqrt(loss(X, Y)) = 0.13291979710192517\n",
      "sqrt(loss(X, Y)) = 0.13284808944827\n",
      "sqrt(loss(X, Y)) = 0.1328768489770642\n",
      "sqrt(loss(X, Y)) = 0.13281149815456475\n",
      "sqrt(loss(X, Y)) = 0.13283830424921356\n",
      "sqrt(loss(X, Y)) = 0.1327604794335215\n",
      "sqrt(loss(X, Y)) = 0.13268688007904345\n",
      "sqrt(loss(X, Y)) = 0.13271412553773138\n",
      "sqrt(loss(X, Y)) = 0.13264748375067728\n",
      "sqrt(loss(X, Y)) = 0.13267263987333133\n",
      "sqrt(loss(X, Y)) = 0.13259297714523174\n",
      "sqrt(loss(X, Y)) = 0.13251764055423715\n",
      "sqrt(loss(X, Y)) = 0.13254306393694026\n",
      "sqrt(loss(X, Y)) = 0.13247448778900867\n",
      "sqrt(loss(X, Y)) = 0.1324977245070091\n",
      "sqrt(loss(X, Y)) = 0.13241565381498002\n",
      "sqrt(loss(X, Y)) = 0.13233808761132795\n",
      "sqrt(loss(X, Y)) = 0.13236146648954905\n",
      "sqrt(loss(X, Y)) = 0.13228868213310963\n",
      "sqrt(loss(X, Y)) = 0.1323096799252842\n",
      "sqrt(loss(X, Y)) = 0.13222508251327034\n",
      "sqrt(loss(X, Y)) = 0.1321451830856615\n",
      "sqrt(loss(X, Y)) = 0.13216641269224838\n",
      "sqrt(loss(X, Y)) = 0.13208363648886898\n",
      "sqrt(loss(X, Y)) = 0.13210191591526937\n",
      "sqrt(loss(X, Y)) = 0.13201548178239414\n",
      "sqrt(loss(X, Y)) = 0.13193373138270242\n",
      "sqrt(loss(X, Y)) = 0.13195282946601075\n",
      "sqrt(loss(X, Y)) = 0.13186761952388157\n",
      "sqrt(loss(X, Y)) = 0.1318836071257036\n",
      "sqrt(loss(X, Y)) = 0.13179446728470884\n",
      "sqrt(loss(X, Y)) = 0.13171014780611065\n",
      "sqrt(loss(X, Y)) = 0.13172679993599176\n",
      "sqrt(loss(X, Y)) = 0.13163851808438728\n",
      "sqrt(loss(X, Y)) = 0.1315546170309902\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: Epoch 137\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 138\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 139\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 140\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 141\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 142\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 143\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 144\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 145\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 146\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 147\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 148\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 149\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 150\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 151\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 152\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 153\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 154\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 155\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 156\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 157\n",
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      "┌ Info: Epoch 158\n",
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      "┌ Info: Epoch 159\n",
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      "┌ Info: Epoch 160\n",
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      "┌ Info: Epoch 161\n",
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      "┌ Info: Epoch 162\n",
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      "┌ Info: Epoch 163\n",
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      "┌ Info: Epoch 164\n",
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      "┌ Info: Epoch 165\n",
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      "┌ Info: Epoch 166\n",
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      "┌ Info: Epoch 167\n",
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      "┌ Info: Epoch 168\n",
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      "┌ Info: Epoch 169\n",
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      "┌ Info: Epoch 170\n",
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      "┌ Info: Epoch 171\n",
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      "┌ Info: Epoch 172\n",
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      "┌ Info: Epoch 173\n",
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      "┌ Info: Epoch 174\n",
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      "┌ Info: Epoch 175\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 176\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 177\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 178\n",
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      "┌ Info: Epoch 179\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 180\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sqrt(loss(X, Y)) = 0.13157035048376556\n",
      "sqrt(loss(X, Y)) = 0.13148180745746463\n",
      "sqrt(loss(X, Y)) = 0.1314940980223518\n",
      "sqrt(loss(X, Y)) = 0.13139462390922724\n",
      "sqrt(loss(X, Y)) = 0.13130654694129615\n",
      "sqrt(loss(X, Y)) = 0.1313194119379568\n",
      "sqrt(loss(X, Y)) = 0.13122700753254068\n",
      "sqrt(loss(X, Y)) = 0.13113900319063038\n",
      "sqrt(loss(X, Y)) = 0.13115050892099644\n",
      "sqrt(loss(X, Y)) = 0.13105689197165388\n",
      "sqrt(loss(X, Y)) = 0.130967584931588\n",
      "sqrt(loss(X, Y)) = 0.13097749999221647\n",
      "sqrt(loss(X, Y)) = 0.13088213501149937\n",
      "sqrt(loss(X, Y)) = 0.1307910584459776\n",
      "sqrt(loss(X, Y)) = 0.1307992067573411\n",
      "sqrt(loss(X, Y)) = 0.1307017072974762\n",
      "sqrt(loss(X, Y)) = 0.13060627191845708\n",
      "sqrt(loss(X, Y)) = 0.13061235057492968\n",
      "sqrt(loss(X, Y)) = 0.13051272837404052\n",
      "sqrt(loss(X, Y)) = 0.13041517659708868\n",
      "sqrt(loss(X, Y)) = 0.13041929979964803\n",
      "sqrt(loss(X, Y)) = 0.13031015917378475\n",
      "sqrt(loss(X, Y)) = 0.1302104371838259\n",
      "sqrt(loss(X, Y)) = 0.13021281414596822\n",
      "sqrt(loss(X, Y)) = 0.13010171871388584\n",
      "sqrt(loss(X, Y)) = 0.1300000536149564\n",
      "sqrt(loss(X, Y)) = 0.13000252259530792\n",
      "sqrt(loss(X, Y)) = 0.12988884631767722\n",
      "sqrt(loss(X, Y)) = 0.12977602192263296\n",
      "sqrt(loss(X, Y)) = 0.12977557946443644\n",
      "sqrt(loss(X, Y)) = 0.1296601164169744\n",
      "sqrt(loss(X, Y)) = 0.12954647540624833\n",
      "sqrt(loss(X, Y)) = 0.12954397198730377\n",
      "sqrt(loss(X, Y)) = 0.12942588743212585\n",
      "sqrt(loss(X, Y)) = 0.129310357352517\n",
      "sqrt(loss(X, Y)) = 0.12930555092200752\n",
      "sqrt(loss(X, Y)) = 0.12918432505755545\n",
      "sqrt(loss(X, Y)) = 0.12906571071894815\n",
      "sqrt(loss(X, Y)) = 0.12905547569071954\n",
      "sqrt(loss(X, Y)) = 0.12893098582131618\n",
      "sqrt(loss(X, Y)) = 0.12880942783820779\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: Epoch 181\n",
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      "┌ Info: Epoch 182\n",
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      "┌ Info: Epoch 183\n",
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      "┌ Info: Epoch 184\n",
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      "┌ Info: Epoch 185\n",
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      "┌ Info: Epoch 186\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 187\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 188\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 189\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 190\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 191\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 192\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 193\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 194\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 195\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 196\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 197\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 198\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 199\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 200\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 201\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 202\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 203\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 204\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 205\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 206\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 207\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 208\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 209\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 210\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 211\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 212\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 213\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 214\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 215\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 216\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 217\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 218\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 219\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 220\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 221\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sqrt(loss(X, Y)) = 0.12879657159997682\n",
      "sqrt(loss(X, Y)) = 0.12866980179647752\n",
      "sqrt(loss(X, Y)) = 0.1285449624920435\n",
      "sqrt(loss(X, Y)) = 0.1285293134664993\n",
      "sqrt(loss(X, Y)) = 0.12839883187691065\n",
      "sqrt(loss(X, Y)) = 0.12827041983622173\n",
      "sqrt(loss(X, Y)) = 0.12815576852305766\n",
      "sqrt(loss(X, Y)) = 0.1281381351212398\n",
      "sqrt(loss(X, Y)) = 0.12800468397240042\n",
      "sqrt(loss(X, Y)) = 0.12787294697770504\n",
      "sqrt(loss(X, Y)) = 0.1277547556100519\n",
      "sqrt(loss(X, Y)) = 0.127733342293944\n",
      "sqrt(loss(X, Y)) = 0.12759560994608304\n",
      "sqrt(loss(X, Y)) = 0.1274595357712804\n",
      "sqrt(loss(X, Y)) = 0.12732755800093437\n",
      "sqrt(loss(X, Y)) = 0.12730129595518969\n",
      "sqrt(loss(X, Y)) = 0.12716080544622213\n",
      "sqrt(loss(X, Y)) = 0.12702068696269508\n",
      "sqrt(loss(X, Y)) = 0.12688424067061785\n",
      "sqrt(loss(X, Y)) = 0.12685381461398723\n",
      "sqrt(loss(X, Y)) = 0.1267082280948123\n",
      "sqrt(loss(X, Y)) = 0.12656303078384024\n",
      "sqrt(loss(X, Y)) = 0.12642134773328242\n",
      "sqrt(loss(X, Y)) = 0.12629098370207406\n",
      "sqrt(loss(X, Y)) = 0.12625644613390205\n",
      "sqrt(loss(X, Y)) = 0.1261045464668838\n",
      "sqrt(loss(X, Y)) = 0.12595298151777368\n",
      "sqrt(loss(X, Y)) = 0.12580468886116125\n",
      "sqrt(loss(X, Y)) = 0.12566764815482995\n",
      "sqrt(loss(X, Y)) = 0.12553125948961333\n",
      "sqrt(loss(X, Y)) = 0.12539405842328627\n",
      "sqrt(loss(X, Y)) = 0.12525583590692432\n",
      "sqrt(loss(X, Y)) = 0.12511645724270126\n",
      "sqrt(loss(X, Y)) = 0.12497581058709095\n",
      "sqrt(loss(X, Y)) = 0.12483380300323013\n",
      "sqrt(loss(X, Y)) = 0.12469035450162165\n",
      "sqrt(loss(X, Y)) = 0.12454539862680437\n",
      "sqrt(loss(X, Y)) = 0.12439888014833754\n",
      "sqrt(loss(X, Y)) = 0.12425075094203002\n",
      "sqrt(loss(X, Y)) = 0.12410096857424704\n",
      "sqrt(loss(X, Y)) = 0.12394949851737734\n",
      "sqrt(loss(X, Y)) = 0.12379630842772825\n",
      "sqrt(loss(X, Y)) = 0.12364136981621944\n",
      "sqrt(loss(X, Y)) = 0.12348465676198629\n",
      "sqrt(loss(X, Y)) = 0.12332610604955262\n",
      "sqrt(loss(X, Y)) = 0.12316573383745444\n",
      "sqrt(loss(X, Y)) = 0.12300352412821045\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: Epoch 222\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 223\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 224\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 225\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 226\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 227\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 228\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 229\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 230\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 231\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 232\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 233\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 234\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 235\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 236\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 237\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 238\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 239\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 240\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 241\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 242\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 243\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 244\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 245\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 246\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 247\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 248\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 249\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 250\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 251\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 252\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 253\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 254\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 255\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 256\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 257\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 258\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 259\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 260\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 261\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 262\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 263\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 264\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 265\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 266\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 267\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 268\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sqrt(loss(X, Y)) = 0.1228393428005725\n",
      "sqrt(loss(X, Y)) = 0.12267326583955897\n",
      "sqrt(loss(X, Y)) = 0.12250524422619237\n",
      "sqrt(loss(X, Y)) = 0.12232517203129706\n",
      "sqrt(loss(X, Y)) = 0.12215521038849099\n",
      "sqrt(loss(X, Y)) = 0.12198436745165675\n",
      "sqrt(loss(X, Y)) = 0.12181076071606207\n",
      "sqrt(loss(X, Y)) = 0.12162477928771473\n",
      "sqrt(loss(X, Y)) = 0.1214471741881058\n",
      "sqrt(loss(X, Y)) = 0.12125811682315392\n",
      "sqrt(loss(X, Y)) = 0.12107702475744163\n",
      "sqrt(loss(X, Y)) = 0.12089449015154559\n",
      "sqrt(loss(X, Y)) = 0.12069891103570173\n",
      "sqrt(loss(X, Y)) = 0.12051149568895123\n",
      "sqrt(loss(X, Y)) = 0.12031227474804346\n",
      "sqrt(loss(X, Y)) = 0.12012039801078409\n",
      "sqrt(loss(X, Y)) = 0.11991658965838475\n",
      "sqrt(loss(X, Y)) = 0.11972132002118546\n",
      "sqrt(loss(X, Y)) = 0.1195134001261065\n",
      "sqrt(loss(X, Y)) = 0.11931333983840295\n",
      "sqrt(loss(X, Y)) = 0.11910118271243436\n",
      "sqrt(loss(X, Y)) = 0.11889689277463594\n",
      "sqrt(loss(X, Y)) = 0.11868036031838876\n",
      "sqrt(loss(X, Y)) = 0.11847179571237422\n",
      "sqrt(loss(X, Y)) = 0.11824883774806592\n",
      "sqrt(loss(X, Y)) = 0.11803643814554861\n",
      "sqrt(loss(X, Y)) = 0.11780652942695782\n",
      "sqrt(loss(X, Y)) = 0.1175861823776461\n",
      "sqrt(loss(X, Y)) = 0.11736454268466862\n",
      "sqrt(loss(X, Y)) = 0.11712925193296422\n",
      "sqrt(loss(X, Y)) = 0.1169020279414695\n",
      "sqrt(loss(X, Y)) = 0.11666363033770831\n",
      "sqrt(loss(X, Y)) = 0.1164327584216296\n",
      "sqrt(loss(X, Y)) = 0.11619934373249988\n",
      "sqrt(loss(X, Y)) = 0.11595014815775916\n",
      "sqrt(loss(X, Y)) = 0.11571105110211093\n",
      "sqrt(loss(X, Y)) = 0.1154705005154409\n",
      "sqrt(loss(X, Y)) = 0.11521259355869021\n",
      "sqrt(loss(X, Y)) = 0.11496626263274541\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: Epoch 269\n",
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      "┌ Info: Epoch 294\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 295\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 296\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 297\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 298\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 299\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 300\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 301\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 302\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 303\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 304\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 305\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 306\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 307\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sqrt(loss(X, Y)) = 0.11471712910183748\n",
      "sqrt(loss(X, Y)) = 0.11445195503005724\n",
      "sqrt(loss(X, Y)) = 0.11419850831859699\n",
      "sqrt(loss(X, Y)) = 0.11394211846893741\n",
      "sqrt(loss(X, Y)) = 0.11366876812941223\n",
      "sqrt(loss(X, Y)) = 0.11340704788222597\n",
      "sqrt(loss(X, Y)) = 0.1131423652858\n",
      "sqrt(loss(X, Y)) = 0.11287202406652247\n",
      "sqrt(loss(X, Y)) = 0.1125882033780836\n",
      "sqrt(loss(X, Y)) = 0.11231485860618186\n",
      "sqrt(loss(X, Y)) = 0.11203786862352227\n",
      "sqrt(loss(X, Y)) = 0.11175749325708327\n",
      "sqrt(loss(X, Y)) = 0.11147433684981385\n",
      "sqrt(loss(X, Y)) = 0.11117771947034769\n",
      "sqrt(loss(X, Y)) = 0.11089248244653487\n",
      "sqrt(loss(X, Y)) = 0.11060229225491991\n",
      "sqrt(loss(X, Y)) = 0.11030873104484976\n",
      "sqrt(loss(X, Y)) = 0.11001225215821718\n",
      "sqrt(loss(X, Y)) = 0.1097022519512609\n",
      "sqrt(loss(X, Y)) = 0.10940287692779435\n",
      "sqrt(loss(X, Y)) = 0.10909939555197515\n",
      "sqrt(loss(X, Y)) = 0.10879259258098223\n",
      "sqrt(loss(X, Y)) = 0.10848283066677956\n",
      "sqrt(loss(X, Y)) = 0.1081595019529196\n",
      "sqrt(loss(X, Y)) = 0.10784795714320089\n",
      "sqrt(loss(X, Y)) = 0.10753112281300663\n",
      "sqrt(loss(X, Y)) = 0.10721093031953939\n",
      "sqrt(loss(X, Y)) = 0.10688769578928184\n",
      "sqrt(loss(X, Y)) = 0.1065509281313993\n",
      "sqrt(loss(X, Y)) = 0.10622489474082213\n",
      "sqrt(loss(X, Y)) = 0.10589472581100648\n",
      "sqrt(loss(X, Y)) = 0.10556120706330183\n",
      "sqrt(loss(X, Y)) = 0.10522473165604458\n",
      "sqrt(loss(X, Y)) = 0.10487462590459902\n",
      "sqrt(loss(X, Y)) = 0.10453522865650505\n",
      "sqrt(loss(X, Y)) = 0.10419180886541565\n",
      "sqrt(loss(X, Y)) = 0.10384506460450525\n",
      "sqrt(loss(X, Y)) = 0.10349543431127499\n",
      "sqrt(loss(X, Y)) = 0.10312924296378188\n",
      "sqrt(loss(X, Y)) = 0.10277681306728371\n",
      "sqrt(loss(X, Y)) = 0.10241754988876478\n",
      "sqrt(loss(X, Y)) = 0.1020548165654045\n",
      "sqrt(loss(X, Y)) = 0.10168957659822296\n",
      "sqrt(loss(X, Y)) = 0.10132182157048111\n",
      "sqrt(loss(X, Y)) = 0.10095154003452103\n",
      "sqrt(loss(X, Y)) = 0.10057873106843998\n",
      "sqrt(loss(X, Y)) = 0.10020338011813737\n",
      "sqrt(loss(X, Y)) = 0.09981886820802197\n",
      "sqrt(loss(X, Y)) = 0.09944113030972078\n",
      "sqrt(loss(X, Y)) = 0.09905237011783082\n",
      "sqrt(loss(X, Y)) = 0.09866747297843202\n",
      "sqrt(loss(X, Y)) = 0.098274013454772\n",
      "sqrt(loss(X, Y)) = 0.09788742632457519\n",
      "sqrt(loss(X, Y)) = 0.09748934766644946\n",
      "sqrt(loss(X, Y)) = 0.09709491423388385\n",
      "sqrt(loss(X, Y)) = 0.09669199223658644\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: Epoch 308\n",
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      "┌ Info: Epoch 315\n",
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      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 324\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 325\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 326\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 327\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 328\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 329\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 330\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 331\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 332\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 333\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 334\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 335\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 336\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 337\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 338\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 339\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 340\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 341\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 342\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 343\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 344\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 345\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 346\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 347\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 348\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 349\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 350\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 351\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 352\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 353\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 354\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 355\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 356\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 357\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 358\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 359\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 360\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 361\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 362\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 363\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sqrt(loss(X, Y)) = 0.09628706595745964\n",
      "sqrt(loss(X, Y)) = 0.09588956865146232\n",
      "sqrt(loss(X, Y)) = 0.09548028987179508\n",
      "sqrt(loss(X, Y)) = 0.09506787930774538\n",
      "sqrt(loss(X, Y)) = 0.09465395631048631\n",
      "sqrt(loss(X, Y)) = 0.094247940588607\n",
      "sqrt(loss(X, Y)) = 0.09383032503118459\n",
      "sqrt(loss(X, Y)) = 0.0934096248718368\n",
      "sqrt(loss(X, Y)) = 0.09299186125653353\n",
      "sqrt(loss(X, Y)) = 0.09257245296630874\n",
      "sqrt(loss(X, Y)) = 0.09215076569416891\n",
      "sqrt(loss(X, Y)) = 0.0917269459352282\n",
      "sqrt(loss(X, Y)) = 0.09130111089171072\n",
      "sqrt(loss(X, Y)) = 0.09087336165519234\n",
      "sqrt(loss(X, Y)) = 0.09044378442722076\n",
      "sqrt(loss(X, Y)) = 0.0900124960847129\n",
      "sqrt(loss(X, Y)) = 0.08957950124336683\n",
      "sqrt(loss(X, Y)) = 0.0891451960466234\n",
      "sqrt(loss(X, Y)) = 0.08870925862507312\n",
      "sqrt(loss(X, Y)) = 0.08825488354162772\n",
      "sqrt(loss(X, Y)) = 0.08781919247676079\n",
      "sqrt(loss(X, Y)) = 0.08738186298364606\n",
      "sqrt(loss(X, Y)) = 0.08694218359714542\n",
      "sqrt(loss(X, Y)) = 0.08648364835970156\n",
      "sqrt(loss(X, Y)) = 0.08604419285570106\n",
      "sqrt(loss(X, Y)) = 0.08560333584310025\n",
      "sqrt(loss(X, Y)) = 0.08514288474571353\n",
      "sqrt(loss(X, Y)) = 0.08470139550723577\n",
      "sqrt(loss(X, Y)) = 0.08425864961149003\n",
      "sqrt(loss(X, Y)) = 0.08379457531521092\n",
      "sqrt(loss(X, Y)) = 0.08335194495118915\n",
      "sqrt(loss(X, Y)) = 0.08289160131116323\n",
      "sqrt(loss(X, Y)) = 0.08244932180456704\n",
      "sqrt(loss(X, Y)) = 0.08198836078335\n",
      "sqrt(loss(X, Y)) = 0.08155284310380899\n",
      "sqrt(loss(X, Y)) = 0.0810909457365582\n",
      "sqrt(loss(X, Y)) = 0.08064590906807415\n",
      "sqrt(loss(X, Y)) = 0.08020357947331293\n",
      "sqrt(loss(X, Y)) = 0.07974194496044122\n",
      "sqrt(loss(X, Y)) = 0.07931058792397151\n",
      "sqrt(loss(X, Y)) = 0.07885264725039515\n",
      "sqrt(loss(X, Y)) = 0.07841221685690662\n",
      "sqrt(loss(X, Y)) = 0.07795673125367313\n",
      "sqrt(loss(X, Y)) = 0.07753136125263403\n",
      "sqrt(loss(X, Y)) = 0.07707936156174582\n",
      "sqrt(loss(X, Y)) = 0.0766535057801458\n",
      "sqrt(loss(X, Y)) = 0.0762051853962684\n",
      "sqrt(loss(X, Y)) = 0.07578403627891614\n",
      "sqrt(loss(X, Y)) = 0.07534090005511812\n",
      "sqrt(loss(X, Y)) = 0.07492554986048089\n",
      "sqrt(loss(X, Y)) = 0.07448650721569117\n",
      "sqrt(loss(X, Y)) = 0.07407720848467149\n",
      "sqrt(loss(X, Y)) = 0.07364173298085205\n",
      "sqrt(loss(X, Y)) = 0.07323916258283059\n",
      "sqrt(loss(X, Y)) = 0.07281209861558617\n",
      "sqrt(loss(X, Y)) = 0.07241775856080987\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: Epoch 364\n",
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      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 390\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 391\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 392\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 393\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 394\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 395\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 396\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 397\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 398\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 399\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 400\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 401\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 402\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 403\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 404\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 405\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 406\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 407\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 408\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 409\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 410\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 411\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 412\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 413\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 414\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 415\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 416\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 417\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 418\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sqrt(loss(X, Y)) = 0.07199974542341861\n",
      "sqrt(loss(X, Y)) = 0.07161449179958072\n",
      "sqrt(loss(X, Y)) = 0.07120652727464248\n",
      "sqrt(loss(X, Y)) = 0.07083138242392535\n",
      "sqrt(loss(X, Y)) = 0.07045071996952398\n",
      "sqrt(loss(X, Y)) = 0.0700821243963188\n",
      "sqrt(loss(X, Y)) = 0.06969380501568749\n",
      "sqrt(loss(X, Y)) = 0.06934154150730212\n",
      "sqrt(loss(X, Y)) = 0.06899333422369738\n",
      "sqrt(loss(X, Y)) = 0.06864735360157247\n",
      "sqrt(loss(X, Y)) = 0.06830821745978612\n",
      "sqrt(loss(X, Y)) = 0.06797643444857743\n",
      "sqrt(loss(X, Y)) = 0.06765241659939251\n",
      "sqrt(loss(X, Y)) = 0.0673365773295512\n",
      "sqrt(loss(X, Y)) = 0.06703213597253957\n",
      "sqrt(loss(X, Y)) = 0.06673309042352528\n",
      "sqrt(loss(X, Y)) = 0.06644704727907443\n",
      "sqrt(loss(X, Y)) = 0.0661663408198989\n",
      "sqrt(loss(X, Y)) = 0.06590349828265106\n",
      "sqrt(loss(X, Y)) = 0.06564289017664501\n",
      "sqrt(loss(X, Y)) = 0.0654022863628625\n",
      "sqrt(loss(X, Y)) = 0.06516335914429397\n",
      "sqrt(loss(X, Y)) = 0.06493825907011079\n",
      "sqrt(loss(X, Y)) = 0.06472067392155814\n",
      "sqrt(loss(X, Y)) = 0.06451623913192084\n",
      "sqrt(loss(X, Y)) = 0.0643187851286218\n",
      "sqrt(loss(X, Y)) = 0.06413104253043332\n",
      "sqrt(loss(X, Y)) = 0.0639528072396557\n",
      "sqrt(loss(X, Y)) = 0.06378313479504953\n",
      "sqrt(loss(X, Y)) = 0.06362089779268996\n",
      "sqrt(loss(X, Y)) = 0.06359331927384615\n",
      "sqrt(loss(X, Y)) = 0.06334873445298171\n",
      "sqrt(loss(X, Y)) = 0.06321734526941891\n",
      "sqrt(loss(X, Y)) = 0.06312969329972676\n",
      "sqrt(loss(X, Y)) = 0.0629923211178809\n",
      "sqrt(loss(X, Y)) = 0.06295441443086607\n",
      "sqrt(loss(X, Y)) = 0.06281542111304722\n",
      "sqrt(loss(X, Y)) = 0.0627740462207359\n",
      "sqrt(loss(X, Y)) = 0.06264236863886234\n",
      "sqrt(loss(X, Y)) = 0.06260769418272233\n",
      "sqrt(loss(X, Y)) = 0.06248275297126791\n",
      "sqrt(loss(X, Y)) = 0.062452994111133045\n",
      "sqrt(loss(X, Y)) = 0.062334263241371174\n",
      "sqrt(loss(X, Y)) = 0.06230493889926482\n",
      "sqrt(loss(X, Y)) = 0.062192477808750736\n",
      "sqrt(loss(X, Y)) = 0.06216408603955595\n",
      "sqrt(loss(X, Y)) = 0.06205766077237693\n",
      "sqrt(loss(X, Y)) = 0.06202951764606024\n",
      "sqrt(loss(X, Y)) = 0.06192917979784723\n",
      "sqrt(loss(X, Y)) = 0.06189730172258574\n",
      "sqrt(loss(X, Y)) = 0.06180128841483667\n",
      "sqrt(loss(X, Y)) = 0.06177725778927844\n",
      "sqrt(loss(X, Y)) = 0.06168490950555244\n",
      "sqrt(loss(X, Y)) = 0.061658814270915546\n",
      "sqrt(loss(X, Y)) = 0.06159631078897496\n",
      "sqrt(loss(X, Y)) = 0.06157853563903858\n",
      "sqrt(loss(X, Y)) = 0.06152049145985937\n",
      "sqrt(loss(X, Y)) = 0.06150200444699306\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: Epoch 419\n",
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      "┌ Info: Epoch 420\n",
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      "┌ Info: Epoch 421\n",
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      "┌ Info: Epoch 422\n",
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      "┌ Info: Epoch 423\n",
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      "┌ Info: Epoch 424\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 425\n",
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      "┌ Info: Epoch 426\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 427\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 428\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 429\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 430\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 431\n",
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      "┌ Info: Epoch 432\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 433\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 434\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 435\n",
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      "┌ Info: Epoch 436\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 437\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 438\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 439\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 440\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 441\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 442\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 443\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 444\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 445\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 446\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 447\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 448\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 449\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 450\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 451\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 452\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 453\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 454\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 455\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 456\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 457\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 458\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 459\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 460\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 461\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 462\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 463\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 464\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 465\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 466\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 467\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 468\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 469\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 470\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 471\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 472\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 473\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 474\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 475\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 476\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sqrt(loss(X, Y)) = 0.06143952010041313\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: Epoch 477\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sqrt(loss(X, Y)) = 0.061424006618960934\n",
      "sqrt(loss(X, Y)) = 0.06137250057214394\n",
      "sqrt(loss(X, Y)) = 0.0613567727404746\n",
      "sqrt(loss(X, Y)) = 0.06130964581288766\n",
      "sqrt(loss(X, Y)) = 0.061292481077374686\n",
      "sqrt(loss(X, Y)) = 0.06123915868252256\n",
      "sqrt(loss(X, Y)) = 0.06122547631138677\n",
      "sqrt(loss(X, Y)) = 0.06117757314420683\n",
      "sqrt(loss(X, Y)) = 0.06116458505337068\n",
      "sqrt(loss(X, Y)) = 0.06111599323571645\n",
      "sqrt(loss(X, Y)) = 0.06110391083706878\n",
      "sqrt(loss(X, Y)) = 0.061055415357273185\n",
      "sqrt(loss(X, Y)) = 0.06104745946492978\n",
      "sqrt(loss(X, Y)) = 0.06100008430581263\n",
      "sqrt(loss(X, Y)) = 0.06099078395776146\n",
      "sqrt(loss(X, Y)) = 0.060943282848084944\n",
      "sqrt(loss(X, Y)) = 0.060936938393250976\n",
      "sqrt(loss(X, Y)) = 0.060895009922774544\n",
      "sqrt(loss(X, Y)) = 0.0608864110607203\n",
      "sqrt(loss(X, Y)) = 0.06083860784629258\n",
      "sqrt(loss(X, Y)) = 0.06083293698736168\n",
      "sqrt(loss(X, Y)) = 0.0607861046932033\n",
      "sqrt(loss(X, Y)) = 0.06078037651704921\n",
      "sqrt(loss(X, Y)) = 0.06073386339414689\n",
      "sqrt(loss(X, Y)) = 0.060727681800863936\n",
      "sqrt(loss(X, Y)) = 0.06068110818092633\n",
      "sqrt(loss(X, Y)) = 0.06067781473069544\n",
      "sqrt(loss(X, Y)) = 0.06063161535067128\n",
      "sqrt(loss(X, Y)) = 0.06062837422954163\n",
      "sqrt(loss(X, Y)) = 0.06058259138412123\n",
      "sqrt(loss(X, Y)) = 0.06057936795939912\n",
      "sqrt(loss(X, Y)) = 0.06053402779229889\n",
      "sqrt(loss(X, Y)) = 0.06053084493065553\n",
      "sqrt(loss(X, Y)) = 0.06048594961416454\n",
      "sqrt(loss(X, Y)) = 0.06048295708468124\n",
      "sqrt(loss(X, Y)) = 0.0604384292614248\n",
      "sqrt(loss(X, Y)) = 0.060440156171315686\n",
      "sqrt(loss(X, Y)) = 0.06039580902040391\n",
      "sqrt(loss(X, Y)) = 0.06039739475037333\n",
      "sqrt(loss(X, Y)) = 0.0603534178566823\n",
      "sqrt(loss(X, Y)) = 0.060356008812822104\n",
      "sqrt(loss(X, Y)) = 0.060291763019652944\n",
      "sqrt(loss(X, Y)) = 0.06027860445168552\n",
      "sqrt(loss(X, Y)) = 0.06021562411228675\n",
      "sqrt(loss(X, Y)) = 0.06020878248956211\n",
      "sqrt(loss(X, Y)) = 0.060149708031001115\n",
      "sqrt(loss(X, Y)) = 0.06014206089095149\n",
      "sqrt(loss(X, Y)) = 0.060107205931303594\n",
      "sqrt(loss(X, Y)) = 0.060110386515600134\n",
      "sqrt(loss(X, Y)) = 0.06007440207724131\n",
      "sqrt(loss(X, Y)) = 0.06007687836439895\n",
      "sqrt(loss(X, Y)) = 0.0600408377733745\n",
      "sqrt(loss(X, Y)) = 0.060044433180992\n",
      "sqrt(loss(X, Y)) = 0.059991235187876524\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: Epoch 478\n",
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      "┌ Info: Epoch 480\n",
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      "┌ Info: Epoch 481\n",
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      "┌ Info: Epoch 482\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 483\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 484\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 485\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 486\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 487\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 488\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 489\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 490\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 491\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 492\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 493\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 494\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 495\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 496\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 497\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 498\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 499\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 500\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 501\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 502\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 503\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 504\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 505\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 506\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 507\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 508\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 509\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 510\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 511\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 512\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 513\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 514\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 515\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 516\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 517\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 518\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 519\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 520\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 521\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 522\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 523\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 524\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 525\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 526\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 527\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 528\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 529\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 530\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 531\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 532\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sqrt(loss(X, Y)) = 0.05998385415750084\n",
      "sqrt(loss(X, Y)) = 0.05993206728637904\n",
      "sqrt(loss(X, Y)) = 0.05992997514084063\n",
      "sqrt(loss(X, Y)) = 0.05988029937494309\n",
      "sqrt(loss(X, Y)) = 0.05987939379261729\n",
      "sqrt(loss(X, Y)) = 0.05983183722348977\n",
      "sqrt(loss(X, Y)) = 0.05982914177164244\n",
      "sqrt(loss(X, Y)) = 0.059801051420320224\n",
      "sqrt(loss(X, Y)) = 0.059807329858487523\n",
      "sqrt(loss(X, Y)) = 0.059778050733543246\n",
      "sqrt(loss(X, Y)) = 0.059785648084241416\n",
      "sqrt(loss(X, Y)) = 0.059738701709618466\n",
      "sqrt(loss(X, Y)) = 0.05973494501676393\n",
      "sqrt(loss(X, Y)) = 0.05968880011972144\n",
      "sqrt(loss(X, Y)) = 0.05968704893145025\n",
      "sqrt(loss(X, Y)) = 0.059642665841776486\n",
      "sqrt(loss(X, Y)) = 0.05964076124722964\n",
      "sqrt(loss(X, Y)) = 0.05959798357608976\n",
      "sqrt(loss(X, Y)) = 0.05959583359371474\n",
      "sqrt(loss(X, Y)) = 0.05955453106257696\n",
      "sqrt(loss(X, Y)) = 0.05955213869885357\n",
      "sqrt(loss(X, Y)) = 0.059512194446826934\n",
      "sqrt(loss(X, Y)) = 0.05951161820115469\n",
      "sqrt(loss(X, Y)) = 0.05947282449376067\n",
      "sqrt(loss(X, Y)) = 0.05947195693169601\n",
      "sqrt(loss(X, Y)) = 0.059434259546688004\n",
      "sqrt(loss(X, Y)) = 0.0594331354879111\n",
      "sqrt(loss(X, Y)) = 0.05939836544776514\n",
      "sqrt(loss(X, Y)) = 0.05939713755424166\n",
      "sqrt(loss(X, Y)) = 0.05936325619272656\n",
      "sqrt(loss(X, Y)) = 0.05936170688120551\n",
      "sqrt(loss(X, Y)) = 0.05932965366990123\n",
      "sqrt(loss(X, Y)) = 0.059327979814490596\n",
      "sqrt(loss(X, Y)) = 0.05929661732510078\n",
      "sqrt(loss(X, Y)) = 0.059294661630110164\n",
      "sqrt(loss(X, Y)) = 0.059263917050797754\n",
      "sqrt(loss(X, Y)) = 0.05926174336652802\n",
      "sqrt(loss(X, Y)) = 0.0592315764544472\n",
      "sqrt(loss(X, Y)) = 0.059229204150745465\n",
      "sqrt(loss(X, Y)) = 0.05919958244417608\n",
      "sqrt(loss(X, Y)) = 0.059197020555078916\n",
      "sqrt(loss(X, Y)) = 0.05916791417810063\n",
      "sqrt(loss(X, Y)) = 0.05916517781348381\n",
      "sqrt(loss(X, Y)) = 0.05913655986198467\n",
      "sqrt(loss(X, Y)) = 0.05913365500824598\n",
      "sqrt(loss(X, Y)) = 0.05910550017054622\n",
      "sqrt(loss(X, Y)) = 0.05910243748936353\n",
      "sqrt(loss(X, Y)) = 0.059074723775627364\n",
      "sqrt(loss(X, Y)) = 0.05907151226585707\n",
      "sqrt(loss(X, Y)) = 0.059044221141353104\n",
      "sqrt(loss(X, Y)) = 0.059040866730884704\n",
      "sqrt(loss(X, Y)) = 0.0590139804306981\n",
      "sqrt(loss(X, Y)) = 0.059010489965174405\n",
      "sqrt(loss(X, Y)) = 0.058983993483665474\n",
      "sqrt(loss(X, Y)) = 0.05898037068634559\n",
      "sqrt(loss(X, Y)) = 0.058954250392679645\n",
      "sqrt(loss(X, Y)) = 0.058950504759355045\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: Epoch 533\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
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      "┌ Info: Epoch 535\n",
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      "┌ Info: Epoch 537\n",
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      "┌ Info: Epoch 538\n",
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      "┌ Info: Epoch 539\n",
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      "┌ Info: Epoch 552\n",
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      "┌ Info: Epoch 553\n",
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      "┌ Info: Epoch 554\n",
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      "┌ Info: Epoch 556\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 557\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 558\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 559\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 560\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 561\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 562\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 563\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 564\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 565\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 566\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 567\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 568\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 569\n",
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      "┌ Info: Epoch 570\n",
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      "┌ Info: Epoch 571\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 572\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 573\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 574\n",
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      "┌ Info: Epoch 575\n",
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      "┌ Info: Epoch 576\n",
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      "┌ Info: Epoch 577\n",
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      "┌ Info: Epoch 578\n",
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      "┌ Info: Epoch 579\n",
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      "┌ Info: Epoch 580\n",
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      "┌ Info: Epoch 581\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 582\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 583\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 584\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 585\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 586\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 587\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 588\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 589\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sqrt(loss(X, Y)) = 0.058924748702005786\n",
      "sqrt(loss(X, Y)) = 0.058922899977245306\n",
      "sqrt(loss(X, Y)) = 0.058895498502876044\n",
      "sqrt(loss(X, Y)) = 0.05889140247417169\n",
      "sqrt(loss(X, Y)) = 0.05886470696964911\n",
      "sqrt(loss(X, Y)) = 0.05886018329248878\n",
      "sqrt(loss(X, Y)) = 0.058833965302580844\n",
      "sqrt(loss(X, Y)) = 0.05883040051513805\n",
      "sqrt(loss(X, Y)) = 0.05880346073023309\n",
      "sqrt(loss(X, Y)) = 0.05879910234693353\n",
      "sqrt(loss(X, Y)) = 0.05877277728230707\n",
      "sqrt(loss(X, Y)) = 0.058768176936555026\n",
      "sqrt(loss(X, Y)) = 0.058742379015243845\n",
      "sqrt(loss(X, Y)) = 0.05873766535520031\n",
      "sqrt(loss(X, Y)) = 0.05871238260419905\n",
      "sqrt(loss(X, Y)) = 0.058707567653039784\n",
      "sqrt(loss(X, Y)) = 0.05868281701741554\n",
      "sqrt(loss(X, Y)) = 0.05867787938088268\n",
      "sqrt(loss(X, Y)) = 0.05865369128485822\n",
      "sqrt(loss(X, Y)) = 0.05864859336121556\n",
      "sqrt(loss(X, Y)) = 0.05862500648174645\n",
      "sqrt(loss(X, Y)) = 0.058619701898141655\n",
      "sqrt(loss(X, Y)) = 0.05859675965319988\n",
      "sqrt(loss(X, Y)) = 0.058591200844775954\n",
      "sqrt(loss(X, Y)) = 0.05856894705814123\n",
      "sqrt(loss(X, Y)) = 0.058563084385535334\n",
      "sqrt(loss(X, Y)) = 0.05854155950561276\n",
      "sqrt(loss(X, Y)) = 0.058535348433835274\n",
      "sqrt(loss(X, Y)) = 0.058514590687195105\n",
      "sqrt(loss(X, Y)) = 0.058507990432704424\n",
      "sqrt(loss(X, Y)) = 0.058488030505348565\n",
      "sqrt(loss(X, Y)) = 0.058481208577920545\n",
      "sqrt(loss(X, Y)) = 0.05846205964346913\n",
      "sqrt(loss(X, Y)) = 0.05845497401513276\n",
      "sqrt(loss(X, Y)) = 0.05843809612489399\n",
      "sqrt(loss(X, Y)) = 0.05843070885072371\n",
      "sqrt(loss(X, Y)) = 0.05841455945407597\n",
      "sqrt(loss(X, Y)) = 0.05840724566875018\n",
      "sqrt(loss(X, Y)) = 0.05839201962979742\n",
      "sqrt(loss(X, Y)) = 0.05838357371403978\n",
      "sqrt(loss(X, Y)) = 0.05836935033628749\n",
      "sqrt(loss(X, Y)) = 0.05836013196558047\n",
      "sqrt(loss(X, Y)) = 0.058346295762163866\n",
      "sqrt(loss(X, Y)) = 0.05833747808741548\n",
      "sqrt(loss(X, Y)) = 0.05832246158308466\n",
      "sqrt(loss(X, Y)) = 0.058314117982185366\n",
      "sqrt(loss(X, Y)) = 0.05829976074955642\n",
      "sqrt(loss(X, Y)) = 0.05829094028503811\n",
      "sqrt(loss(X, Y)) = 0.058277168059450886\n",
      "sqrt(loss(X, Y)) = 0.058268553858011295\n",
      "sqrt(loss(X, Y)) = 0.05825578298440225\n",
      "sqrt(loss(X, Y)) = 0.05824606397738738\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: Epoch 590\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 591\n",
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      "┌ Info: Epoch 592\n",
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      "┌ Info: Epoch 593\n",
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      "┌ Info: Epoch 594\n",
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      "┌ Info: Epoch 595\n",
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      "┌ Info: Epoch 596\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 597\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 598\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 599\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 600\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 601\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 602\n",
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      "┌ Info: Epoch 603\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 604\n",
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      "┌ Info: Epoch 605\n",
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      "┌ Info: Epoch 606\n",
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      "┌ Info: Epoch 607\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 608\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 609\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 610\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 611\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 612\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 613\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 614\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 615\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 616\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 617\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 618\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 619\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 620\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 621\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 622\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 623\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 624\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 625\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 626\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 627\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 628\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 629\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 630\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 631\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 632\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 633\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 634\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 635\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 636\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 637\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 638\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 639\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 640\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sqrt(loss(X, Y)) = 0.058234084416918255\n",
      "sqrt(loss(X, Y)) = 0.058224559027680776\n",
      "sqrt(loss(X, Y)) = 0.0582132823878926\n",
      "sqrt(loss(X, Y)) = 0.05820309081532838\n",
      "sqrt(loss(X, Y)) = 0.05819222852397791\n",
      "sqrt(loss(X, Y)) = 0.05818205966108597\n",
      "sqrt(loss(X, Y)) = 0.05817139880571442\n",
      "sqrt(loss(X, Y)) = 0.058161386178840056\n",
      "sqrt(loss(X, Y)) = 0.05815116298637364\n",
      "sqrt(loss(X, Y)) = 0.05814060629513853\n",
      "sqrt(loss(X, Y)) = 0.058130723303326165\n",
      "sqrt(loss(X, Y)) = 0.058119217782004774\n",
      "sqrt(loss(X, Y)) = 0.058109674072938475\n",
      "sqrt(loss(X, Y)) = 0.05809665438726106\n",
      "sqrt(loss(X, Y)) = 0.058087285177587045\n",
      "sqrt(loss(X, Y)) = 0.05807415444869369\n",
      "sqrt(loss(X, Y)) = 0.05806499919304367\n",
      "sqrt(loss(X, Y)) = 0.05805175119006148\n",
      "sqrt(loss(X, Y)) = 0.05804316315649072\n",
      "sqrt(loss(X, Y)) = 0.058029595794441696\n",
      "sqrt(loss(X, Y)) = 0.05802136927465001\n",
      "sqrt(loss(X, Y)) = 0.05800769283187214\n",
      "sqrt(loss(X, Y)) = 0.05799962217795077\n",
      "sqrt(loss(X, Y)) = 0.05798659506949445\n",
      "sqrt(loss(X, Y)) = 0.05797881174662178\n",
      "sqrt(loss(X, Y)) = 0.05796556469794789\n",
      "sqrt(loss(X, Y)) = 0.0579580723150213\n",
      "sqrt(loss(X, Y)) = 0.057944602517579484\n",
      "sqrt(loss(X, Y)) = 0.05793740854883534\n",
      "sqrt(loss(X, Y)) = 0.05792370662150874\n",
      "sqrt(loss(X, Y)) = 0.05791682092515467\n",
      "sqrt(loss(X, Y)) = 0.057902935761984\n",
      "sqrt(loss(X, Y)) = 0.057896336770805315\n",
      "sqrt(loss(X, Y)) = 0.05788223085788189\n",
      "sqrt(loss(X, Y)) = 0.057875942123249605\n",
      "sqrt(loss(X, Y)) = 0.05786256633548483\n",
      "sqrt(loss(X, Y)) = 0.057856516400655214\n",
      "sqrt(loss(X, Y)) = 0.057842955647076554\n",
      "sqrt(loss(X, Y)) = 0.05783716006166958\n",
      "sqrt(loss(X, Y)) = 0.057823447367989785\n",
      "sqrt(loss(X, Y)) = 0.057817832790142624\n",
      "sqrt(loss(X, Y)) = 0.057803873650813874\n",
      "sqrt(loss(X, Y)) = 0.05779855461649462\n",
      "sqrt(loss(X, Y)) = 0.05778435666830918\n",
      "sqrt(loss(X, Y)) = 0.05777932752793083\n",
      "sqrt(loss(X, Y)) = 0.05776489490404227\n",
      "sqrt(loss(X, Y)) = 0.05776015318364472\n",
      "sqrt(loss(X, Y)) = 0.05774548861636785\n",
      "sqrt(loss(X, Y)) = 0.05774103137967747\n",
      "sqrt(loss(X, Y)) = 0.057726140349308674\n",
      "sqrt(loss(X, Y)) = 0.057721965790090174\n",
      "sqrt(loss(X, Y)) = 0.05770685082334189\n",
      "sqrt(loss(X, Y)) = 0.057702954958283974\n",
      "sqrt(loss(X, Y)) = 0.05768762060479499\n",
      "sqrt(loss(X, Y)) = 0.05768400216554817\n",
      "sqrt(loss(X, Y)) = 0.05766845339319066\n",
      "sqrt(loss(X, Y)) = 0.05766510726645213\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: Epoch 641\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sqrt(loss(X, Y)) = 0.05764935304974698\n",
      "sqrt(loss(X, Y)) = 0.057646273620597356\n",
      "sqrt(loss(X, Y)) = 0.05763032903180904\n",
      "sqrt(loss(X, Y)) = 0.0576275513860393\n",
      "sqrt(loss(X, Y)) = 0.05761220384692716\n",
      "sqrt(loss(X, Y)) = 0.05760963314455915\n",
      "sqrt(loss(X, Y)) = 0.057594081116174006\n",
      "sqrt(loss(X, Y)) = 0.05759172453268606\n",
      "sqrt(loss(X, Y)) = 0.05757594459191627\n",
      "sqrt(loss(X, Y)) = 0.057573810173290725\n",
      "sqrt(loss(X, Y)) = 0.05755771122272418\n",
      "sqrt(loss(X, Y)) = 0.057555821485343654\n",
      "sqrt(loss(X, Y)) = 0.05753854931704158\n",
      "sqrt(loss(X, Y)) = 0.05753711771523291\n",
      "sqrt(loss(X, Y)) = 0.05751980361534432\n",
      "sqrt(loss(X, Y)) = 0.05751874670107765\n",
      "sqrt(loss(X, Y)) = 0.05750139547127051\n",
      "sqrt(loss(X, Y)) = 0.05749955258871089\n",
      "sqrt(loss(X, Y)) = 0.057483389704529445\n",
      "sqrt(loss(X, Y)) = 0.05748185408753444\n",
      "sqrt(loss(X, Y)) = 0.05746558410967551\n",
      "sqrt(loss(X, Y)) = 0.0574643111587807\n",
      "sqrt(loss(X, Y)) = 0.05744791436863707\n",
      "sqrt(loss(X, Y)) = 0.05744687177736662\n",
      "sqrt(loss(X, Y)) = 0.05743034409846782\n",
      "sqrt(loss(X, Y)) = 0.057429510978747905\n",
      "sqrt(loss(X, Y)) = 0.05741285246324039\n",
      "sqrt(loss(X, Y)) = 0.05741221426618452\n",
      "sqrt(loss(X, Y)) = 0.057395430682245244\n",
      "sqrt(loss(X, Y)) = 0.057394976145823874\n",
      "sqrt(loss(X, Y)) = 0.057378072235857995\n",
      "sqrt(loss(X, Y)) = 0.05737779298857667\n",
      "sqrt(loss(X, Y)) = 0.05736077755731981\n",
      "sqrt(loss(X, Y)) = 0.057360663429322035\n",
      "sqrt(loss(X, Y)) = 0.05734354385049874\n",
      "sqrt(loss(X, Y)) = 0.057343587116054154\n",
      "sqrt(loss(X, Y)) = 0.05732637138858229\n",
      "sqrt(loss(X, Y)) = 0.0573265606477523\n",
      "sqrt(loss(X, Y)) = 0.057309257148603354\n",
      "sqrt(loss(X, Y)) = 0.057309583324167995\n",
      "sqrt(loss(X, Y)) = 0.05729219971512358\n",
      "sqrt(loss(X, Y)) = 0.057292654111083446\n",
      "sqrt(loss(X, Y)) = 0.05727519907257537\n",
      "sqrt(loss(X, Y)) = 0.0572757699471514\n",
      "sqrt(loss(X, Y)) = 0.057258249877214604\n",
      "sqrt(loss(X, Y)) = 0.05725893016720099\n",
      "sqrt(loss(X, Y)) = 0.05724135385774916\n",
      "sqrt(loss(X, Y)) = 0.0572421344087569\n",
      "sqrt(loss(X, Y)) = 0.057224510312540444\n",
      "sqrt(loss(X, Y)) = 0.057225678417383\n",
      "sqrt(loss(X, Y)) = 0.057208596613900854\n",
      "sqrt(loss(X, Y)) = 0.057210158308704184\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: Epoch 698\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 699\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 700\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 701\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 702\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 703\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 704\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 705\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 706\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 707\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 708\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 709\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 710\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 711\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 712\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 713\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 714\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 715\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 716\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 717\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 718\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 719\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 720\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 721\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 722\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 723\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 724\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 725\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 726\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 727\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 728\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 729\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 730\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 731\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 732\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 733\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 734\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 735\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 736\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 737\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 738\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 739\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 740\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 741\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 742\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 743\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 744\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 745\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 746\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 747\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 748\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 749\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sqrt(loss(X, Y)) = 0.05719283365191397\n",
      "sqrt(loss(X, Y)) = 0.05719467651467844\n",
      "sqrt(loss(X, Y)) = 0.057177139625273814\n",
      "sqrt(loss(X, Y)) = 0.057178604017125406\n",
      "sqrt(loss(X, Y)) = 0.05716220907121001\n",
      "sqrt(loss(X, Y)) = 0.05716576008167262\n",
      "sqrt(loss(X, Y)) = 0.057147300746508575\n",
      "sqrt(loss(X, Y)) = 0.057148972158873663\n",
      "sqrt(loss(X, Y)) = 0.05713331672286481\n",
      "sqrt(loss(X, Y)) = 0.05713985139681925\n",
      "sqrt(loss(X, Y)) = 0.05712014579700411\n",
      "sqrt(loss(X, Y)) = 0.05712321591140951\n",
      "sqrt(loss(X, Y)) = 0.05710641303354917\n",
      "sqrt(loss(X, Y)) = 0.05711311718349053\n",
      "sqrt(loss(X, Y)) = 0.05709218777090291\n",
      "sqrt(loss(X, Y)) = 0.057095380247337686\n",
      "sqrt(loss(X, Y)) = 0.05707819158428629\n",
      "sqrt(loss(X, Y)) = 0.05708571685798464\n",
      "sqrt(loss(X, Y)) = 0.057066570971089886\n",
      "sqrt(loss(X, Y)) = 0.05707405707518032\n",
      "sqrt(loss(X, Y)) = 0.057055022765257944\n",
      "sqrt(loss(X, Y)) = 0.05706392094151823\n",
      "sqrt(loss(X, Y)) = 0.057044279601195534\n",
      "sqrt(loss(X, Y)) = 0.05705366894905468\n",
      "sqrt(loss(X, Y)) = 0.05703343160418993\n",
      "sqrt(loss(X, Y)) = 0.05704319672760026\n",
      "sqrt(loss(X, Y)) = 0.05702238100727148\n",
      "sqrt(loss(X, Y)) = 0.05703251619613544\n",
      "sqrt(loss(X, Y)) = 0.05701143209821488\n",
      "sqrt(loss(X, Y)) = 0.05702191539140446\n",
      "sqrt(loss(X, Y)) = 0.05700035124279222\n",
      "sqrt(loss(X, Y)) = 0.05701166416410725\n",
      "sqrt(loss(X, Y)) = 0.056992967283642046\n",
      "sqrt(loss(X, Y)) = 0.05700763697923116\n",
      "sqrt(loss(X, Y)) = 0.05698761015518208\n",
      "sqrt(loss(X, Y)) = 0.05700170394158936\n",
      "sqrt(loss(X, Y)) = 0.05698056712896279\n",
      "sqrt(loss(X, Y)) = 0.056994727187437345\n",
      "sqrt(loss(X, Y)) = 0.05697432592926189\n",
      "sqrt(loss(X, Y)) = 0.05698956188575282\n",
      "sqrt(loss(X, Y)) = 0.0569685312729287\n",
      "sqrt(loss(X, Y)) = 0.05698379192578188\n",
      "sqrt(loss(X, Y)) = 0.05696199277138228\n",
      "sqrt(loss(X, Y)) = 0.05697734596625594\n",
      "sqrt(loss(X, Y)) = 0.056955079277439466\n",
      "sqrt(loss(X, Y)) = 0.05697049103916148\n",
      "sqrt(loss(X, Y)) = 0.056947813886305675\n",
      "sqrt(loss(X, Y)) = 0.05696326488076102\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: Epoch 750\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 751\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 752\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 753\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 754\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 755\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
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      "┌ Info: Epoch 757\n",
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      "┌ Info: Epoch 758\n",
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      "┌ Info: Epoch 759\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
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      "┌ Info: Epoch 761\n",
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      "┌ Info: Epoch 762\n",
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      "┌ Info: Epoch 768\n",
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      "┌ Info: Epoch 797\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sqrt(loss(X, Y)) = 0.05694022453517065\n",
      "sqrt(loss(X, Y)) = 0.056955683897366285\n",
      "sqrt(loss(X, Y)) = 0.05693232788006943\n",
      "sqrt(loss(X, Y)) = 0.05694814394466227\n",
      "sqrt(loss(X, Y)) = 0.05692608635453704\n",
      "sqrt(loss(X, Y)) = 0.05694190104324332\n",
      "sqrt(loss(X, Y)) = 0.056918941180793436\n",
      "sqrt(loss(X, Y)) = 0.05693406183086528\n",
      "sqrt(loss(X, Y)) = 0.05691092344027283\n",
      "sqrt(loss(X, Y)) = 0.05692608276023741\n",
      "sqrt(loss(X, Y)) = 0.056902801625308704\n",
      "sqrt(loss(X, Y)) = 0.05691784750664774\n",
      "sqrt(loss(X, Y)) = 0.05689445687982768\n",
      "sqrt(loss(X, Y)) = 0.056909386137382084\n",
      "sqrt(loss(X, Y)) = 0.05688591696892082\n",
      "sqrt(loss(X, Y)) = 0.05690070463449574\n",
      "sqrt(loss(X, Y)) = 0.056877182509976074\n",
      "sqrt(loss(X, Y)) = 0.056891802046954085\n",
      "sqrt(loss(X, Y)) = 0.05686558894862196\n",
      "sqrt(loss(X, Y)) = 0.056880091384689864\n",
      "sqrt(loss(X, Y)) = 0.0568536534780626\n",
      "sqrt(loss(X, Y)) = 0.05686871801847493\n",
      "sqrt(loss(X, Y)) = 0.05684417494578205\n",
      "sqrt(loss(X, Y)) = 0.05686281002144128\n",
      "sqrt(loss(X, Y)) = 0.05683756358709785\n",
      "sqrt(loss(X, Y)) = 0.05685529320439482\n",
      "sqrt(loss(X, Y)) = 0.05682954818542418\n",
      "sqrt(loss(X, Y)) = 0.056844539780427195\n",
      "sqrt(loss(X, Y)) = 0.05681833728395577\n",
      "sqrt(loss(X, Y)) = 0.0568334331010858\n",
      "sqrt(loss(X, Y)) = 0.05680870090369167\n",
      "sqrt(loss(X, Y)) = 0.056827227390242645\n",
      "sqrt(loss(X, Y)) = 0.056806056161805736\n",
      "sqrt(loss(X, Y)) = 0.05682585900932488\n",
      "sqrt(loss(X, Y)) = 0.05680418344800197\n",
      "sqrt(loss(X, Y)) = 0.05682373163604104\n",
      "sqrt(loss(X, Y)) = 0.056801746723381286\n",
      "sqrt(loss(X, Y)) = 0.05682113942524872\n",
      "sqrt(loss(X, Y)) = 0.0567988490540942\n",
      "sqrt(loss(X, Y)) = 0.056816451708996574\n",
      "sqrt(loss(X, Y)) = 0.056792772281972104\n",
      "sqrt(loss(X, Y)) = 0.056810585805432766\n",
      "sqrt(loss(X, Y)) = 0.05678682979666512\n",
      "sqrt(loss(X, Y)) = 0.05680430216887036\n",
      "sqrt(loss(X, Y)) = 0.05678062048030207\n",
      "sqrt(loss(X, Y)) = 0.05679764681197206\n",
      "sqrt(loss(X, Y)) = 0.056774159740332404\n",
      "sqrt(loss(X, Y)) = 0.05679055967879771\n",
      "sqrt(loss(X, Y)) = 0.05676738383800216\n",
      "sqrt(loss(X, Y)) = 0.05678296366438703\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: Epoch 798\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 799\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 800\n",
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      "┌ Info: Epoch 801\n",
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      "┌ Info: Epoch 802\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 803\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 804\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 805\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 806\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 807\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 808\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 809\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 810\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 811\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 812\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 813\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 814\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 815\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 816\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 817\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 818\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 819\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 820\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 821\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 822\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 823\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 824\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 825\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 826\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 827\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 828\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 829\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 830\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 831\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 832\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 833\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 834\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 835\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 836\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 837\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 838\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 839\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 840\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 841\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 842\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 843\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 844\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 845\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 846\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 847\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 848\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sqrt(loss(X, Y)) = 0.05675998271232353\n",
      "sqrt(loss(X, Y)) = 0.056772314552168975\n",
      "sqrt(loss(X, Y)) = 0.05678879327463814\n",
      "sqrt(loss(X, Y)) = 0.05677842810148282\n",
      "sqrt(loss(X, Y)) = 0.05674673037775183\n",
      "sqrt(loss(X, Y)) = 0.05679189232881522\n",
      "sqrt(loss(X, Y)) = 0.0568407453555807\n",
      "sqrt(loss(X, Y)) = 0.05679248550863774\n",
      "sqrt(loss(X, Y)) = 0.05677239184214742\n",
      "sqrt(loss(X, Y)) = 0.05674810504741858\n",
      "sqrt(loss(X, Y)) = 0.05672433500161262\n",
      "sqrt(loss(X, Y)) = 0.05676028321226239\n",
      "sqrt(loss(X, Y)) = 0.05680903882042214\n",
      "sqrt(loss(X, Y)) = 0.056760578364140855\n",
      "sqrt(loss(X, Y)) = 0.056745956099711344\n",
      "sqrt(loss(X, Y)) = 0.05673542423715397\n",
      "sqrt(loss(X, Y)) = 0.05677798416572334\n",
      "sqrt(loss(X, Y)) = 0.056740530790977455\n",
      "sqrt(loss(X, Y)) = 0.056727780964528435\n",
      "sqrt(loss(X, Y)) = 0.05672104632897028\n",
      "sqrt(loss(X, Y)) = 0.05676997711006849\n",
      "sqrt(loss(X, Y)) = 0.056717466923016914\n",
      "sqrt(loss(X, Y)) = 0.05670416788162051\n",
      "sqrt(loss(X, Y)) = 0.05681624682234211\n",
      "sqrt(loss(X, Y)) = 0.056918669966415876\n",
      "sqrt(loss(X, Y)) = 0.056658764086124844\n",
      "sqrt(loss(X, Y)) = 0.0566036720284911\n",
      "sqrt(loss(X, Y)) = 0.056838274688051846\n",
      "sqrt(loss(X, Y)) = 0.056934188370515816\n",
      "sqrt(loss(X, Y)) = 0.05656022012036263\n",
      "sqrt(loss(X, Y)) = 0.05650380608515454\n",
      "sqrt(loss(X, Y)) = 0.056853641307897315\n",
      "sqrt(loss(X, Y)) = 0.05648175784492129\n",
      "sqrt(loss(X, Y)) = 0.05643043602896497\n",
      "sqrt(loss(X, Y)) = 0.05680172240269454\n",
      "sqrt(loss(X, Y)) = 0.0564106401108543\n",
      "sqrt(loss(X, Y)) = 0.0563601864868072\n",
      "sqrt(loss(X, Y)) = 0.05673386040873656\n",
      "sqrt(loss(X, Y)) = 0.05634213260129119\n",
      "sqrt(loss(X, Y)) = 0.056299165404649625\n",
      "sqrt(loss(X, Y)) = 0.05667103019911272\n",
      "sqrt(loss(X, Y)) = 0.05628284845783439\n",
      "sqrt(loss(X, Y)) = 0.05624032589100861\n",
      "sqrt(loss(X, Y)) = 0.05663227533586654\n",
      "sqrt(loss(X, Y)) = 0.0562325071529363\n",
      "sqrt(loss(X, Y)) = 0.056194180357618265\n",
      "sqrt(loss(X, Y)) = 0.05658285455438387\n",
      "sqrt(loss(X, Y)) = 0.056187350840600614\n",
      "sqrt(loss(X, Y)) = 0.056148517174536706\n",
      "sqrt(loss(X, Y)) = 0.05653592387520067\n",
      "sqrt(loss(X, Y)) = 0.056142140566029605\n",
      "sqrt(loss(X, Y)) = 0.0561032682874371\n",
      "sqrt(loss(X, Y)) = 0.05648980651888335\n",
      "sqrt(loss(X, Y)) = 0.056097788414640255\n",
      "sqrt(loss(X, Y)) = 0.05605882600601499\n",
      "sqrt(loss(X, Y)) = 0.056447789216444604\n",
      "sqrt(loss(X, Y)) = 0.056056930546967774\n",
      "sqrt(loss(X, Y)) = 0.0560179721631396\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: Epoch 849\n",
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      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 894\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 895\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 896\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 897\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 898\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 899\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 900\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 901\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 902\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 903\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 904\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 905\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sqrt(loss(X, Y)) = 0.05641186187671783\n",
      "sqrt(loss(X, Y)) = 0.056015514146749844\n",
      "sqrt(loss(X, Y)) = 0.05597869589575177\n",
      "sqrt(loss(X, Y)) = 0.0563674190340678\n",
      "sqrt(loss(X, Y)) = 0.05597349522225302\n",
      "sqrt(loss(X, Y)) = 0.05593645873523682\n",
      "sqrt(loss(X, Y)) = 0.0563276953466528\n",
      "sqrt(loss(X, Y)) = 0.05593460780863967\n",
      "sqrt(loss(X, Y)) = 0.055897587114271736\n",
      "sqrt(loss(X, Y)) = 0.056284384379708084\n",
      "sqrt(loss(X, Y)) = 0.05589264483036015\n",
      "sqrt(loss(X, Y)) = 0.05585555552595655\n",
      "sqrt(loss(X, Y)) = 0.056241672625769595\n",
      "sqrt(loss(X, Y)) = 0.05585050327121168\n",
      "sqrt(loss(X, Y)) = 0.05581309465027288\n",
      "sqrt(loss(X, Y)) = 0.056197564965536365\n",
      "sqrt(loss(X, Y)) = 0.05580851097472711\n",
      "sqrt(loss(X, Y)) = 0.05577152668329082\n",
      "sqrt(loss(X, Y)) = 0.05615549115775247\n",
      "sqrt(loss(X, Y)) = 0.055766479207564036\n",
      "sqrt(loss(X, Y)) = 0.055729161926384316\n",
      "sqrt(loss(X, Y)) = 0.05611187399980955\n",
      "sqrt(loss(X, Y)) = 0.05572511674447278\n",
      "sqrt(loss(X, Y)) = 0.055690515789699725\n",
      "sqrt(loss(X, Y)) = 0.05607407776416092\n",
      "sqrt(loss(X, Y)) = 0.055685689482567996\n",
      "sqrt(loss(X, Y)) = 0.05565082251168884\n",
      "sqrt(loss(X, Y)) = 0.0560332425052714\n",
      "sqrt(loss(X, Y)) = 0.055646051626202696\n",
      "sqrt(loss(X, Y)) = 0.05561127554853626\n",
      "sqrt(loss(X, Y)) = 0.05599595384146751\n",
      "sqrt(loss(X, Y)) = 0.05560557047365356\n",
      "sqrt(loss(X, Y)) = 0.055569809267524514\n",
      "sqrt(loss(X, Y)) = 0.05595210535769007\n",
      "sqrt(loss(X, Y)) = 0.055564653114142916\n",
      "sqrt(loss(X, Y)) = 0.05553105460397313\n",
      "sqrt(loss(X, Y)) = 0.05591401092066383\n",
      "sqrt(loss(X, Y)) = 0.05552568069191706\n",
      "sqrt(loss(X, Y)) = 0.055492394007650714\n",
      "sqrt(loss(X, Y)) = 0.055874589081963424\n",
      "sqrt(loss(X, Y)) = 0.05548699439624699\n",
      "sqrt(loss(X, Y)) = 0.05545367609027022\n",
      "sqrt(loss(X, Y)) = 0.05583539865557795\n",
      "sqrt(loss(X, Y)) = 0.05544821426663404\n",
      "sqrt(loss(X, Y)) = 0.055414924851408476\n",
      "sqrt(loss(X, Y)) = 0.055796148593685875\n",
      "sqrt(loss(X, Y)) = 0.05540939722881499\n",
      "sqrt(loss(X, Y)) = 0.0553761127195011\n",
      "sqrt(loss(X, Y)) = 0.05575687109744331\n",
      "sqrt(loss(X, Y)) = 0.055370509601959855\n",
      "sqrt(loss(X, Y)) = 0.05533723097354482\n",
      "sqrt(loss(X, Y)) = 0.05572113337439225\n",
      "sqrt(loss(X, Y)) = 0.055330403977562725\n",
      "sqrt(loss(X, Y)) = 0.055299199340335344\n",
      "sqrt(loss(X, Y)) = 0.055681113030177344\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: Epoch 906\n",
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      "┌ Info: Epoch 907\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 908\n",
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      "┌ Info: Epoch 909\n",
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      "┌ Info: Epoch 910\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 911\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 912\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 913\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 914\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 915\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 916\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 917\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 918\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 919\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 920\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 921\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 922\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 923\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 924\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 925\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 926\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 927\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 928\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 929\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 930\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 931\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 932\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 933\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 934\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 935\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 936\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 937\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 938\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 939\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 940\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 941\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 942\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 943\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 944\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 945\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 946\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 947\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 948\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 949\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 950\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 951\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 952\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 953\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 954\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 955\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 956\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 957\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 958\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 959\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 960\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 961\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sqrt(loss(X, Y)) = 0.055292424042606286\n",
      "sqrt(loss(X, Y)) = 0.05526088416123257\n",
      "sqrt(loss(X, Y)) = 0.05564256833168308\n",
      "sqrt(loss(X, Y)) = 0.05525399018159047\n",
      "sqrt(loss(X, Y)) = 0.05522260204775419\n",
      "sqrt(loss(X, Y)) = 0.055603655077104734\n",
      "sqrt(loss(X, Y)) = 0.055215620676635525\n",
      "sqrt(loss(X, Y)) = 0.05518425142519178\n",
      "sqrt(loss(X, Y)) = 0.05556460947605327\n",
      "sqrt(loss(X, Y)) = 0.05517704090839409\n",
      "sqrt(loss(X, Y)) = 0.0551457127802835\n",
      "sqrt(loss(X, Y)) = 0.05552082344380765\n",
      "sqrt(loss(X, Y)) = 0.05513480492481145\n",
      "sqrt(loss(X, Y)) = 0.055103295977586696\n",
      "sqrt(loss(X, Y)) = 0.05547823172026252\n",
      "sqrt(loss(X, Y)) = 0.05509232561320308\n",
      "sqrt(loss(X, Y)) = 0.05506135389530564\n",
      "sqrt(loss(X, Y)) = 0.05543542864365096\n",
      "sqrt(loss(X, Y)) = 0.05505038021279641\n",
      "sqrt(loss(X, Y)) = 0.05501964492063363\n",
      "sqrt(loss(X, Y)) = 0.055393047693558524\n",
      "sqrt(loss(X, Y)) = 0.05500857268483249\n",
      "sqrt(loss(X, Y)) = 0.05497815788891455\n",
      "sqrt(loss(X, Y)) = 0.05535090382008166\n",
      "sqrt(loss(X, Y)) = 0.054966909926500225\n",
      "sqrt(loss(X, Y)) = 0.054936901760745976\n",
      "sqrt(loss(X, Y)) = 0.0553091355828689\n",
      "sqrt(loss(X, Y)) = 0.054925336964276136\n",
      "sqrt(loss(X, Y)) = 0.05489599283634645\n",
      "sqrt(loss(X, Y)) = 0.05526702328892221\n",
      "sqrt(loss(X, Y)) = 0.05488431432323501\n",
      "sqrt(loss(X, Y)) = 0.05485511724486131\n",
      "sqrt(loss(X, Y)) = 0.05522544667285982\n",
      "sqrt(loss(X, Y)) = 0.054839774673677\n",
      "sqrt(loss(X, Y)) = 0.05481065707878169\n",
      "sqrt(loss(X, Y)) = 0.05518014275855937\n",
      "sqrt(loss(X, Y)) = 0.05479891831657687\n",
      "sqrt(loss(X, Y)) = 0.05477044971468553\n",
      "sqrt(loss(X, Y)) = 0.055138358902762016\n",
      "sqrt(loss(X, Y)) = 0.054755463262424975\n",
      "sqrt(loss(X, Y)) = 0.05472675585483185\n",
      "sqrt(loss(X, Y)) = 0.05509416230811883\n",
      "sqrt(loss(X, Y)) = 0.05471490052095381\n",
      "sqrt(loss(X, Y)) = 0.054687382681449\n",
      "sqrt(loss(X, Y)) = 0.055052481756255225\n",
      "sqrt(loss(X, Y)) = 0.05467147978707257\n",
      "sqrt(loss(X, Y)) = 0.05464333857063624\n",
      "sqrt(loss(X, Y)) = 0.0550075524924059\n",
      "sqrt(loss(X, Y)) = 0.054628123429696425\n",
      "sqrt(loss(X, Y)) = 0.05460229615499173\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: Epoch 962\n",
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      "┌ Info: Epoch 990\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 991\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 992\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 993\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 994\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 995\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 996\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 997\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 998\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 999\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1000\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1001\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1002\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1003\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1004\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1005\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1006\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1007\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1008\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1009\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1010\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1011\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sqrt(loss(X, Y)) = 0.05496333495059453\n",
      "sqrt(loss(X, Y)) = 0.05458528011350464\n",
      "sqrt(loss(X, Y)) = 0.05455585382962924\n",
      "sqrt(loss(X, Y)) = 0.0549137636646829\n",
      "sqrt(loss(X, Y)) = 0.054543281905376266\n",
      "sqrt(loss(X, Y)) = 0.054514454741997426\n",
      "sqrt(loss(X, Y)) = 0.05486996854541493\n",
      "sqrt(loss(X, Y)) = 0.05450186233171069\n",
      "sqrt(loss(X, Y)) = 0.0544737618528227\n",
      "sqrt(loss(X, Y)) = 0.05482777761000755\n",
      "sqrt(loss(X, Y)) = 0.054460833646142195\n",
      "sqrt(loss(X, Y)) = 0.05443319394158901\n",
      "sqrt(loss(X, Y)) = 0.05478972900413834\n",
      "sqrt(loss(X, Y)) = 0.05441933590109971\n",
      "sqrt(loss(X, Y)) = 0.054396039869198005\n",
      "sqrt(loss(X, Y)) = 0.05474679856242539\n",
      "sqrt(loss(X, Y)) = 0.05438212814215862\n",
      "sqrt(loss(X, Y)) = 0.05435731543881325\n",
      "sqrt(loss(X, Y)) = 0.05470848221093677\n",
      "sqrt(loss(X, Y)) = 0.05434340365019669\n",
      "sqrt(loss(X, Y)) = 0.0543173871329079\n",
      "sqrt(loss(X, Y)) = 0.05466946747486958\n",
      "sqrt(loss(X, Y)) = 0.054300729018865526\n",
      "sqrt(loss(X, Y)) = 0.05427362015055359\n",
      "sqrt(loss(X, Y)) = 0.05462632431956451\n",
      "sqrt(loss(X, Y)) = 0.05426004800026536\n",
      "sqrt(loss(X, Y)) = 0.05423219404835531\n",
      "sqrt(loss(X, Y)) = 0.054585052280578555\n",
      "sqrt(loss(X, Y)) = 0.05421886774254629\n",
      "sqrt(loss(X, Y)) = 0.05419017732533932\n",
      "sqrt(loss(X, Y)) = 0.05454423481869582\n",
      "sqrt(loss(X, Y)) = 0.054128641983796244\n",
      "sqrt(loss(X, Y)) = 0.05413088673460882\n",
      "sqrt(loss(X, Y)) = 0.05450088473395761\n",
      "sqrt(loss(X, Y)) = 0.05410670645356404\n",
      "sqrt(loss(X, Y)) = 0.054083944194052\n",
      "sqrt(loss(X, Y)) = 0.05443319890735914\n",
      "sqrt(loss(X, Y)) = 0.054011550255860796\n",
      "sqrt(loss(X, Y)) = 0.054021310945696385\n",
      "sqrt(loss(X, Y)) = 0.054394797493962074\n",
      "sqrt(loss(X, Y)) = 0.05398630395223415\n",
      "sqrt(loss(X, Y)) = 0.05396578423828305\n",
      "sqrt(loss(X, Y)) = 0.05431383548397328\n",
      "sqrt(loss(X, Y)) = 0.05391210692899359\n",
      "sqrt(loss(X, Y)) = 0.053970366884017705\n",
      "sqrt(loss(X, Y)) = 0.054285066082853106\n",
      "sqrt(loss(X, Y)) = 0.05384994401090974\n",
      "sqrt(loss(X, Y)) = 0.053831003043179115\n",
      "sqrt(loss(X, Y)) = 0.05417378017846716\n",
      "sqrt(loss(X, Y)) = 0.0539925908841515\n",
      "sqrt(loss(X, Y)) = 0.05411954884973696\n",
      "sqrt(loss(X, Y)) = 0.05371285480398991\n",
      "sqrt(loss(X, Y)) = 0.05377249453892415\n",
      "sqrt(loss(X, Y)) = 0.05408662674029492\n",
      "sqrt(loss(X, Y)) = 0.05364994774720262\n",
      "sqrt(loss(X, Y)) = 0.05363119629160453\n",
      "sqrt(loss(X, Y)) = 0.053980354451510545\n",
      "sqrt(loss(X, Y)) = 0.05383772775536774\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: Epoch 1012\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1013\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1014\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1015\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1016\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1017\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1018\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1019\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1020\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1021\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1022\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1023\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1024\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1025\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1026\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1027\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1028\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1029\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1030\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1031\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1032\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1033\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1034\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1035\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1036\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1037\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1038\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1039\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1040\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1041\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1042\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1043\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1044\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1045\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1046\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1047\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1048\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1049\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1050\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1051\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1052\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1053\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1054\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1055\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1056\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1057\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1058\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1059\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1060\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1061\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1062\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1063\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1064\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1065\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1066\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1067\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1068\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1069\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sqrt(loss(X, Y)) = 0.05395997408864137\n",
      "sqrt(loss(X, Y)) = 0.05353516446715135\n",
      "sqrt(loss(X, Y)) = 0.05351977857035044\n",
      "sqrt(loss(X, Y)) = 0.05386999507603762\n",
      "sqrt(loss(X, Y)) = 0.05375473109195139\n",
      "sqrt(loss(X, Y)) = 0.053865364834046976\n",
      "sqrt(loss(X, Y)) = 0.053419316856926115\n",
      "sqrt(loss(X, Y)) = 0.05339998452842331\n",
      "sqrt(loss(X, Y)) = 0.05375124638926193\n",
      "sqrt(loss(X, Y)) = 0.05365140290087185\n",
      "sqrt(loss(X, Y)) = 0.05375443535951463\n",
      "sqrt(loss(X, Y)) = 0.05330015866108681\n",
      "sqrt(loss(X, Y)) = 0.0532805146111932\n",
      "sqrt(loss(X, Y)) = 0.05363209250790877\n",
      "sqrt(loss(X, Y)) = 0.05353879846767627\n",
      "sqrt(loss(X, Y)) = 0.053643887052077184\n",
      "sqrt(loss(X, Y)) = 0.05317885162138726\n",
      "sqrt(loss(X, Y)) = 0.053164636591694354\n",
      "sqrt(loss(X, Y)) = 0.05351632874364119\n",
      "sqrt(loss(X, Y)) = 0.05343603645912598\n",
      "sqrt(loss(X, Y)) = 0.0535382125016165\n",
      "sqrt(loss(X, Y)) = 0.05305547197543971\n",
      "sqrt(loss(X, Y)) = 0.053040882297180854\n",
      "sqrt(loss(X, Y)) = 0.05338943062693925\n",
      "sqrt(loss(X, Y)) = 0.05331393460147185\n",
      "sqrt(loss(X, Y)) = 0.05341784835383083\n",
      "sqrt(loss(X, Y)) = 0.05293130490964266\n",
      "sqrt(loss(X, Y)) = 0.052916475544063715\n",
      "sqrt(loss(X, Y)) = 0.05326521792113266\n",
      "sqrt(loss(X, Y)) = 0.05320732986933488\n",
      "sqrt(loss(X, Y)) = 0.05331451340625906\n",
      "sqrt(loss(X, Y)) = 0.05280874536723161\n",
      "sqrt(loss(X, Y)) = 0.052795633066877797\n",
      "sqrt(loss(X, Y)) = 0.05314425789864927\n",
      "sqrt(loss(X, Y)) = 0.05310310269465934\n",
      "sqrt(loss(X, Y)) = 0.05320518726764734\n",
      "sqrt(loss(X, Y)) = 0.052683718800271326\n",
      "sqrt(loss(X, Y)) = 0.05267149315225819\n",
      "sqrt(loss(X, Y)) = 0.05302037557840704\n",
      "sqrt(loss(X, Y)) = 0.0529989634628651\n",
      "sqrt(loss(X, Y)) = 0.05309907099128792\n",
      "sqrt(loss(X, Y)) = 0.05255675475202249\n",
      "sqrt(loss(X, Y)) = 0.0525460693794476\n",
      "sqrt(loss(X, Y)) = 0.05289551044944349\n",
      "sqrt(loss(X, Y)) = 0.05288986862735483\n",
      "sqrt(loss(X, Y)) = 0.052983084362675896\n",
      "sqrt(loss(X, Y)) = 0.05243299383201723\n",
      "sqrt(loss(X, Y)) = 0.05242219546434275\n",
      "sqrt(loss(X, Y)) = 0.05277156497441398\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: Epoch 1070\n",
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      "┌ Info: Epoch 1080\n",
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      "┌ Info: Epoch 1085\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1086\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1087\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1088\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1089\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1090\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1091\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1092\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1093\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1094\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1095\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1096\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1097\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1098\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1099\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1100\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1101\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1102\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1103\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1104\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1105\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1106\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1107\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1108\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1109\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1110\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1111\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1112\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1113\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1114\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1115\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1116\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1117\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1118\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sqrt(loss(X, Y)) = 0.05279421523484237\n",
      "sqrt(loss(X, Y)) = 0.05285108081601608\n",
      "sqrt(loss(X, Y)) = 0.05230703573380973\n",
      "sqrt(loss(X, Y)) = 0.05229340474850141\n",
      "sqrt(loss(X, Y)) = 0.05264203225625941\n",
      "sqrt(loss(X, Y)) = 0.052716275074345814\n",
      "sqrt(loss(X, Y)) = 0.05251105107014952\n",
      "sqrt(loss(X, Y)) = 0.05262736800984263\n",
      "sqrt(loss(X, Y)) = 0.05208349499641467\n",
      "sqrt(loss(X, Y)) = 0.052103013176603624\n",
      "sqrt(loss(X, Y)) = 0.05247445253161819\n",
      "sqrt(loss(X, Y)) = 0.05234709940052506\n",
      "sqrt(loss(X, Y)) = 0.05246498345410128\n",
      "sqrt(loss(X, Y)) = 0.05191413668798173\n",
      "sqrt(loss(X, Y)) = 0.05193464052730413\n",
      "sqrt(loss(X, Y)) = 0.0523175550796298\n",
      "sqrt(loss(X, Y)) = 0.052195304739660664\n",
      "sqrt(loss(X, Y)) = 0.05231098746085735\n",
      "sqrt(loss(X, Y)) = 0.05175930437378108\n",
      "sqrt(loss(X, Y)) = 0.05178088620292739\n",
      "sqrt(loss(X, Y)) = 0.052165124627573396\n",
      "sqrt(loss(X, Y)) = 0.052044438366580986\n",
      "sqrt(loss(X, Y)) = 0.05216819513651853\n",
      "sqrt(loss(X, Y)) = 0.05160611192706843\n",
      "sqrt(loss(X, Y)) = 0.0516288811531226\n",
      "sqrt(loss(X, Y)) = 0.052019468302130334\n",
      "sqrt(loss(X, Y)) = 0.05198707128979088\n",
      "sqrt(loss(X, Y)) = 0.052094744431593766\n",
      "sqrt(loss(X, Y)) = 0.051494993439015216\n",
      "sqrt(loss(X, Y)) = 0.051487211026372284\n",
      "sqrt(loss(X, Y)) = 0.051846220231644514\n",
      "sqrt(loss(X, Y)) = 0.052059954555538863\n",
      "sqrt(loss(X, Y)) = 0.051427066812314425\n",
      "sqrt(loss(X, Y)) = 0.051421993847226156\n",
      "sqrt(loss(X, Y)) = 0.05178242888746951\n",
      "sqrt(loss(X, Y)) = 0.05199661258146052\n",
      "sqrt(loss(X, Y)) = 0.05135580654413886\n",
      "sqrt(loss(X, Y)) = 0.051350863211434554\n",
      "sqrt(loss(X, Y)) = 0.05171161654778249\n",
      "sqrt(loss(X, Y)) = 0.05192670050390163\n",
      "sqrt(loss(X, Y)) = 0.05128386155360195\n",
      "sqrt(loss(X, Y)) = 0.051278972431465786\n",
      "sqrt(loss(X, Y)) = 0.05163980975944009\n",
      "sqrt(loss(X, Y)) = 0.051855852040974655\n",
      "sqrt(loss(X, Y)) = 0.051211240782709014\n",
      "sqrt(loss(X, Y)) = 0.05120639575200972\n",
      "sqrt(loss(X, Y)) = 0.05156603819946046\n",
      "sqrt(loss(X, Y)) = 0.051782989094411536\n",
      "sqrt(loss(X, Y)) = 0.05113553598982515\n",
      "sqrt(loss(X, Y)) = 0.05113149263016413\n",
      "sqrt(loss(X, Y)) = 0.051494010777601774\n",
      "sqrt(loss(X, Y)) = 0.05171184900220843\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: Epoch 1119\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1120\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1121\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1122\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1123\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1124\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1125\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1126\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1127\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1128\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1129\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1130\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1131\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1132\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1133\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1134\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1135\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1136\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1137\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1138\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1139\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1140\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1141\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1142\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1143\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1144\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1145\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1146\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1147\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1148\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1149\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1150\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1151\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1152\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1153\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1154\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1155\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1156\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1157\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1158\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1159\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1160\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1161\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1162\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1163\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1164\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1165\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1166\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1167\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1168\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1169\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sqrt(loss(X, Y)) = 0.05105418439439451\n",
      "sqrt(loss(X, Y)) = 0.051053016195072944\n",
      "sqrt(loss(X, Y)) = 0.051419544345795945\n",
      "sqrt(loss(X, Y)) = 0.05163713581411465\n",
      "sqrt(loss(X, Y)) = 0.050973623027061994\n",
      "sqrt(loss(X, Y)) = 0.05097287599153247\n",
      "sqrt(loss(X, Y)) = 0.051339614225040006\n",
      "sqrt(loss(X, Y)) = 0.051558266137394455\n",
      "sqrt(loss(X, Y)) = 0.050892867024951306\n",
      "sqrt(loss(X, Y)) = 0.05088695406159212\n",
      "sqrt(loss(X, Y)) = 0.05125370995354878\n",
      "sqrt(loss(X, Y)) = 0.05147279023959723\n",
      "sqrt(loss(X, Y)) = 0.0508065919252328\n",
      "sqrt(loss(X, Y)) = 0.05080596490111607\n",
      "sqrt(loss(X, Y)) = 0.05117208071178126\n",
      "sqrt(loss(X, Y)) = 0.05139249302630643\n",
      "sqrt(loss(X, Y)) = 0.0507252999443409\n",
      "sqrt(loss(X, Y)) = 0.05071944788624822\n",
      "sqrt(loss(X, Y)) = 0.05108456393126292\n",
      "sqrt(loss(X, Y)) = 0.05130536483846765\n",
      "sqrt(loss(X, Y)) = 0.05063846102913539\n",
      "sqrt(loss(X, Y)) = 0.05063249324841276\n",
      "sqrt(loss(X, Y)) = 0.05099815162504323\n",
      "sqrt(loss(X, Y)) = 0.05121993375690761\n",
      "sqrt(loss(X, Y)) = 0.05055163555053651\n",
      "sqrt(loss(X, Y)) = 0.05055104162518604\n",
      "sqrt(loss(X, Y)) = 0.05091516537219911\n",
      "sqrt(loss(X, Y)) = 0.05113812827238316\n",
      "sqrt(loss(X, Y)) = 0.050469799111147594\n",
      "sqrt(loss(X, Y)) = 0.050463928993029226\n",
      "sqrt(loss(X, Y)) = 0.05082830252743786\n",
      "sqrt(loss(X, Y)) = 0.05105190599917323\n",
      "sqrt(loss(X, Y)) = 0.050357490879305966\n",
      "sqrt(loss(X, Y)) = 0.05038710014695439\n",
      "sqrt(loss(X, Y)) = 0.050772590963395606\n",
      "sqrt(loss(X, Y)) = 0.05088742412155196\n",
      "sqrt(loss(X, Y)) = 0.050320595505431255\n",
      "sqrt(loss(X, Y)) = 0.050421289431388724\n",
      "sqrt(loss(X, Y)) = 0.05073668194575185\n",
      "sqrt(loss(X, Y)) = 0.050209903148617986\n",
      "sqrt(loss(X, Y)) = 0.050311744400683436\n",
      "sqrt(loss(X, Y)) = 0.05062923718454738\n",
      "sqrt(loss(X, Y)) = 0.050100135480419965\n",
      "sqrt(loss(X, Y)) = 0.05020313247440561\n",
      "sqrt(loss(X, Y)) = 0.05052227003937039\n",
      "sqrt(loss(X, Y)) = 0.049992163827165516\n",
      "sqrt(loss(X, Y)) = 0.050096724031821416\n",
      "sqrt(loss(X, Y)) = 0.050417252364527904\n",
      "sqrt(loss(X, Y)) = 0.049920678873197434\n",
      "sqrt(loss(X, Y)) = 0.0500373446808771\n",
      "sqrt(loss(X, Y)) = 0.05033413725550207\n",
      "sqrt(loss(X, Y)) = 0.04978892555411974\n",
      "sqrt(loss(X, Y)) = 0.04989533589999199\n",
      "sqrt(loss(X, Y)) = 0.05022035306659577\n",
      "sqrt(loss(X, Y)) = 0.04999241043300664\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: Epoch 1170\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1171\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1172\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1173\n",
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      "┌ Info: Epoch 1175\n",
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      "┌ Info: Epoch 1176\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1177\n",
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      "┌ Info: Epoch 1178\n",
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      "┌ Info: Epoch 1179\n",
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      "┌ Info: Epoch 1180\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1181\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1182\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1183\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1184\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1185\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1186\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1187\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1188\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1189\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1190\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1191\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1192\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1193\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1194\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1195\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1196\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1197\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1198\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1199\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1200\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1201\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1202\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1203\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1204\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1205\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1206\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1207\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1208\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1209\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1210\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1211\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1212\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1213\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1214\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1215\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1216\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1217\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1218\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1219\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1220\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1221\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1222\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1223\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1224\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sqrt(loss(X, Y)) = 0.05014462979442887\n",
      "sqrt(loss(X, Y)) = 0.049917370607028624\n",
      "sqrt(loss(X, Y)) = 0.050071814845666085\n",
      "sqrt(loss(X, Y)) = 0.04983865858989305\n",
      "sqrt(loss(X, Y)) = 0.05000499142232712\n",
      "sqrt(loss(X, Y)) = 0.04949224068927237\n",
      "sqrt(loss(X, Y)) = 0.04961377792232464\n",
      "sqrt(loss(X, Y)) = 0.04993302480476887\n",
      "sqrt(loss(X, Y)) = 0.04936517344130381\n",
      "sqrt(loss(X, Y)) = 0.049476286465839334\n",
      "sqrt(loss(X, Y)) = 0.04981621958271212\n",
      "sqrt(loss(X, Y)) = 0.04958326693562444\n",
      "sqrt(loss(X, Y)) = 0.04975568721506475\n",
      "sqrt(loss(X, Y)) = 0.04937136111933461\n",
      "sqrt(loss(X, Y)) = 0.049534925911114996\n",
      "sqrt(loss(X, Y)) = 0.04977421358171184\n",
      "sqrt(loss(X, Y)) = 0.048992140366273834\n",
      "sqrt(loss(X, Y)) = 0.04900241938256496\n",
      "sqrt(loss(X, Y)) = 0.0493876617752209\n",
      "sqrt(loss(X, Y)) = 0.04964402882774205\n",
      "sqrt(loss(X, Y)) = 0.048908079658331945\n",
      "sqrt(loss(X, Y)) = 0.048958514611033684\n",
      "sqrt(loss(X, Y)) = 0.04936009105244086\n",
      "sqrt(loss(X, Y)) = 0.04960173850766599\n",
      "sqrt(loss(X, Y)) = 0.048805534543522766\n",
      "sqrt(loss(X, Y)) = 0.0488490691534749\n",
      "sqrt(loss(X, Y)) = 0.04925163238286736\n",
      "sqrt(loss(X, Y)) = 0.04949731885497274\n",
      "sqrt(loss(X, Y)) = 0.04870458196212886\n",
      "sqrt(loss(X, Y)) = 0.04874887770162018\n",
      "sqrt(loss(X, Y)) = 0.0491524962771796\n",
      "sqrt(loss(X, Y)) = 0.04940028379100214\n",
      "sqrt(loss(X, Y)) = 0.048604469616145604\n",
      "sqrt(loss(X, Y)) = 0.04864963762696877\n",
      "sqrt(loss(X, Y)) = 0.0490545937331908\n",
      "sqrt(loss(X, Y)) = 0.04930423730369425\n",
      "sqrt(loss(X, Y)) = 0.048505179721243775\n",
      "sqrt(loss(X, Y)) = 0.048558351939174065\n",
      "sqrt(loss(X, Y)) = 0.04896419395286638\n",
      "sqrt(loss(X, Y)) = 0.049212355700685224\n",
      "sqrt(loss(X, Y)) = 0.048398684529741415\n",
      "sqrt(loss(X, Y)) = 0.048445579111094046\n",
      "sqrt(loss(X, Y)) = 0.048852713264023816\n",
      "sqrt(loss(X, Y)) = 0.04910623894684754\n",
      "sqrt(loss(X, Y)) = 0.048298916734411994\n",
      "sqrt(loss(X, Y)) = 0.048353718275109786\n",
      "sqrt(loss(X, Y)) = 0.048761601499467906\n",
      "sqrt(loss(X, Y)) = 0.049013952937890744\n",
      "sqrt(loss(X, Y)) = 0.04819800900290404\n",
      "sqrt(loss(X, Y)) = 0.04825376637092931\n",
      "sqrt(loss(X, Y)) = 0.048663530950302465\n",
      "sqrt(loss(X, Y)) = 0.048917372565013086\n",
      "sqrt(loss(X, Y)) = 0.048091536391590875\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: Epoch 1225\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1226\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1227\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1228\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1229\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1230\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1231\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1232\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1233\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1234\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1235\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1236\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1237\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1238\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1239\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1240\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1241\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1242\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1243\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1244\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1245\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1246\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1247\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1248\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1249\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1250\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1251\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1252\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1253\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1254\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1255\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1256\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1257\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1258\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1259\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1260\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1261\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1262\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1263\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1264\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1265\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1266\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1267\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1268\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1269\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1270\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1271\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1272\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1273\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1274\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1275\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1276\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1277\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1278\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sqrt(loss(X, Y)) = 0.04814805445638155\n",
      "sqrt(loss(X, Y)) = 0.048558119225384985\n",
      "sqrt(loss(X, Y)) = 0.048813663248726526\n",
      "sqrt(loss(X, Y)) = 0.04798945853053493\n",
      "sqrt(loss(X, Y)) = 0.048046848410694454\n",
      "sqrt(loss(X, Y)) = 0.04845897818235692\n",
      "sqrt(loss(X, Y)) = 0.048716319459557254\n",
      "sqrt(loss(X, Y)) = 0.0478898752064136\n",
      "sqrt(loss(X, Y)) = 0.047949256844468297\n",
      "sqrt(loss(X, Y)) = 0.04836189766846968\n",
      "sqrt(loss(X, Y)) = 0.0486208157552313\n",
      "sqrt(loss(X, Y)) = 0.04778817042416845\n",
      "sqrt(loss(X, Y)) = 0.0478486676325132\n",
      "sqrt(loss(X, Y)) = 0.048263553945521426\n",
      "sqrt(loss(X, Y)) = 0.04852410216854329\n",
      "sqrt(loss(X, Y)) = 0.0476824099383955\n",
      "sqrt(loss(X, Y)) = 0.04774407085287498\n",
      "sqrt(loss(X, Y)) = 0.04815994081697593\n",
      "sqrt(loss(X, Y)) = 0.048421949040528746\n",
      "sqrt(loss(X, Y)) = 0.04758227538328942\n",
      "sqrt(loss(X, Y)) = 0.04764470406907839\n",
      "sqrt(loss(X, Y)) = 0.04806084393083118\n",
      "sqrt(loss(X, Y)) = 0.04834701282682511\n",
      "sqrt(loss(X, Y)) = 0.04747712315860839\n",
      "sqrt(loss(X, Y)) = 0.04752531463841937\n",
      "sqrt(loss(X, Y)) = 0.0479433042382157\n",
      "sqrt(loss(X, Y)) = 0.048240215077395024\n",
      "sqrt(loss(X, Y)) = 0.04738395758664597\n",
      "sqrt(loss(X, Y)) = 0.04744681344727579\n",
      "sqrt(loss(X, Y)) = 0.04786471199782866\n",
      "sqrt(loss(X, Y)) = 0.04815446963607523\n",
      "sqrt(loss(X, Y)) = 0.04728557962213796\n",
      "sqrt(loss(X, Y)) = 0.04734895789710325\n",
      "sqrt(loss(X, Y)) = 0.047769424509105075\n",
      "sqrt(loss(X, Y)) = 0.048061153952962295\n",
      "sqrt(loss(X, Y)) = 0.04718957178820727\n",
      "sqrt(loss(X, Y)) = 0.04724815072963321\n",
      "sqrt(loss(X, Y)) = 0.047668544996375906\n",
      "sqrt(loss(X, Y)) = 0.04796803751276666\n",
      "sqrt(loss(X, Y)) = 0.04708662130970908\n",
      "sqrt(loss(X, Y)) = 0.047151366801267795\n",
      "sqrt(loss(X, Y)) = 0.047573873575161935\n",
      "sqrt(loss(X, Y)) = 0.0478756438796257\n",
      "sqrt(loss(X, Y)) = 0.046992454725863816\n",
      "sqrt(loss(X, Y)) = 0.04705226157903806\n",
      "sqrt(loss(X, Y)) = 0.04747613091404487\n",
      "sqrt(loss(X, Y)) = 0.04777916019721195\n",
      "sqrt(loss(X, Y)) = 0.0468919037064717\n",
      "sqrt(loss(X, Y)) = 0.04695215624280858\n",
      "sqrt(loss(X, Y)) = 0.04737504316636574\n",
      "sqrt(loss(X, Y)) = 0.04768644839421518\n",
      "sqrt(loss(X, Y)) = 0.046795802648687816\n",
      "sqrt(loss(X, Y)) = 0.04685707657839645\n",
      "sqrt(loss(X, Y)) = 0.04728288082964412\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: Epoch 1279\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1280\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1281\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1282\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1283\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1284\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1285\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1286\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1287\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1288\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1289\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1290\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1291\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1292\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1293\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1294\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1295\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1296\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1297\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1298\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1299\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1300\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1301\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1302\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1303\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1304\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1305\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1306\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1307\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1308\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1309\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1310\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1311\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1312\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1313\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1314\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1315\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1316\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1317\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1318\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1319\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1320\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1321\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1322\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1323\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1324\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1325\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1326\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1327\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1328\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1329\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1330\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1331\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sqrt(loss(X, Y)) = 0.0475893495961687\n",
      "sqrt(loss(X, Y)) = 0.046697401760678194\n",
      "sqrt(loss(X, Y)) = 0.046759967951846726\n",
      "sqrt(loss(X, Y)) = 0.04718651848189864\n",
      "sqrt(loss(X, Y)) = 0.04750103625706576\n",
      "sqrt(loss(X, Y)) = 0.046604660793347026\n",
      "sqrt(loss(X, Y)) = 0.04666786470279633\n",
      "sqrt(loss(X, Y)) = 0.04709361549374361\n",
      "sqrt(loss(X, Y)) = 0.04741006897936256\n",
      "sqrt(loss(X, Y)) = 0.046509692418256976\n",
      "sqrt(loss(X, Y)) = 0.04657794045192936\n",
      "sqrt(loss(X, Y)) = 0.04700495720322933\n",
      "sqrt(loss(X, Y)) = 0.04732336478376691\n",
      "sqrt(loss(X, Y)) = 0.046418936825555646\n",
      "sqrt(loss(X, Y)) = 0.046487678452472925\n",
      "sqrt(loss(X, Y)) = 0.046915997328973676\n",
      "sqrt(loss(X, Y)) = 0.047236640684172895\n",
      "sqrt(loss(X, Y)) = 0.04632629369420343\n",
      "sqrt(loss(X, Y)) = 0.0463955395600506\n",
      "sqrt(loss(X, Y)) = 0.04682468602218196\n",
      "sqrt(loss(X, Y)) = 0.04714659137412307\n",
      "sqrt(loss(X, Y)) = 0.046233326378381234\n",
      "sqrt(loss(X, Y)) = 0.04630298432055414\n",
      "sqrt(loss(X, Y)) = 0.04674046666199495\n",
      "sqrt(loss(X, Y)) = 0.047063949311390504\n",
      "sqrt(loss(X, Y)) = 0.0461473517416128\n",
      "sqrt(loss(X, Y)) = 0.046216921531135595\n",
      "sqrt(loss(X, Y)) = 0.04664708877738546\n",
      "sqrt(loss(X, Y)) = 0.04697171450224621\n",
      "sqrt(loss(X, Y)) = 0.046053764638366856\n",
      "sqrt(loss(X, Y)) = 0.046123755583613715\n",
      "sqrt(loss(X, Y)) = 0.04656264664736973\n",
      "sqrt(loss(X, Y)) = 0.04689623145178012\n",
      "sqrt(loss(X, Y)) = 0.04596965402407115\n",
      "sqrt(loss(X, Y)) = 0.046038967202239454\n",
      "sqrt(loss(X, Y)) = 0.046475797263988324\n",
      "sqrt(loss(X, Y)) = 0.04681078705541418\n",
      "sqrt(loss(X, Y)) = 0.04588268288655546\n",
      "sqrt(loss(X, Y)) = 0.04595294546845454\n",
      "sqrt(loss(X, Y)) = 0.046390506576807405\n",
      "sqrt(loss(X, Y)) = 0.046726411544119745\n",
      "sqrt(loss(X, Y)) = 0.045795169862619084\n",
      "sqrt(loss(X, Y)) = 0.04586567890672646\n",
      "sqrt(loss(X, Y)) = 0.0463067380478995\n",
      "sqrt(loss(X, Y)) = 0.046643655349915276\n",
      "sqrt(loss(X, Y)) = 0.04571087315169185\n",
      "sqrt(loss(X, Y)) = 0.04578120826796206\n",
      "sqrt(loss(X, Y)) = 0.04621984379947839\n",
      "sqrt(loss(X, Y)) = 0.046565064022821755\n",
      "sqrt(loss(X, Y)) = 0.04562691675247835\n",
      "sqrt(loss(X, Y)) = 0.045696363051992435\n",
      "sqrt(loss(X, Y)) = 0.046135206480992216\n",
      "sqrt(loss(X, Y)) = 0.0464817044851357\n",
      "sqrt(loss(X, Y)) = 0.04554292831082437\n",
      "sqrt(loss(X, Y)) = 0.04561219511136857\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: Epoch 1332\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1333\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1334\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1335\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1336\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1337\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1338\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1339\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1340\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1341\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1342\n",
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      "┌ Info: Epoch 1343\n",
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      "┌ Info: Epoch 1344\n",
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      "┌ Info: Epoch 1347\n",
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      "┌ Info: Epoch 1348\n",
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      "┌ Info: Epoch 1350\n",
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      "┌ Info: Epoch 1351\n",
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      "┌ Info: Epoch 1352\n",
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      "┌ Info: Epoch 1353\n",
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      "┌ Info: Epoch 1354\n",
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      "┌ Info: Epoch 1355\n",
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      "┌ Info: Epoch 1356\n",
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      "┌ Info: Epoch 1357\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1358\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1359\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1360\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1361\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1362\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1363\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1364\n",
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      "┌ Info: Epoch 1366\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1367\n",
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      "┌ Info: Epoch 1368\n",
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      "┌ Info: Epoch 1369\n",
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      "┌ Info: Epoch 1370\n",
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      "┌ Info: Epoch 1371\n",
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      "┌ Info: Epoch 1372\n",
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      "┌ Info: Epoch 1373\n",
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      "┌ Info: Epoch 1380\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1381\n",
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      "┌ Info: Epoch 1382\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1383\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1384\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1385\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1386\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1387\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sqrt(loss(X, Y)) = 0.046051236607216134\n",
      "sqrt(loss(X, Y)) = 0.04639879239079946\n",
      "sqrt(loss(X, Y)) = 0.04545896766866622\n",
      "sqrt(loss(X, Y)) = 0.045528104866242806\n",
      "sqrt(loss(X, Y)) = 0.04596730679617381\n",
      "sqrt(loss(X, Y)) = 0.04631583420442511\n",
      "sqrt(loss(X, Y)) = 0.045375130169175375\n",
      "sqrt(loss(X, Y)) = 0.04544419700346546\n",
      "sqrt(loss(X, Y)) = 0.04588350830641065\n",
      "sqrt(loss(X, Y)) = 0.04623294385886298\n",
      "sqrt(loss(X, Y)) = 0.045292310281611764\n",
      "sqrt(loss(X, Y)) = 0.04536191784270197\n",
      "sqrt(loss(X, Y)) = 0.04580137682878682\n",
      "sqrt(loss(X, Y)) = 0.04615144530609769\n",
      "sqrt(loss(X, Y)) = 0.04520884561310854\n",
      "sqrt(loss(X, Y)) = 0.045278312614791025\n",
      "sqrt(loss(X, Y)) = 0.04571781503902872\n",
      "sqrt(loss(X, Y)) = 0.04607902962257102\n",
      "sqrt(loss(X, Y)) = 0.04512851296046229\n",
      "sqrt(loss(X, Y)) = 0.04519719638298374\n",
      "sqrt(loss(X, Y)) = 0.045640111288030326\n",
      "sqrt(loss(X, Y)) = 0.04600175118277964\n",
      "sqrt(loss(X, Y)) = 0.045050734385597625\n",
      "sqrt(loss(X, Y)) = 0.04511916067570923\n",
      "sqrt(loss(X, Y)) = 0.04555847702819275\n",
      "sqrt(loss(X, Y)) = 0.04592191680619749\n",
      "sqrt(loss(X, Y)) = 0.04497007596430055\n",
      "sqrt(loss(X, Y)) = 0.04503826702960334\n",
      "sqrt(loss(X, Y)) = 0.04548114133074585\n",
      "sqrt(loss(X, Y)) = 0.04586916224161732\n",
      "sqrt(loss(X, Y)) = 0.044889569009509474\n",
      "sqrt(loss(X, Y)) = 0.04493818869479035\n",
      "sqrt(loss(X, Y)) = 0.04537913230665605\n",
      "sqrt(loss(X, Y)) = 0.04576941624131726\n",
      "sqrt(loss(X, Y)) = 0.044824856269742634\n",
      "sqrt(loss(X, Y)) = 0.04489265973847094\n",
      "sqrt(loss(X, Y)) = 0.04533069152616607\n",
      "sqrt(loss(X, Y)) = 0.045719087019054326\n",
      "sqrt(loss(X, Y)) = 0.04474438335701705\n",
      "sqrt(loss(X, Y)) = 0.04479244663546277\n",
      "sqrt(loss(X, Y)) = 0.04523226281991268\n",
      "sqrt(loss(X, Y)) = 0.045622757755803725\n",
      "sqrt(loss(X, Y)) = 0.04467403735998486\n",
      "sqrt(loss(X, Y)) = 0.04474060751680603\n",
      "sqrt(loss(X, Y)) = 0.04518160623494166\n",
      "sqrt(loss(X, Y)) = 0.04557044871905964\n",
      "sqrt(loss(X, Y)) = 0.04460077018188577\n",
      "sqrt(loss(X, Y)) = 0.044648782546611185\n",
      "sqrt(loss(X, Y)) = 0.045087551908867506\n",
      "sqrt(loss(X, Y)) = 0.04547812667084431\n",
      "sqrt(loss(X, Y)) = 0.04453048697739103\n",
      "sqrt(loss(X, Y)) = 0.044596394537363\n",
      "sqrt(loss(X, Y)) = 0.04503626195091836\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: Epoch 1388\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1389\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1390\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1391\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1392\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1393\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1394\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1395\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1396\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1397\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1398\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1399\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1400\n",
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      "┌ Info: Epoch 1401\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1402\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1403\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1404\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1405\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1406\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1407\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1408\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1409\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1410\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1411\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1412\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1413\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1414\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1415\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1416\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1417\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1418\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1419\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1420\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1421\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1422\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1423\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1424\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1425\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1426\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1427\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1428\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1429\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1430\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1431\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1432\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1433\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1434\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1435\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1436\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1437\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1438\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1439\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sqrt(loss(X, Y)) = 0.04542525861692786\n",
      "sqrt(loss(X, Y)) = 0.044458625206348065\n",
      "sqrt(loss(X, Y)) = 0.04450540149062215\n",
      "sqrt(loss(X, Y)) = 0.04494271781775466\n",
      "sqrt(loss(X, Y)) = 0.04533335608030489\n",
      "sqrt(loss(X, Y)) = 0.044385282744373915\n",
      "sqrt(loss(X, Y)) = 0.04443145222521684\n",
      "sqrt(loss(X, Y)) = 0.044874525768840656\n",
      "sqrt(loss(X, Y)) = 0.04526496021005321\n",
      "sqrt(loss(X, Y)) = 0.04432315687669486\n",
      "sqrt(loss(X, Y)) = 0.044386824286623265\n",
      "sqrt(loss(X, Y)) = 0.04482444947748188\n",
      "sqrt(loss(X, Y)) = 0.04521346351828097\n",
      "sqrt(loss(X, Y)) = 0.04425303054000368\n",
      "sqrt(loss(X, Y)) = 0.04429764506544611\n",
      "sqrt(loss(X, Y)) = 0.04473980394052576\n",
      "sqrt(loss(X, Y)) = 0.04513073086350401\n",
      "sqrt(loss(X, Y)) = 0.04418768731488912\n",
      "sqrt(loss(X, Y)) = 0.04423146483463834\n",
      "sqrt(loss(X, Y)) = 0.044665300081383384\n",
      "sqrt(loss(X, Y)) = 0.045055949338264276\n",
      "sqrt(loss(X, Y)) = 0.044117220868578416\n",
      "sqrt(loss(X, Y)) = 0.04415982832088146\n",
      "sqrt(loss(X, Y)) = 0.04459912703463898\n",
      "sqrt(loss(X, Y)) = 0.0449892620605481\n",
      "sqrt(loss(X, Y)) = 0.04404785183606255\n",
      "sqrt(loss(X, Y)) = 0.04408941377766285\n",
      "sqrt(loss(X, Y)) = 0.04452753311524001\n",
      "sqrt(loss(X, Y)) = 0.044917598429616006\n",
      "sqrt(loss(X, Y)) = 0.0439950056275733\n",
      "sqrt(loss(X, Y)) = 0.044055113911768105\n",
      "sqrt(loss(X, Y)) = 0.044495854273906094\n",
      "sqrt(loss(X, Y)) = 0.044884166643041325\n",
      "sqrt(loss(X, Y)) = 0.04392784688539682\n",
      "sqrt(loss(X, Y)) = 0.0439682710940808\n",
      "sqrt(loss(X, Y)) = 0.04440583233033127\n",
      "sqrt(loss(X, Y)) = 0.04479605536290492\n",
      "sqrt(loss(X, Y)) = 0.0438653404728144\n",
      "sqrt(loss(X, Y)) = 0.0439052350575733\n",
      "sqrt(loss(X, Y)) = 0.04434143674673538\n",
      "sqrt(loss(X, Y)) = 0.04473135168995293\n",
      "sqrt(loss(X, Y)) = 0.043804180481028285\n",
      "sqrt(loss(X, Y)) = 0.04384297485396137\n",
      "sqrt(loss(X, Y)) = 0.044277871764990144\n",
      "sqrt(loss(X, Y)) = 0.04466733701545638\n",
      "sqrt(loss(X, Y)) = 0.04374325569328709\n",
      "sqrt(loss(X, Y)) = 0.043780890725615945\n",
      "sqrt(loss(X, Y)) = 0.044214377999665266\n",
      "sqrt(loss(X, Y)) = 0.04460335854276623\n",
      "sqrt(loss(X, Y)) = 0.04368278429538048\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: Epoch 1440\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1441\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1442\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1443\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1444\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
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      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
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      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1447\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1448\n",
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      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1490\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sqrt(loss(X, Y)) = 0.04371936044764103\n",
      "sqrt(loss(X, Y)) = 0.04415142138922585\n",
      "sqrt(loss(X, Y)) = 0.04453985544920943\n",
      "sqrt(loss(X, Y)) = 0.0436227853358376\n",
      "sqrt(loss(X, Y)) = 0.04365831771239712\n",
      "sqrt(loss(X, Y)) = 0.04408889869008253\n",
      "sqrt(loss(X, Y)) = 0.04447675693783165\n",
      "sqrt(loss(X, Y)) = 0.04356329157372498\n",
      "sqrt(loss(X, Y)) = 0.043597778248556665\n",
      "sqrt(loss(X, Y)) = 0.0440268350796138\n",
      "sqrt(loss(X, Y)) = 0.04441407427529107\n",
      "sqrt(loss(X, Y)) = 0.043504347486024685\n",
      "sqrt(loss(X, Y)) = 0.04353778153716338\n",
      "sqrt(loss(X, Y)) = 0.04396526248182229\n",
      "sqrt(loss(X, Y)) = 0.04435188819980104\n",
      "sqrt(loss(X, Y)) = 0.043446000831241824\n",
      "sqrt(loss(X, Y)) = 0.043478376142957714\n",
      "sqrt(loss(X, Y)) = 0.04390423388443305\n",
      "sqrt(loss(X, Y)) = 0.04429034298300013\n",
      "sqrt(loss(X, Y)) = 0.04338832676934499\n",
      "sqrt(loss(X, Y)) = 0.04341964539892191\n",
      "sqrt(loss(X, Y)) = 0.0438438531477027\n",
      "sqrt(loss(X, Y)) = 0.0442296564439223\n",
      "sqrt(loss(X, Y)) = 0.043331397281477134\n",
      "sqrt(loss(X, Y)) = 0.04336166317351469\n",
      "sqrt(loss(X, Y)) = 0.043784186427786476\n",
      "sqrt(loss(X, Y)) = 0.04416929498762584\n",
      "sqrt(loss(X, Y)) = 0.04327513658494472\n",
      "sqrt(loss(X, Y)) = 0.04330432283924334\n",
      "sqrt(loss(X, Y)) = 0.0437251132406328\n",
      "sqrt(loss(X, Y)) = 0.044109473598411886\n",
      "sqrt(loss(X, Y)) = 0.04321957831615237\n",
      "sqrt(loss(X, Y)) = 0.04324766653408394\n",
      "sqrt(loss(X, Y)) = 0.04366666964903181\n",
      "sqrt(loss(X, Y)) = 0.044050254650607634\n",
      "sqrt(loss(X, Y)) = 0.043164746744704656\n",
      "sqrt(loss(X, Y)) = 0.04319172239744617\n",
      "sqrt(loss(X, Y)) = 0.043608902759967816\n",
      "sqrt(loss(X, Y)) = 0.04399168094984882\n",
      "sqrt(loss(X, Y)) = 0.043110661155153676\n",
      "sqrt(loss(X, Y)) = 0.04313650795417077\n",
      "sqrt(loss(X, Y)) = 0.04355182197583388\n",
      "sqrt(loss(X, Y)) = 0.04393376389270008\n",
      "sqrt(loss(X, Y)) = 0.04305734179966052\n",
      "sqrt(loss(X, Y)) = 0.04308203188671084\n",
      "sqrt(loss(X, Y)) = 0.043495444061292826\n",
      "sqrt(loss(X, Y)) = 0.04387652041946903\n",
      "sqrt(loss(X, Y)) = 0.04300421491451669\n",
      "sqrt(loss(X, Y)) = 0.04302518653285318\n",
      "sqrt(loss(X, Y)) = 0.04343515015982505\n",
      "sqrt(loss(X, Y)) = 0.04381588499599194\n",
      "sqrt(loss(X, Y)) = 0.042953874568525244\n",
      "sqrt(loss(X, Y)) = 0.04297597374565451\n",
      "sqrt(loss(X, Y)) = 0.043388062645346254\n",
      "sqrt(loss(X, Y)) = 0.043767500906620696\n",
      "sqrt(loss(X, Y)) = 0.042905625565855664\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: Epoch 1491\n",
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      "┌ Info: Epoch 1492\n",
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      "┌ Info: Epoch 1493\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1494\n",
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      "┌ Info: Epoch 1495\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1496\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1497\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1498\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1499\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1500\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1501\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1502\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1503\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1504\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1505\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1506\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1507\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1508\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1509\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1510\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1511\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1512\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1513\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1514\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1515\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1516\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1517\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1518\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1519\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1520\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1521\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1522\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1523\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1524\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1525\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1526\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1527\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1528\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1529\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1530\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1531\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1532\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1533\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1534\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1535\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1536\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1537\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1538\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1539\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1540\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1541\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1542\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1543\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1544\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1545\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sqrt(loss(X, Y)) = 0.042923805889210846\n",
      "sqrt(loss(X, Y)) = 0.04332876884992519\n",
      "sqrt(loss(X, Y)) = 0.04370752628301661\n",
      "sqrt(loss(X, Y)) = 0.042856815057996996\n",
      "sqrt(loss(X, Y)) = 0.04287642148337165\n",
      "sqrt(loss(X, Y)) = 0.0432811755362681\n",
      "sqrt(loss(X, Y)) = 0.043658461263678124\n",
      "sqrt(loss(X, Y)) = 0.04280761322574653\n",
      "sqrt(loss(X, Y)) = 0.042817704707917834\n",
      "sqrt(loss(X, Y)) = 0.043214088103314084\n",
      "sqrt(loss(X, Y)) = 0.04359195783065109\n",
      "sqrt(loss(X, Y)) = 0.04276161192968448\n",
      "sqrt(loss(X, Y)) = 0.04277809783769843\n",
      "sqrt(loss(X, Y)) = 0.04318091718485983\n",
      "sqrt(loss(X, Y)) = 0.043557624638492075\n",
      "sqrt(loss(X, Y)) = 0.04272336770516139\n",
      "sqrt(loss(X, Y)) = 0.042735720847943144\n",
      "sqrt(loss(X, Y)) = 0.04313466707623048\n",
      "sqrt(loss(X, Y)) = 0.04350969022079171\n",
      "sqrt(loss(X, Y)) = 0.04268029768893625\n",
      "sqrt(loss(X, Y)) = 0.042686071964547614\n",
      "sqrt(loss(X, Y)) = 0.04307483837703495\n",
      "sqrt(loss(X, Y)) = 0.043449316290362895\n",
      "sqrt(loss(X, Y)) = 0.04263529319613672\n",
      "sqrt(loss(X, Y)) = 0.04264767894978893\n",
      "sqrt(loss(X, Y)) = 0.04304361748052189\n",
      "sqrt(loss(X, Y)) = 0.043416646038759436\n",
      "sqrt(loss(X, Y)) = 0.04262341405357206\n",
      "sqrt(loss(X, Y)) = 0.042587889522013575\n",
      "sqrt(loss(X, Y)) = 0.04293076752656242\n",
      "sqrt(loss(X, Y)) = 0.04333446164045822\n",
      "sqrt(loss(X, Y)) = 0.04257066218210921\n",
      "sqrt(loss(X, Y)) = 0.042578114212063166\n",
      "sqrt(loss(X, Y)) = 0.04296224951713743\n",
      "sqrt(loss(X, Y)) = 0.043331168140475745\n",
      "sqrt(loss(X, Y)) = 0.04256287502386373\n",
      "sqrt(loss(X, Y)) = 0.04252322327874097\n",
      "sqrt(loss(X, Y)) = 0.042858898586314684\n",
      "sqrt(loss(X, Y)) = 0.04325964738161418\n",
      "sqrt(loss(X, Y)) = 0.04251664906734727\n",
      "sqrt(loss(X, Y)) = 0.04252990241629179\n",
      "sqrt(loss(X, Y)) = 0.042917877705657186\n",
      "sqrt(loss(X, Y)) = 0.04326035662245622\n",
      "sqrt(loss(X, Y)) = 0.0425092892614324\n",
      "sqrt(loss(X, Y)) = 0.042465785157916956\n",
      "sqrt(loss(X, Y)) = 0.04279221560885266\n",
      "sqrt(loss(X, Y)) = 0.04318865935027574\n",
      "sqrt(loss(X, Y)) = 0.04246114593280598\n",
      "sqrt(loss(X, Y)) = 0.04245678043613317\n",
      "sqrt(loss(X, Y)) = 0.04282625813080184\n",
      "sqrt(loss(X, Y)) = 0.04317672153912089\n",
      "sqrt(loss(X, Y)) = 0.042427038773345235\n",
      "sqrt(loss(X, Y)) = 0.04241442533553784\n",
      "sqrt(loss(X, Y)) = 0.0427746271374293\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: Epoch 1546\n",
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      "┌ Info: Epoch 1589\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1590\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1591\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1592\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1593\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1594\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1595\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1596\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1597\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1598\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1599\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1600\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sqrt(loss(X, Y)) = 0.04313701370850405\n",
      "sqrt(loss(X, Y)) = 0.04242736230610856\n",
      "sqrt(loss(X, Y)) = 0.042378827413586825\n",
      "sqrt(loss(X, Y)) = 0.042690447758308186\n",
      "sqrt(loss(X, Y)) = 0.04308141459852734\n",
      "sqrt(loss(X, Y)) = 0.04236972409079782\n",
      "sqrt(loss(X, Y)) = 0.04236146291914948\n",
      "sqrt(loss(X, Y)) = 0.04272218338715639\n",
      "sqrt(loss(X, Y)) = 0.04305874202363882\n",
      "sqrt(loss(X, Y)) = 0.04237290218872852\n",
      "sqrt(loss(X, Y)) = 0.042320449006631344\n",
      "sqrt(loss(X, Y)) = 0.042625180432571734\n",
      "sqrt(loss(X, Y)) = 0.04301183531282016\n",
      "sqrt(loss(X, Y)) = 0.04231890048773292\n",
      "sqrt(loss(X, Y)) = 0.04230342773728504\n",
      "sqrt(loss(X, Y)) = 0.04265220494963656\n",
      "sqrt(loss(X, Y)) = 0.04298560499279977\n",
      "sqrt(loss(X, Y)) = 0.04232569535138385\n",
      "sqrt(loss(X, Y)) = 0.04226958740608367\n",
      "sqrt(loss(X, Y)) = 0.042561207844598076\n",
      "sqrt(loss(X, Y)) = 0.042943144690324495\n",
      "sqrt(loss(X, Y)) = 0.04231113013750925\n",
      "sqrt(loss(X, Y)) = 0.04225332221082055\n",
      "sqrt(loss(X, Y)) = 0.04254118049783529\n",
      "sqrt(loss(X, Y)) = 0.04289303981136009\n",
      "sqrt(loss(X, Y)) = 0.042241022685477095\n",
      "sqrt(loss(X, Y)) = 0.04222190811471436\n",
      "sqrt(loss(X, Y)) = 0.04256085687941175\n",
      "sqrt(loss(X, Y)) = 0.042887015597528104\n",
      "sqrt(loss(X, Y)) = 0.04225631262501057\n",
      "sqrt(loss(X, Y)) = 0.04219530265764786\n",
      "sqrt(loss(X, Y)) = 0.04247340272432325\n",
      "sqrt(loss(X, Y)) = 0.04284837831543784\n",
      "sqrt(loss(X, Y)) = 0.04224545173976952\n",
      "sqrt(loss(X, Y)) = 0.042182680264164364\n",
      "sqrt(loss(X, Y)) = 0.04245422176456818\n",
      "sqrt(loss(X, Y)) = 0.042798319298925386\n",
      "sqrt(loss(X, Y)) = 0.04217042419870332\n",
      "sqrt(loss(X, Y)) = 0.04214262903878006\n",
      "sqrt(loss(X, Y)) = 0.042470808239883286\n",
      "sqrt(loss(X, Y)) = 0.04278956685383706\n",
      "sqrt(loss(X, Y)) = 0.04218666520450656\n",
      "sqrt(loss(X, Y)) = 0.042122097043735186\n",
      "sqrt(loss(X, Y)) = 0.04238409731275872\n",
      "sqrt(loss(X, Y)) = 0.042724561200873375\n",
      "sqrt(loss(X, Y)) = 0.04212088111317718\n",
      "sqrt(loss(X, Y)) = 0.04208193773582902\n",
      "sqrt(loss(X, Y)) = 0.04239109473159742\n",
      "sqrt(loss(X, Y)) = 0.04270736841022853\n",
      "sqrt(loss(X, Y)) = 0.04213736138483632\n",
      "sqrt(loss(X, Y)) = 0.042068865843583436\n",
      "sqrt(loss(X, Y)) = 0.04231960015000229\n",
      "sqrt(loss(X, Y)) = 0.04265593466139443\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: Epoch 1601\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1602\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1603\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1604\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1605\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1606\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1607\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1608\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1609\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1610\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1611\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1612\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1613\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1614\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1615\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1616\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1617\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1618\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1619\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1620\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1621\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1622\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1623\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1624\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1625\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1626\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1627\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1628\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1629\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1630\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1631\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1632\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1633\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1634\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1635\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1636\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1637\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1638\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1639\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1640\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1641\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1642\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1643\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1644\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1645\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1646\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1647\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1648\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1649\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1650\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1651\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1652\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1653\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sqrt(loss(X, Y)) = 0.042121840406535584\n",
      "sqrt(loss(X, Y)) = 0.042050452791455376\n",
      "sqrt(loss(X, Y)) = 0.04229641546401086\n",
      "sqrt(loss(X, Y)) = 0.04262259377964657\n",
      "sqrt(loss(X, Y)) = 0.04210637460103086\n",
      "sqrt(loss(X, Y)) = 0.0420324833828294\n",
      "sqrt(loss(X, Y)) = 0.04227189693394646\n",
      "sqrt(loss(X, Y)) = 0.04259543132661713\n",
      "sqrt(loss(X, Y)) = 0.04208759128990075\n",
      "sqrt(loss(X, Y)) = 0.042012565666880615\n",
      "sqrt(loss(X, Y)) = 0.04224660566074407\n",
      "sqrt(loss(X, Y)) = 0.04256818579504204\n",
      "sqrt(loss(X, Y)) = 0.042071506608272025\n",
      "sqrt(loss(X, Y)) = 0.04199560380272357\n",
      "sqrt(loss(X, Y)) = 0.04222452333335994\n",
      "sqrt(loss(X, Y)) = 0.04254325961931922\n",
      "sqrt(loss(X, Y)) = 0.04205731393339424\n",
      "sqrt(loss(X, Y)) = 0.04197913654669201\n",
      "sqrt(loss(X, Y)) = 0.042199421215681425\n",
      "sqrt(loss(X, Y)) = 0.0425161952348304\n",
      "sqrt(loss(X, Y)) = 0.042041417030105915\n",
      "sqrt(loss(X, Y)) = 0.04196212609956969\n",
      "sqrt(loss(X, Y)) = 0.04217702173508573\n",
      "sqrt(loss(X, Y)) = 0.04249152579299464\n",
      "sqrt(loss(X, Y)) = 0.042025728870617673\n",
      "sqrt(loss(X, Y)) = 0.041945696545506575\n",
      "sqrt(loss(X, Y)) = 0.04215542885797492\n",
      "sqrt(loss(X, Y)) = 0.04246757619828255\n",
      "sqrt(loss(X, Y)) = 0.04201080851714338\n",
      "sqrt(loss(X, Y)) = 0.04192998470227449\n",
      "sqrt(loss(X, Y)) = 0.04213428679206368\n",
      "sqrt(loss(X, Y)) = 0.0424440250616572\n",
      "sqrt(loss(X, Y)) = 0.04199626708444344\n",
      "sqrt(loss(X, Y)) = 0.041920199534328094\n",
      "sqrt(loss(X, Y)) = 0.04211916899061607\n",
      "sqrt(loss(X, Y)) = 0.04242722361721396\n",
      "sqrt(loss(X, Y)) = 0.04199850863230616\n",
      "sqrt(loss(X, Y)) = 0.04191245724106912\n",
      "sqrt(loss(X, Y)) = 0.042096416979894115\n",
      "sqrt(loss(X, Y)) = 0.042401570915166095\n",
      "sqrt(loss(X, Y)) = 0.041980969284447715\n",
      "sqrt(loss(X, Y)) = 0.04190159271962816\n",
      "sqrt(loss(X, Y)) = 0.04208541745708406\n",
      "sqrt(loss(X, Y)) = 0.04237859496682475\n",
      "sqrt(loss(X, Y)) = 0.04197296528438473\n",
      "sqrt(loss(X, Y)) = 0.04188505043345222\n",
      "sqrt(loss(X, Y)) = 0.042062943877191936\n",
      "sqrt(loss(X, Y)) = 0.04235171582627803\n",
      "sqrt(loss(X, Y)) = 0.04195579217356984\n",
      "sqrt(loss(X, Y)) = 0.041873020190440945\n",
      "sqrt(loss(X, Y)) = 0.04204548900016109\n",
      "sqrt(loss(X, Y)) = 0.04233465758552176\n",
      "sqrt(loss(X, Y)) = 0.0419530061129811\n",
      "sqrt(loss(X, Y)) = 0.04186749889077161\n",
      "sqrt(loss(X, Y)) = 0.042028844180944575\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: Epoch 1654\n",
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      "┌ Info: Epoch 1655\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
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      "┌ Info: Epoch 1683\n",
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      "┌ Info: Epoch 1684\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1685\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1686\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1687\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1688\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1689\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1690\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1691\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1692\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1693\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1694\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1695\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1696\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1697\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1698\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1699\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1700\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1701\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1702\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1703\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1704\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1705\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1706\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1707\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1708\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sqrt(loss(X, Y)) = 0.04231581389052901\n",
      "sqrt(loss(X, Y)) = 0.04195265809555066\n",
      "sqrt(loss(X, Y)) = 0.04186409273707833\n",
      "sqrt(loss(X, Y)) = 0.04201324082845867\n",
      "sqrt(loss(X, Y)) = 0.04229546646208071\n",
      "sqrt(loss(X, Y)) = 0.041944876542512395\n",
      "sqrt(loss(X, Y)) = 0.04184886516842464\n",
      "sqrt(loss(X, Y)) = 0.04199226818204511\n",
      "sqrt(loss(X, Y)) = 0.04227128160934337\n",
      "sqrt(loss(X, Y)) = 0.041929719597029944\n",
      "sqrt(loss(X, Y)) = 0.041839330139703125\n",
      "sqrt(loss(X, Y)) = 0.041977446682601095\n",
      "sqrt(loss(X, Y)) = 0.04225519806737223\n",
      "sqrt(loss(X, Y)) = 0.041921300955491916\n",
      "sqrt(loss(X, Y)) = 0.04183060819966232\n",
      "sqrt(loss(X, Y)) = 0.041963894467217565\n",
      "sqrt(loss(X, Y)) = 0.04223886678133887\n",
      "sqrt(loss(X, Y)) = 0.041913118177250096\n",
      "sqrt(loss(X, Y)) = 0.04182192035274899\n",
      "sqrt(loss(X, Y)) = 0.04195014441580531\n",
      "sqrt(loss(X, Y)) = 0.042222846181566874\n",
      "sqrt(loss(X, Y)) = 0.041904921099601804\n",
      "sqrt(loss(X, Y)) = 0.04181332566971781\n",
      "sqrt(loss(X, Y)) = 0.04193645234292839\n",
      "sqrt(loss(X, Y)) = 0.0422029957965702\n",
      "sqrt(loss(X, Y)) = 0.04189916672773589\n",
      "sqrt(loss(X, Y)) = 0.04180582664458302\n",
      "sqrt(loss(X, Y)) = 0.041922347836416934\n",
      "sqrt(loss(X, Y)) = 0.042186659343402315\n",
      "sqrt(loss(X, Y)) = 0.0418916158542571\n",
      "sqrt(loss(X, Y)) = 0.041797704322236486\n",
      "sqrt(loss(X, Y)) = 0.04190851296252884\n",
      "sqrt(loss(X, Y)) = 0.04217050138956116\n",
      "sqrt(loss(X, Y)) = 0.04188386019525161\n",
      "sqrt(loss(X, Y)) = 0.04178423568697085\n",
      "sqrt(loss(X, Y)) = 0.041889316699475904\n",
      "sqrt(loss(X, Y)) = 0.04214835179005442\n",
      "sqrt(loss(X, Y)) = 0.04187055783462691\n",
      "sqrt(loss(X, Y)) = 0.04177644396910219\n",
      "sqrt(loss(X, Y)) = 0.041875465315406\n",
      "sqrt(loss(X, Y)) = 0.042132755476330004\n",
      "sqrt(loss(X, Y)) = 0.04186333791878419\n",
      "sqrt(loss(X, Y)) = 0.04176909596388079\n",
      "sqrt(loss(X, Y)) = 0.04186249223374294\n",
      "sqrt(loss(X, Y)) = 0.04211746537546908\n",
      "sqrt(loss(X, Y)) = 0.04185649627306535\n",
      "sqrt(loss(X, Y)) = 0.04176209156870251\n",
      "sqrt(loss(X, Y)) = 0.041849648839154895\n",
      "sqrt(loss(X, Y)) = 0.04207990998332392\n",
      "sqrt(loss(X, Y)) = 0.041858802371970934\n",
      "sqrt(loss(X, Y)) = 0.041756204611674334\n",
      "sqrt(loss(X, Y)) = 0.04183148922360417\n",
      "sqrt(loss(X, Y)) = 0.042059577754575826\n",
      "sqrt(loss(X, Y)) = 0.0418498818739221\n",
      "sqrt(loss(X, Y)) = 0.041746655891095434\n",
      "sqrt(loss(X, Y)) = 0.041815215572491155\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: Epoch 1709\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1710\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1711\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1712\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1713\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1714\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1715\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1716\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1717\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1718\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1719\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1720\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1721\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1722\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1723\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1724\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1725\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1726\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1727\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1728\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1729\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1730\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1731\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1732\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1733\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1734\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1735\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1736\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1737\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1738\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1739\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1740\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1741\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1742\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1743\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1744\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1745\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1746\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1747\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1748\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1749\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1750\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1751\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1752\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1753\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1754\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1755\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1756\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1757\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1758\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1759\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1760\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1761\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1762\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1763\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sqrt(loss(X, Y)) = 0.04203307349329214\n",
      "sqrt(loss(X, Y)) = 0.04184654384628621\n",
      "sqrt(loss(X, Y)) = 0.04173968104053496\n",
      "sqrt(loss(X, Y)) = 0.04179871563642831\n",
      "sqrt(loss(X, Y)) = 0.04201414377409496\n",
      "sqrt(loss(X, Y)) = 0.04183882580954561\n",
      "sqrt(loss(X, Y)) = 0.04173176863978103\n",
      "sqrt(loss(X, Y)) = 0.041783067739776594\n",
      "sqrt(loss(X, Y)) = 0.041995959623134585\n",
      "sqrt(loss(X, Y)) = 0.04183092453791972\n",
      "sqrt(loss(X, Y)) = 0.04172392683005857\n",
      "sqrt(loss(X, Y)) = 0.041767696736178125\n",
      "sqrt(loss(X, Y)) = 0.04197801854536686\n",
      "sqrt(loss(X, Y)) = 0.04182315512442084\n",
      "sqrt(loss(X, Y)) = 0.04171630149743907\n",
      "sqrt(loss(X, Y)) = 0.04175250747767398\n",
      "sqrt(loss(X, Y)) = 0.04193316596261134\n",
      "sqrt(loss(X, Y)) = 0.04180743232346394\n",
      "sqrt(loss(X, Y)) = 0.041685562366252364\n",
      "sqrt(loss(X, Y)) = 0.041729607981631014\n",
      "sqrt(loss(X, Y)) = 0.04186579865609483\n",
      "sqrt(loss(X, Y)) = 0.04161522885855073\n",
      "sqrt(loss(X, Y)) = 0.04154651870434907\n",
      "sqrt(loss(X, Y)) = 0.04174542087408529\n",
      "sqrt(loss(X, Y)) = 0.041553475341668596\n",
      "sqrt(loss(X, Y)) = 0.0416354321765456\n",
      "sqrt(loss(X, Y)) = 0.041882218986992834\n",
      "sqrt(loss(X, Y)) = 0.04175759651213375\n",
      "sqrt(loss(X, Y)) = 0.041644307140815365\n",
      "sqrt(loss(X, Y)) = 0.04165764175630313\n",
      "sqrt(loss(X, Y)) = 0.04181054094723429\n",
      "sqrt(loss(X, Y)) = 0.04162026686026174\n",
      "sqrt(loss(X, Y)) = 0.04151854171246961\n",
      "sqrt(loss(X, Y)) = 0.041651454637101244\n",
      "sqrt(loss(X, Y)) = 0.041630348624490346\n",
      "sqrt(loss(X, Y)) = 0.04180987403903985\n",
      "sqrt(loss(X, Y)) = 0.04171548055470873\n",
      "sqrt(loss(X, Y)) = 0.04160252178856784\n",
      "sqrt(loss(X, Y)) = 0.04159800048698149\n",
      "sqrt(loss(X, Y)) = 0.04170569411781594\n",
      "sqrt(loss(X, Y)) = 0.041438023346826176\n",
      "sqrt(loss(X, Y)) = 0.04146525774275043\n",
      "sqrt(loss(X, Y)) = 0.041734329896118416\n",
      "sqrt(loss(X, Y)) = 0.04171487684039514\n",
      "sqrt(loss(X, Y)) = 0.04158510356447385\n",
      "sqrt(loss(X, Y)) = 0.04155891003165817\n",
      "sqrt(loss(X, Y)) = 0.041663540321901206\n",
      "sqrt(loss(X, Y)) = 0.04140163758330471\n",
      "sqrt(loss(X, Y)) = 0.04141492214120413\n",
      "sqrt(loss(X, Y)) = 0.041671673985484\n",
      "sqrt(loss(X, Y)) = 0.04168795742677767\n",
      "sqrt(loss(X, Y)) = 0.04155312499521422\n",
      "sqrt(loss(X, Y)) = 0.0415097762233304\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: Epoch 1764\n",
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      "┌ Info: Epoch 1765\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1766\n",
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      "┌ Info: Epoch 1767\n",
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      "┌ Info: Epoch 1772\n",
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      "┌ Info: Epoch 1775\n",
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      "┌ Info: Epoch 1776\n",
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      "┌ Info: Epoch 1777\n",
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      "┌ Info: Epoch 1778\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1779\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1780\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1781\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1782\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1783\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1784\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1785\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1786\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1787\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1788\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1789\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1790\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1791\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1792\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1793\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1794\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1795\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1796\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1797\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1798\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1799\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1800\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1801\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1802\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1803\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1804\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1805\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1806\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1807\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1808\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1809\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1810\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1811\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1812\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1813\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1814\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1815\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1816\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sqrt(loss(X, Y)) = 0.0416115851367606\n",
      "sqrt(loss(X, Y)) = 0.04135800618538159\n",
      "sqrt(loss(X, Y)) = 0.0413611716347235\n",
      "sqrt(loss(X, Y)) = 0.0416149409573395\n",
      "sqrt(loss(X, Y)) = 0.04166241866346306\n",
      "sqrt(loss(X, Y)) = 0.0415226923499024\n",
      "sqrt(loss(X, Y)) = 0.041461716108334205\n",
      "sqrt(loss(X, Y)) = 0.04156152623893963\n",
      "sqrt(loss(X, Y)) = 0.0413960295082944\n",
      "sqrt(loss(X, Y)) = 0.041312791764738524\n",
      "sqrt(loss(X, Y)) = 0.041488072471730926\n",
      "sqrt(loss(X, Y)) = 0.041433467618304264\n",
      "sqrt(loss(X, Y)) = 0.041322841299374954\n",
      "sqrt(loss(X, Y)) = 0.04143483226091188\n",
      "sqrt(loss(X, Y)) = 0.04136483072975081\n",
      "sqrt(loss(X, Y)) = 0.04127793634776695\n",
      "sqrt(loss(X, Y)) = 0.041433558277910135\n",
      "sqrt(loss(X, Y)) = 0.04138791086848696\n",
      "sqrt(loss(X, Y)) = 0.0412808948296782\n",
      "sqrt(loss(X, Y)) = 0.04139713447324029\n",
      "sqrt(loss(X, Y)) = 0.04136780538625479\n",
      "sqrt(loss(X, Y)) = 0.041264882791357003\n",
      "sqrt(loss(X, Y)) = 0.04137983813594946\n",
      "sqrt(loss(X, Y)) = 0.04136455497208228\n",
      "sqrt(loss(X, Y)) = 0.04125776730141934\n",
      "sqrt(loss(X, Y)) = 0.04136650385600628\n",
      "sqrt(loss(X, Y)) = 0.041359494022950534\n",
      "sqrt(loss(X, Y)) = 0.04124645308622988\n",
      "sqrt(loss(X, Y)) = 0.04134914180203207\n",
      "sqrt(loss(X, Y)) = 0.041384872647629925\n",
      "sqrt(loss(X, Y)) = 0.04125873339915557\n",
      "sqrt(loss(X, Y)) = 0.04132402922399704\n",
      "sqrt(loss(X, Y)) = 0.041350462481316165\n",
      "sqrt(loss(X, Y)) = 0.041239419074527335\n",
      "sqrt(loss(X, Y)) = 0.041322923253035854\n",
      "sqrt(loss(X, Y)) = 0.04140001318751553\n",
      "sqrt(loss(X, Y)) = 0.04126366338869457\n",
      "sqrt(loss(X, Y)) = 0.04129829785951203\n",
      "sqrt(loss(X, Y)) = 0.041352367743508325\n",
      "sqrt(loss(X, Y)) = 0.04123298444871391\n",
      "sqrt(loss(X, Y)) = 0.0412906141915602\n",
      "sqrt(loss(X, Y)) = 0.04141444868347123\n",
      "sqrt(loss(X, Y)) = 0.04126934201041212\n",
      "sqrt(loss(X, Y)) = 0.04125929488413111\n",
      "sqrt(loss(X, Y)) = 0.04134031014487126\n",
      "sqrt(loss(X, Y)) = 0.041216219846834885\n",
      "sqrt(loss(X, Y)) = 0.0412634013021831\n",
      "sqrt(loss(X, Y)) = 0.0414124224672058\n",
      "sqrt(loss(X, Y)) = 0.041262919383345975\n",
      "sqrt(loss(X, Y)) = 0.04123665111433779\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: Epoch 1817\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1818\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1819\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1820\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1821\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1822\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1823\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1824\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1825\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1826\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1827\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1828\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1829\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1830\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1831\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1832\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1833\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1834\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1835\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1836\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1837\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1838\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1839\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1840\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1841\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1842\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1843\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1844\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1845\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1846\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1847\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1848\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1849\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1850\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1851\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1852\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1853\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1854\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1855\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1856\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1857\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1858\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1859\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1860\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1861\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1862\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1863\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1864\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1865\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1866\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1867\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sqrt(loss(X, Y)) = 0.04141680373990758\n",
      "sqrt(loss(X, Y)) = 0.04126040432255316\n",
      "sqrt(loss(X, Y)) = 0.041218300908978094\n",
      "sqrt(loss(X, Y)) = 0.0414105048731609\n",
      "sqrt(loss(X, Y)) = 0.04125739735634456\n",
      "sqrt(loss(X, Y)) = 0.04120635614678115\n",
      "sqrt(loss(X, Y)) = 0.041420402026570595\n",
      "sqrt(loss(X, Y)) = 0.0412643767952399\n",
      "sqrt(loss(X, Y)) = 0.041194504363087095\n",
      "sqrt(loss(X, Y)) = 0.04144453291490406\n",
      "sqrt(loss(X, Y)) = 0.041282556697938476\n",
      "sqrt(loss(X, Y)) = 0.041182161474027014\n",
      "sqrt(loss(X, Y)) = 0.04144674567710639\n",
      "sqrt(loss(X, Y)) = 0.04128292270345811\n",
      "sqrt(loss(X, Y)) = 0.0411729894288231\n",
      "sqrt(loss(X, Y)) = 0.041594127305702826\n",
      "sqrt(loss(X, Y)) = 0.04140705391466472\n",
      "sqrt(loss(X, Y)) = 0.04114444694160421\n",
      "sqrt(loss(X, Y)) = 0.0414022693648671\n",
      "sqrt(loss(X, Y)) = 0.04123725455053059\n",
      "sqrt(loss(X, Y)) = 0.041158672434524576\n",
      "sqrt(loss(X, Y)) = 0.04164404379914527\n",
      "sqrt(loss(X, Y)) = 0.041464126303303865\n",
      "sqrt(loss(X, Y)) = 0.041113410682566284\n",
      "sqrt(loss(X, Y)) = 0.04132975896768997\n",
      "sqrt(loss(X, Y)) = 0.0411830028124755\n",
      "sqrt(loss(X, Y)) = 0.0411480906409556\n",
      "sqrt(loss(X, Y)) = 0.04163735645420895\n",
      "sqrt(loss(X, Y)) = 0.04147407212301154\n",
      "sqrt(loss(X, Y)) = 0.041097128104338757\n",
      "sqrt(loss(X, Y)) = 0.04141702498813553\n",
      "sqrt(loss(X, Y)) = 0.04124316587591154\n",
      "sqrt(loss(X, Y)) = 0.04112054412585392\n",
      "sqrt(loss(X, Y)) = 0.0416483645280765\n",
      "sqrt(loss(X, Y)) = 0.041492238684666456\n",
      "sqrt(loss(X, Y)) = 0.0410902387205892\n",
      "sqrt(loss(X, Y)) = 0.04143522801420785\n",
      "sqrt(loss(X, Y)) = 0.041261611627418975\n",
      "sqrt(loss(X, Y)) = 0.041112385507273926\n",
      "sqrt(loss(X, Y)) = 0.041661627059891085\n",
      "sqrt(loss(X, Y)) = 0.041515274298144396\n",
      "sqrt(loss(X, Y)) = 0.04108290154477941\n",
      "sqrt(loss(X, Y)) = 0.041487216298597245\n",
      "sqrt(loss(X, Y)) = 0.04130987035522834\n",
      "sqrt(loss(X, Y)) = 0.04110224101685605\n",
      "sqrt(loss(X, Y)) = 0.041701958683245006\n",
      "sqrt(loss(X, Y)) = 0.0415451826677899\n",
      "sqrt(loss(X, Y)) = 0.04107347947595384\n",
      "sqrt(loss(X, Y)) = 0.04171567250325885\n",
      "sqrt(loss(X, Y)) = 0.04150716549729889\n",
      "sqrt(loss(X, Y)) = 0.04107210706126916\n",
      "sqrt(loss(X, Y)) = 0.04171925757550277\n",
      "sqrt(loss(X, Y)) = 0.04152864865149044\n",
      "sqrt(loss(X, Y)) = 0.04106284673285287\n",
      "sqrt(loss(X, Y)) = 0.04172539668792126\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: Epoch 1868\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1869\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1870\n",
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      "┌ Info: Epoch 1871\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1872\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1873\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1874\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1875\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1876\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1877\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1878\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1879\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1880\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1881\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1882\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1883\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1884\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1885\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1886\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1887\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1888\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1889\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1890\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1891\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1892\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1893\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1894\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1895\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1896\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1897\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1898\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1899\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1900\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1901\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1902\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1903\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1904\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1905\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1906\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1907\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1908\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1909\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1910\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1911\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1912\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1913\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1914\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1915\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1916\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1917\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1918\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1919\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1920\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1921\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1922\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sqrt(loss(X, Y)) = 0.04153815258617949\n",
      "sqrt(loss(X, Y)) = 0.04106136861191919\n",
      "sqrt(loss(X, Y)) = 0.04172302542760522\n",
      "sqrt(loss(X, Y)) = 0.04153742211981898\n",
      "sqrt(loss(X, Y)) = 0.041056534435279596\n",
      "sqrt(loss(X, Y)) = 0.041725818437512045\n",
      "sqrt(loss(X, Y)) = 0.04154780551155174\n",
      "sqrt(loss(X, Y)) = 0.04105193299425098\n",
      "sqrt(loss(X, Y)) = 0.04172915113337554\n",
      "sqrt(loss(X, Y)) = 0.041557540018006524\n",
      "sqrt(loss(X, Y)) = 0.04104756559896027\n",
      "sqrt(loss(X, Y)) = 0.04173243707730151\n",
      "sqrt(loss(X, Y)) = 0.041567192027575216\n",
      "sqrt(loss(X, Y)) = 0.04104203713481321\n",
      "sqrt(loss(X, Y)) = 0.041741184973174805\n",
      "sqrt(loss(X, Y)) = 0.04158085089290045\n",
      "sqrt(loss(X, Y)) = 0.04104406558314235\n",
      "sqrt(loss(X, Y)) = 0.04174888584261694\n",
      "sqrt(loss(X, Y)) = 0.04159013662548784\n",
      "sqrt(loss(X, Y)) = 0.041042794713418124\n",
      "sqrt(loss(X, Y)) = 0.0417545665365392\n",
      "sqrt(loss(X, Y)) = 0.04160354837937584\n",
      "sqrt(loss(X, Y)) = 0.04104141081077375\n",
      "sqrt(loss(X, Y)) = 0.04179100217091737\n",
      "sqrt(loss(X, Y)) = 0.041651812848868014\n",
      "sqrt(loss(X, Y)) = 0.04104379818183549\n",
      "sqrt(loss(X, Y)) = 0.04180119929087662\n",
      "sqrt(loss(X, Y)) = 0.04166295136008874\n",
      "sqrt(loss(X, Y)) = 0.041045536824302474\n",
      "sqrt(loss(X, Y)) = 0.041815118196588535\n",
      "sqrt(loss(X, Y)) = 0.04168833843168917\n",
      "sqrt(loss(X, Y)) = 0.04104663051916203\n",
      "sqrt(loss(X, Y)) = 0.04182824929059015\n",
      "sqrt(loss(X, Y)) = 0.04170874493898852\n",
      "sqrt(loss(X, Y)) = 0.04104898135347258\n",
      "sqrt(loss(X, Y)) = 0.041841762479708056\n",
      "sqrt(loss(X, Y)) = 0.04172996703741983\n",
      "sqrt(loss(X, Y)) = 0.041051990326698595\n",
      "sqrt(loss(X, Y)) = 0.04185345433004346\n",
      "sqrt(loss(X, Y)) = 0.041749301775581435\n",
      "sqrt(loss(X, Y)) = 0.04105470770635964\n",
      "sqrt(loss(X, Y)) = 0.04187104700992959\n",
      "sqrt(loss(X, Y)) = 0.0417753186245703\n",
      "sqrt(loss(X, Y)) = 0.04105912635850995\n",
      "sqrt(loss(X, Y)) = 0.041887337387289544\n",
      "sqrt(loss(X, Y)) = 0.041796717705604385\n",
      "sqrt(loss(X, Y)) = 0.04106383825390236\n",
      "sqrt(loss(X, Y)) = 0.04190143672484997\n",
      "sqrt(loss(X, Y)) = 0.04181688211167568\n",
      "sqrt(loss(X, Y)) = 0.0410733019339277\n",
      "sqrt(loss(X, Y)) = 0.04191640956750189\n",
      "sqrt(loss(X, Y)) = 0.04183546692187021\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: Epoch 1923\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1924\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1925\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1926\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1927\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1928\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1929\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1930\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1931\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1932\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1933\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1934\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1935\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1936\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1937\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1938\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1939\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1940\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1941\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1942\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1943\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1944\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1945\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1946\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1947\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1948\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1949\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1950\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1951\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1952\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1953\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1954\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1955\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1956\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1957\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1958\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1959\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1960\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1961\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1962\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1963\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1964\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1965\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1966\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1967\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1968\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1969\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1970\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1971\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1972\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1973\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1974\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sqrt(loss(X, Y)) = 0.04107764370679797\n",
      "sqrt(loss(X, Y)) = 0.04192907808483737\n",
      "sqrt(loss(X, Y)) = 0.041851408598171984\n",
      "sqrt(loss(X, Y)) = 0.04108216863023621\n",
      "sqrt(loss(X, Y)) = 0.04194123278520836\n",
      "sqrt(loss(X, Y)) = 0.0418727187369867\n",
      "sqrt(loss(X, Y)) = 0.04108734481841496\n",
      "sqrt(loss(X, Y)) = 0.04195192317202448\n",
      "sqrt(loss(X, Y)) = 0.0418845598965706\n",
      "sqrt(loss(X, Y)) = 0.04109620708988281\n",
      "sqrt(loss(X, Y)) = 0.04196879620600959\n",
      "sqrt(loss(X, Y)) = 0.04189395295556275\n",
      "sqrt(loss(X, Y)) = 0.04110400699815907\n",
      "sqrt(loss(X, Y)) = 0.04197888761507874\n",
      "sqrt(loss(X, Y)) = 0.041902150721519094\n",
      "sqrt(loss(X, Y)) = 0.041098093412329686\n",
      "sqrt(loss(X, Y)) = 0.04198032275408891\n",
      "sqrt(loss(X, Y)) = 0.04190551292488729\n",
      "sqrt(loss(X, Y)) = 0.0411104056262256\n",
      "sqrt(loss(X, Y)) = 0.04199217406409856\n",
      "sqrt(loss(X, Y)) = 0.04191435249210557\n",
      "sqrt(loss(X, Y)) = 0.04111237717221389\n",
      "sqrt(loss(X, Y)) = 0.04199891620648928\n",
      "sqrt(loss(X, Y)) = 0.04190803240815025\n",
      "sqrt(loss(X, Y)) = 0.04110738649287865\n",
      "sqrt(loss(X, Y)) = 0.04199674262451893\n",
      "sqrt(loss(X, Y)) = 0.04190444530383574\n",
      "  4.916561 seconds (3.50 M allocations: 1.469 GiB, 14.88% gc time, 8.75% compilation time)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: Epoch 1975\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1976\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1977\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1978\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1979\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1980\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1981\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1982\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1983\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1984\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1985\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1986\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1987\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1988\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1989\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1990\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1991\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1992\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1993\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1994\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1995\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1996\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1997\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1998\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 1999\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n",
      "┌ Info: Epoch 2000\n",
      "└ @ Main /home/abarth/.julia/packages/Flux/qp1gc/src/optimise/train.jl:135\n"
     ]
    }
   ],
   "source": [
    "#η = 0.001 # learning rate\n",
    "\n",
    "@time Flux.@epochs 2000 Flux.train!(loss, Flux.params(m), dataset, ADAM(), cb =  throttle(evalcb,100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "extrema(Y) = (-0.21368027f0, 0.30252457f0)\n",
      "extrema(Ypred) = (-0.19681004266633192, 0.19427913415181852)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(-0.19681004266633192, 0.19427913415181852)"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Ypred = m(X)\n",
    "@show extrema(Y)\n",
    "@show extrema(Ypred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xall = vcat(lon3[:]',lat3[:]',cos.(2*pi*doy3[:]/365)',sin.(2*pi*doy3[:]/365)',temp[:]',zeta[:]') .- meanX;\n",
    "Yall = m(Xall) .+ meanY\n",
    "salt_pred = reshape(Yall,size(temp));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Figure(PyObject <Figure size 1600x700 with 4 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = 3\n",
    "cl = extrema(salt[isfinite.(salt)])\n",
    "figure(figsize=(16,7))\n",
    "subplot(1,2,1)\n",
    "plotvar(salt[:,:,n],cl = cl)\n",
    "title(\"original salinity\");\n",
    "\n",
    "subplot(1,2,2)\n",
    "plotvar(salt_pred[:,:,n],cl = cl)\n",
    "title(\"predicted salinity\");\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Figure(PyObject <Figure size 640x480 with 2 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotvar(temp[:,:,n])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Final remarks\n",
    "\n",
    "* Complex neural networks can take a long-time to converge\n",
    "* A fast GPU can be beneficial\n",
    "* Minimization can fail due to local minima\n",
    "* Mini-batch approach (i.e. computing the gradient on a subset of the data) helps to converge to a global minimum.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Julia 1.6.0",
   "language": "julia",
   "name": "julia-1.6"
  },
  "language_info": {
   "file_extension": ".jl",
   "mimetype": "application/julia",
   "name": "julia",
   "version": "1.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}