color-multiple-shapes.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-07-04T17:28:39.034000",
     "start_time": "2016-07-04T17:28:38.325000"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-07-05T22:57:56.251000",
     "start_time": "2016-07-05T22:57:54.426000"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((50000L, 32L, 32L, 3L), (50000L, 2L, 4L), (50000L, 2L), (50000L, 2L))"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import cairo\n",
    "num_imgs = 50000\n",
    "\n",
    "img_size = 32\n",
    "min_object_size = 4\n",
    "max_object_size = 16\n",
    "num_objects = 2\n",
    "\n",
    "bboxes = np.zeros((num_imgs, num_objects, 4))\n",
    "imgs = np.zeros((num_imgs, img_size, img_size, 4), dtype=np.uint8)  # format: BGRA\n",
    "shapes = np.zeros((num_imgs, num_objects), dtype=int)\n",
    "num_shapes = 3\n",
    "shape_labels = ['rectangle', 'circle', 'triangle']\n",
    "colors = np.zeros((num_imgs, num_objects), dtype=int)\n",
    "num_colors = 3\n",
    "color_labels = ['r', 'g', 'b']\n",
    "\n",
    "for i_img in range(num_imgs):\n",
    "    surface = cairo.ImageSurface.create_for_data(imgs[i_img], cairo.FORMAT_ARGB32, img_size, img_size)\n",
    "    cr = cairo.Context(surface)\n",
    "\n",
    "    # Fill background white.\n",
    "    cr.set_source_rgb(1, 1, 1)\n",
    "    cr.paint()\n",
    "    \n",
    "    # TODO: Try no overlap here.\n",
    "    # Draw random shapes.\n",
    "    for i_object in range(num_objects):\n",
    "        shape = np.random.randint(num_shapes)\n",
    "        shapes[i_img, i_object] = shape\n",
    "        if shape == 0:  # rectangle\n",
    "            w, h = np.random.randint(min_object_size, max_object_size, size=2)\n",
    "            x = np.random.randint(0, img_size - w)\n",
    "            y = np.random.randint(0, img_size - h)\n",
    "            bboxes[i_img, i_object] = [x, y, w, h]\n",
    "            cr.rectangle(x, y, w, h)            \n",
    "        elif shape == 1:  # circle   \n",
    "            r = 0.5 * np.random.randint(min_object_size, max_object_size)\n",
    "            x = np.random.randint(r, img_size - r)\n",
    "            y = np.random.randint(r, img_size - r)\n",
    "            bboxes[i_img, i_object] = [x - r, y - r, 2 * r, 2 * r]\n",
    "            cr.arc(x, y, r, 0, 2*np.pi)\n",
    "        elif shape == 2:  # triangle\n",
    "            w, h = np.random.randint(min_object_size, max_object_size, size=2)\n",
    "            x = np.random.randint(0, img_size - w)\n",
    "            y = np.random.randint(0, img_size - h)\n",
    "            bboxes[i_img, i_object] = [x, y, w, h]\n",
    "            cr.move_to(x, y)\n",
    "            cr.line_to(x+w, y)\n",
    "            cr.line_to(x+w, y+h)\n",
    "            cr.line_to(x, y)\n",
    "            cr.close_path()\n",
    "        \n",
    "        # TODO: Introduce some variation to the colors by adding a small random offset to the rgb values.\n",
    "        color = np.random.randint(num_colors)\n",
    "        colors[i_img, i_object] = color\n",
    "        max_offset = 0.3\n",
    "        r_offset, g_offset, b_offset = max_offset * 2. * (np.random.rand(3) - 0.5)\n",
    "        if color == 0:\n",
    "            cr.set_source_rgb(1-max_offset+r_offset, 0+g_offset, 0+b_offset)\n",
    "        elif color == 1:\n",
    "            cr.set_source_rgb(0+r_offset, 1-max_offset+g_offset, 0+b_offset)\n",
    "        elif color == 2:\n",
    "            cr.set_source_rgb(0+r_offset, 0-max_offset+g_offset, 1+b_offset)\n",
    "        cr.fill()\n",
    "        \n",
    "imgs = imgs[..., 2::-1]  # is BGRA, convert to RGB\n",
    "\n",
    "# surface.write_to_png('imgs/{}.png'.format(i_img))\n",
    "imgs.shape, bboxes.shape, shapes.shape, colors.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-07-05T22:57:56.856000",
     "start_time": "2016-07-05T22:57:56.254000"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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LXGRma4ELon4R6SG6PcZ395lZRl1Y4lpEpEJ05Z5IgBR8kQD1iN/j91R6zp1UK23xRQKk\n4IsESMEXCZCCLxIgBV8kQAq+SIB0Oq8Esp22y/Wcu1y/wBMpN23xRQKk4IsESMEXCZCCLxIgBV8k\nQAq+SIB0Oi8P3d1XLtuz7mY0zzjmeUQqQVt8kQAp+CIBUvBFAqTgiwRIwRcJkIIvEiCdziuBa359\nTcbhes6dVCtt8UUCpOCLBKioXX0zawN2AB3AQXefWIqiRKS8ij3G7yD1uOxtpShGRCqj2F19K8F7\niEiFFRtaB540s1Yzu7oUBYlI+RW7q3+uu28ws5NIfQGscfelXSdqamrq7E4kEiQSiSIXW1lmlnVc\nQ0MD93/x/gpWUz0aGhriLkHSJJNJkslkXtNadz85zZeZNQI73f2OLsO9VMuoRr35s3Un1xeixM/M\ncPeM/0kF7+qbWT8z6x91Hw98DHil0PcTkcopZld/KPBbM/PofX7t7i2lKUtEyqlku/pZF6Bd/V5L\nu/rVrSy7+iLScyn4IgHSr/OKpN1d6Ym0xRcJkIIvEiAFXyRACr5IgBR8kQAp+CIBUvBFAqTgiwRI\nwRcJkIIvEiAFXyRACr5IgBR8kQAp+CIBUvBFAqTgiwRIwRcJkIIvEiAFXyRACr5IgBR8kQAp+CIB\nKir4ZjbNzF41s7+Y2Q2lKkpEyquYh2bWAD8FpgKjgSvM7IxSFVYO+T5CuBJUy9GqpQ7o/bUUs8Wf\nCLzm7u3ufhB4AJhemrLKo7f/ZxaqWmqpljqg99dSTPBPBv6W1v9mNExEqpwa90QCVPBjss1sEtDk\n7tOi/hsBd/dbu0wX7nOkRWKW7THZxQS/FlgLXABsAP4IXOHuawotUkQqo+Cn5br7ITO7DmghdcjQ\nrNCL9AwFb/FFpOcqW+NeNV3cY2ZtZvaymS0zsz9WeNnNZrbJzFakDRtsZi1mttbMnjCzgTHW0mhm\nb5rZS9FrWoVqGW5mT5vZKjNbaWZfi4ZXfN1kqGVWNLzi68bM6s3sxehvdaWZNUbDS7te3L3kL1Jf\nKK8DDcB7gOXAGeVYVp71rAMGx7TsycA4YEXasFuB/4i6bwDmxlhLI3B9DOtlGDAu6u5Pqr3ojDjW\nTY5a4lo3/aJ/a4EXSF0zU9L1Uq4tfrVd3GPEdOrS3ZcC27oMng4sjLoXApfFWAuk1k9FuftGd18e\nde8C1gDDiWHdZKnl8DUpcaybPVFnPal2OKfE66VcYai2i3sceNLMWs3s6hjrOGyIu2+C1B8dMCTm\neq4zs+VmdmelDjvSmdkIUnsiLwBD41w3abW8GA2q+LoxsxozWwZsBJ5091ZKvF5CuYDnXHefAPwr\n8FUzmxx3QV3E2cI6HzjV3ceR+kO7o5ILN7P+wMPA16Otbdd1UbF1k6GWWNaNu3e4+3hSe0ATzWw0\nJV4v5Qr+34FT0vqHR8Ni4e4bon83A78ldSgSp01mNhTAzIYBb8VViLtv9ujAEfgF8M+VWraZ1ZEK\n2q/c/dFocCzrJlMtca6baPnvAElgGiVeL+UKfivwT2bWYGbHAZ8FFpVpWTmZWb/omxwzOx74GPBK\npcvgyGPFRcAXou7PA492naFStUR/RId9isqum7uA1e4+L21YXOvmqFriWDdmduLhQwoz6wtcRKrN\nobTrpYwtk9NItY6+BtxY6ZbRtDpGkjqrsAxYWelagPuA9cB+4A3gSmAw8FS0flqAQTHWcg+wIlpH\n/03qWLIStZwLHEr7v3kp+pt5b6XXTY5aKr5ugDHR8pdHy/5WNLyk60UX8IgEKJTGPRFJo+CLBEjB\nFwmQgi8SIAVfJEAKvkiAFHyRACn4IgH6f5VdQjR3zn7eAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xac8ef940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 5\n",
    "plt.imshow(imgs[i], interpolation='none', origin='lower', extent=[0, img_size, 0, img_size])\n",
    "for bbox, shape, color in zip(bboxes[i], shapes[i], colors[i]):\n",
    "    plt.gca().add_patch(matplotlib.patches.Rectangle((bbox[0], bbox[1]), bbox[2], bbox[3], ec='k', fc='none'))\n",
    "    plt.annotate(shape_labels[shape], (bbox[0], bbox[1] + bbox[3] + 0.7), color=color_labels[color], clip_on=False)\n",
    "# surface.write_to_png(\"circle.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-07-05T23:55:30.611000",
     "start_time": "2016-07-05T23:55:26.822000"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((50000L, 32L, 32L, 3L), 0.40545137653186303, 0.26626301805254604)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = (imgs - 128.) / 255.\n",
    "X.shape, np.mean(X), np.std(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-07-05T23:55:33.242000",
     "start_time": "2016-07-05T23:55:33.179000"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((50000L, 20L), True)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "colors_onehot = np.zeros((num_imgs, num_objects, num_colors))\n",
    "for i_img in range(num_imgs):\n",
    "    for i_object in range(num_objects):\n",
    "        colors_onehot[i_img, i_object, colors[i_img, i_object]] = 1\n",
    "\n",
    "shapes_onehot = np.zeros((num_imgs, num_objects, num_shapes))\n",
    "for i_img in range(num_imgs):\n",
    "    for i_object in range(num_objects):\n",
    "        shapes_onehot[i_img, i_object, shapes[i_img, i_object]] = 1\n",
    "        \n",
    "y = np.concatenate([bboxes / img_size, shapes_onehot, colors_onehot], axis=-1).reshape(num_imgs, -1)\n",
    "y.shape, np.all(np.argmax(colors_onehot, axis=-1) == colors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-07-05T23:55:33.989000",
     "start_time": "2016-07-05T23:55:33.981000"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "i = int(0.8 * num_imgs)\n",
    "train_X = X[:i]\n",
    "test_X = X[i:]\n",
    "train_y = y[:i]\n",
    "test_y = y[i:]\n",
    "test_imgs = imgs[i:]\n",
    "test_bboxes = bboxes[i:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-07-06T00:27:51.515000",
     "start_time": "2016-07-06T00:27:50.598000"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Activation, Dropout, Convolution2D, MaxPooling2D, Flatten\n",
    "\n",
    "# Activate GPU for this, otherwise the convnet will take forever to train with Theano.\n",
    "\n",
    "# TODO: Make one run with very deep network (~10 layers).\n",
    "filter_size = 3\n",
    "pool_size = 2\n",
    "\n",
    "# TODO: Maybe remove pooling bc it takes away the spatial information.\n",
    "\n",
    "model = Sequential([\n",
    "        Convolution2D(32, 6, 6, input_shape=X.shape[1:], dim_ordering='tf', activation='relu'), \n",
    "        MaxPooling2D(pool_size=(pool_size, pool_size)), \n",
    "        Convolution2D(64, filter_size, filter_size, dim_ordering='tf', activation='relu'), \n",
    "        MaxPooling2D(pool_size=(pool_size, pool_size)), \n",
    "        Convolution2D(128, filter_size, filter_size, dim_ordering='tf', activation='relu'), \n",
    "# #         MaxPooling2D(pool_size=(pool_size, pool_size)), \n",
    "        Convolution2D(128, filter_size, filter_size, dim_ordering='tf', activation='relu'), \n",
    "# #         MaxPooling2D(pool_size=(pool_size, pool_size)), \n",
    "        Flatten(), \n",
    "        Dropout(0.4), \n",
    "        Dense(256, activation='relu'), \n",
    "        Dropout(0.4), \n",
    "        Dense(y.shape[-1])\n",
    "    ])\n",
    "\n",
    "model.compile('adadelta', 'mse')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-07-06T16:47:14.477000",
     "start_time": "2016-07-06T00:27:51.517000"
    },
    "code_folding": [
     2,
     17
    ],
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "49s - loss: 0.1347 - val_loss: 0.1122\n",
      "Flipped 46.7975 % of all elements\n",
      "Mean IOU: 0.21033079451\n",
      "Mean dist: 0.219388273656\n",
      "Mean mse: 0.0207690124663\n",
      "Accuracy shapes: 0.4178625\n",
      "Accuracy colors: 0.66785\n",
      "--------------- TEST ----------------\n",
      "Flipped 50.37 % of all elements\n",
      "Mean IOU: 0.206793675131\n",
      "Mean dist: 0.220010190271\n",
      "Mean mse: 0.0207248295481\n",
      "Accuracy shapes: 0.4137\n",
      "Accuracy colors: 0.66685\n",
      "\n",
      "Epoch 1\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "48s - loss: 0.1068 - val_loss: 0.1047\n",
      "Flipped 24.2375 % of all elements\n",
      "Mean IOU: 0.243669244136\n",
      "Mean dist: 0.194529039042\n",
      "Mean mse: 0.0183214527394\n",
      "Accuracy shapes: 0.5530625\n",
      "Accuracy colors: 0.778025\n",
      "--------------- TEST ----------------\n",
      "Flipped 27.27 % of all elements\n",
      "Mean IOU: 0.238765597626\n",
      "Mean dist: 0.193855493724\n",
      "Mean mse: 0.0181368993602\n",
      "Accuracy shapes: 0.54235\n",
      "Accuracy colors: 0.7671\n",
      "\n",
      "Epoch 2\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "48s - loss: 0.0890 - val_loss: 0.1001\n",
      "Flipped 14.4875 % of all elements\n",
      "Mean IOU: 0.26844928384\n",
      "Mean dist: 0.178382605836\n",
      "Mean mse: 0.0162804254468\n",
      "Accuracy shapes: 0.7180625\n",
      "Accuracy colors: 0.852475\n",
      "--------------- TEST ----------------\n",
      "Flipped 15.71 % of all elements\n",
      "Mean IOU: 0.263017331421\n",
      "Mean dist: 0.176943816739\n",
      "Mean mse: 0.0160746197083\n",
      "Accuracy shapes: 0.71625\n",
      "Accuracy colors: 0.8476\n",
      "\n",
      "Epoch 3\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "49s - loss: 0.0743 - val_loss: 0.1036\n",
      "Flipped 11.9825 % of all elements\n",
      "Mean IOU: 0.289909705021\n",
      "Mean dist: 0.165374013296\n",
      "Mean mse: 0.0148229787364\n",
      "Accuracy shapes: 0.7886125\n",
      "Accuracy colors: 0.87825\n",
      "--------------- TEST ----------------\n",
      "Flipped 12.28 % of all elements\n",
      "Mean IOU: 0.284322291076\n",
      "Mean dist: 0.163859219319\n",
      "Mean mse: 0.0146139403248\n",
      "Accuracy shapes: 0.78275\n",
      "Accuracy colors: 0.8734\n",
      "\n",
      "Epoch 4\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "48s - loss: 0.0641 - val_loss: 0.1060\n",
      "Flipped 8.475 % of all elements\n",
      "Mean IOU: 0.308853796636\n",
      "Mean dist: 0.156508761661\n",
      "Mean mse: 0.0137840322475\n",
      "Accuracy shapes: 0.84555\n",
      "Accuracy colors: 0.9039375\n",
      "--------------- TEST ----------------\n",
      "Flipped 8.16 % of all elements\n",
      "Mean IOU: 0.303404962304\n",
      "Mean dist: 0.155804608785\n",
      "Mean mse: 0.0136887984626\n",
      "Accuracy shapes: 0.8363\n",
      "Accuracy colors: 0.8977\n",
      "\n",
      "Epoch 5\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "48s - loss: 0.0565 - val_loss: 0.1067\n",
      "Flipped 6.635 % of all elements\n",
      "Mean IOU: 0.314029170005\n",
      "Mean dist: 0.15214241441\n",
      "Mean mse: 0.0132217867671\n",
      "Accuracy shapes: 0.866675\n",
      "Accuracy colors: 0.910675\n",
      "--------------- TEST ----------------\n",
      "Flipped 6.9 % of all elements\n",
      "Mean IOU: 0.308088475416\n",
      "Mean dist: 0.152038971128\n",
      "Mean mse: 0.0131831493226\n",
      "Accuracy shapes: 0.86195\n",
      "Accuracy colors: 0.90335\n",
      "\n",
      "Epoch 6\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "48s - loss: 0.0522 - val_loss: 0.1116\n",
      "Flipped 6.9725 % of all elements\n",
      "Mean IOU: 0.323648503246\n",
      "Mean dist: 0.149720935395\n",
      "Mean mse: 0.0129679001781\n",
      "Accuracy shapes: 0.8841375\n",
      "Accuracy colors: 0.918475\n",
      "--------------- TEST ----------------\n",
      "Flipped 7.18 % of all elements\n",
      "Mean IOU: 0.317843105334\n",
      "Mean dist: 0.149850095575\n",
      "Mean mse: 0.0129751302853\n",
      "Accuracy shapes: 0.87605\n",
      "Accuracy colors: 0.9142\n",
      "\n",
      "Epoch 7\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "49s - loss: 0.0485 - val_loss: 0.1108\n",
      "Flipped 5.5575 % of all elements\n",
      "Mean IOU: 0.331833769185\n",
      "Mean dist: 0.143842773503\n",
      "Mean mse: 0.0122136618615\n",
      "Accuracy shapes: 0.9005125\n",
      "Accuracy colors: 0.927\n",
      "--------------- TEST ----------------\n",
      "Flipped 6.26 % of all elements\n",
      "Mean IOU: 0.325190797425\n",
      "Mean dist: 0.144330893657\n",
      "Mean mse: 0.0122614741864\n",
      "Accuracy shapes: 0.8907\n",
      "Accuracy colors: 0.9199\n",
      "\n",
      "Epoch 8\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "49s - loss: 0.0461 - val_loss: 0.1137\n",
      "Flipped 5.205 % of all elements\n",
      "Mean IOU: 0.341123800304\n",
      "Mean dist: 0.138611989252\n",
      "Mean mse: 0.0116363412781\n",
      "Accuracy shapes: 0.91\n",
      "Accuracy colors: 0.9310125\n",
      "--------------- TEST ----------------\n",
      "Flipped 5.91 % of all elements\n",
      "Mean IOU: 0.334229541869\n",
      "Mean dist: 0.13898259837\n",
      "Mean mse: 0.0116639406111\n",
      "Accuracy shapes: 0.89875\n",
      "Accuracy colors: 0.92455\n",
      "\n",
      "Epoch 9\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "48s - loss: 0.0438 - val_loss: 0.1154\n",
      "Flipped 5.4775 % of all elements\n",
      "Mean IOU: 0.347196235118\n",
      "Mean dist: 0.135402843612\n",
      "Mean mse: 0.011286528221\n",
      "Accuracy shapes: 0.9154375\n",
      "Accuracy colors: 0.9350375\n",
      "--------------- TEST ----------------\n",
      "Flipped 5.45 % of all elements\n",
      "Mean IOU: 0.340399955598\n",
      "Mean dist: 0.135880339479\n",
      "Mean mse: 0.0113277205003\n",
      "Accuracy shapes: 0.90535\n",
      "Accuracy colors: 0.9254\n",
      "\n",
      "Epoch 10\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "48s - loss: 0.0419 - val_loss: 0.1171\n",
      "Flipped 5.01 % of all elements\n",
      "Mean IOU: 0.352595007579\n",
      "Mean dist: 0.131331778453\n",
      "Mean mse: 0.010839763065\n",
      "Accuracy shapes: 0.9210875\n",
      "Accuracy colors: 0.9402625\n",
      "--------------- TEST ----------------\n",
      "Flipped 4.73 % of all elements\n",
      "Mean IOU: 0.344904714582\n",
      "Mean dist: 0.131795508729\n",
      "Mean mse: 0.0108526415574\n",
      "Accuracy shapes: 0.91375\n",
      "Accuracy colors: 0.93255\n",
      "\n",
      "Epoch 11\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "48s - loss: 0.0402 - val_loss: 0.1164\n",
      "Flipped 4.5025 % of all elements\n",
      "Mean IOU: 0.364728959218\n",
      "Mean dist: 0.12756954609\n",
      "Mean mse: 0.0105041218384\n",
      "Accuracy shapes: 0.92765\n",
      "Accuracy colors: 0.94225\n",
      "--------------- TEST ----------------\n",
      "Flipped 5.66 % of all elements\n",
      "Mean IOU: 0.357306740248\n",
      "Mean dist: 0.12848406333\n",
      "Mean mse: 0.0105880952593\n",
      "Accuracy shapes: 0.9183\n",
      "Accuracy colors: 0.93655\n",
      "\n",
      "Epoch 12\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "48s - loss: 0.0387 - val_loss: 0.1170\n",
      "Flipped 4.035 % of all elements\n",
      "Mean IOU: 0.364925211724\n",
      "Mean dist: 0.126165929141\n",
      "Mean mse: 0.0103198581811\n",
      "Accuracy shapes: 0.929975\n",
      "Accuracy colors: 0.946175\n",
      "--------------- TEST ----------------\n",
      "Flipped 4.81 % of all elements\n",
      "Mean IOU: 0.357064694339\n",
      "Mean dist: 0.12712101971\n",
      "Mean mse: 0.0104007427233\n",
      "Accuracy shapes: 0.91955\n",
      "Accuracy colors: 0.93915\n",
      "\n",
      "Epoch 13\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "49s - loss: 0.0376 - val_loss: 0.1187\n",
      "Flipped 3.7 % of all elements\n",
      "Mean IOU: 0.369254696014\n",
      "Mean dist: 0.123358855083\n",
      "Mean mse: 0.0100092818408\n",
      "Accuracy shapes: 0.9346875\n",
      "Accuracy colors: 0.947525\n",
      "--------------- TEST ----------------\n",
      "Flipped 4.26 % of all elements\n",
      "Mean IOU: 0.361223155484\n",
      "Mean dist: 0.124406362449\n",
      "Mean mse: 0.0100906098564\n",
      "Accuracy shapes: 0.9243\n",
      "Accuracy colors: 0.94105\n",
      "\n",
      "Epoch 14\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "49s - loss: 0.0365 - val_loss: 0.1205\n",
      "Flipped 3.525 % of all elements\n",
      "Mean IOU: 0.377190222808\n",
      "Mean dist: 0.119843913043\n",
      "Mean mse: 0.00971243673339\n",
      "Accuracy shapes: 0.938125\n",
      "Accuracy colors: 0.9498\n",
      "--------------- TEST ----------------\n",
      "Flipped 4.15 % of all elements\n",
      "Mean IOU: 0.369318178004\n",
      "Mean dist: 0.120866582707\n",
      "Mean mse: 0.00979346010223\n",
      "Accuracy shapes: 0.9261\n",
      "Accuracy colors: 0.9441\n",
      "\n",
      "Epoch 15\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "48s - loss: 0.0355 - val_loss: 0.1200\n",
      "Flipped 4.0725 % of all elements\n",
      "Mean IOU: 0.372567684376\n",
      "Mean dist: 0.122458577895\n",
      "Mean mse: 0.00986301875484\n",
      "Accuracy shapes: 0.937475\n",
      "Accuracy colors: 0.9499375\n",
      "--------------- TEST ----------------\n",
      "Flipped 4.58 % of all elements\n",
      "Mean IOU: 0.364496866812\n",
      "Mean dist: 0.123607131613\n",
      "Mean mse: 0.00997003841347\n",
      "Accuracy shapes: 0.92645\n",
      "Accuracy colors: 0.9408\n",
      "\n",
      "Epoch 16\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "49s - loss: 0.0352 - val_loss: 0.1186\n",
      "Flipped 3.8075 % of all elements\n",
      "Mean IOU: 0.378862576425\n",
      "Mean dist: 0.118363056562\n",
      "Mean mse: 0.00949066628347\n",
      "Accuracy shapes: 0.9395625\n",
      "Accuracy colors: 0.9502625\n",
      "--------------- TEST ----------------\n",
      "Flipped 4.28 % of all elements\n",
      "Mean IOU: 0.370230954942\n",
      "Mean dist: 0.119780553292\n",
      "Mean mse: 0.00960662508835\n",
      "Accuracy shapes: 0.92885\n",
      "Accuracy colors: 0.94035\n",
      "\n",
      "Epoch 17\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "49s - loss: 0.0344 - val_loss: 0.1211\n",
      "Flipped 3.72 % of all elements\n",
      "Mean IOU: 0.385145658264\n",
      "Mean dist: 0.116294739611\n",
      "Mean mse: 0.00928757912199\n",
      "Accuracy shapes: 0.9425625\n",
      "Accuracy colors: 0.9532375\n",
      "--------------- TEST ----------------\n",
      "Flipped 4.13 % of all elements\n",
      "Mean IOU: 0.3761576032\n",
      "Mean dist: 0.117867200291\n",
      "Mean mse: 0.00941588691813\n",
      "Accuracy shapes: 0.93015\n",
      "Accuracy colors: 0.94355\n",
      "\n",
      "Epoch 18\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "49s - loss: 0.0336 - val_loss: 0.1202\n",
      "Flipped 4.1025 % of all elements\n",
      "Mean IOU: 0.377865348683\n",
      "Mean dist: 0.11758913873\n",
      "Mean mse: 0.00937793612835\n",
      "Accuracy shapes: 0.9421875\n",
      "Accuracy colors: 0.9521875\n",
      "--------------- TEST ----------------\n",
      "Flipped 4.55 % of all elements\n",
      "Mean IOU: 0.368053820876\n",
      "Mean dist: 0.119188351002\n",
      "Mean mse: 0.00950210390399\n",
      "Accuracy shapes: 0.93065\n",
      "Accuracy colors: 0.94185\n",
      "\n",
      "Epoch 19\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "48s - loss: 0.0333 - val_loss: 0.1205\n",
      "Flipped 3.395 % of all elements\n",
      "Mean IOU: 0.39011896977\n",
      "Mean dist: 0.113913841529\n",
      "Mean mse: 0.00902453063088\n",
      "Accuracy shapes: 0.9446125\n",
      "Accuracy colors: 0.9531625\n",
      "--------------- TEST ----------------\n",
      "Flipped 4.2 % of all elements\n",
      "Mean IOU: 0.381516209716\n",
      "Mean dist: 0.115595224413\n",
      "Mean mse: 0.0091613603925\n",
      "Accuracy shapes: 0.9335\n",
      "Accuracy colors: 0.9442\n",
      "\n",
      "Epoch 20\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "48s - loss: 0.0328 - val_loss: 0.1181\n",
      "Flipped 3.3775 % of all elements\n",
      "Mean IOU: 0.387882367673\n",
      "Mean dist: 0.114495456776\n",
      "Mean mse: 0.00909718864336\n",
      "Accuracy shapes: 0.9470125\n",
      "Accuracy colors: 0.9543125\n",
      "--------------- TEST ----------------\n",
      "Flipped 4.18 % of all elements\n",
      "Mean IOU: 0.379642998994\n",
      "Mean dist: 0.115797125036\n",
      "Mean mse: 0.00920815288283\n",
      "Accuracy shapes: 0.9361\n",
      "Accuracy colors: 0.9442\n",
      "\n",
      "Epoch 21\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "48s - loss: 0.0321 - val_loss: 0.1215\n",
      "Flipped 3.5675 % of all elements\n",
      "Mean IOU: 0.394824501828\n",
      "Mean dist: 0.111992298236\n",
      "Mean mse: 0.00883628803056\n",
      "Accuracy shapes: 0.9489875\n",
      "Accuracy colors: 0.955375\n",
      "--------------- TEST ----------------\n",
      "Flipped 4.67 % of all elements\n",
      "Mean IOU: 0.386731644858\n",
      "Mean dist: 0.113769448498\n",
      "Mean mse: 0.00898164545277\n",
      "Accuracy shapes: 0.93555\n",
      "Accuracy colors: 0.94605\n",
      "\n",
      "Epoch 22\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "48s - loss: 0.0315 - val_loss: 0.1216\n",
      "Flipped 3.8125 % of all elements\n",
      "Mean IOU: 0.388582799628\n",
      "Mean dist: 0.112526834957\n",
      "Mean mse: 0.00885314047625\n",
      "Accuracy shapes: 0.948925\n",
      "Accuracy colors: 0.958075\n",
      "--------------- TEST ----------------\n",
      "Flipped 4.75 % of all elements\n",
      "Mean IOU: 0.379310729602\n",
      "Mean dist: 0.11402238307\n",
      "Mean mse: 0.00897834630201\n",
      "Accuracy shapes: 0.93835\n",
      "Accuracy colors: 0.9486\n",
      "\n",
      "Epoch 23\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "48s - loss: 0.0312 - val_loss: 0.1228\n",
      "Flipped 3.4525 % of all elements\n",
      "Mean IOU: 0.406700365319\n",
      "Mean dist: 0.107524207114\n",
      "Mean mse: 0.00847088028763\n",
      "Accuracy shapes: 0.951925\n",
      "Accuracy colors: 0.9590125\n",
      "--------------- TEST ----------------\n",
      "Flipped 4.07 % of all elements\n",
      "Mean IOU: 0.398076697652\n",
      "Mean dist: 0.109136528016\n",
      "Mean mse: 0.00861952649885\n",
      "Accuracy shapes: 0.94065\n",
      "Accuracy colors: 0.94705\n",
      "\n",
      "Epoch 24\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "48s - loss: 0.0307 - val_loss: 0.1216\n",
      "Flipped 3.39 % of all elements\n",
      "Mean IOU: 0.408052341651\n",
      "Mean dist: 0.105968724066\n",
      "Mean mse: 0.00832737513824\n",
      "Accuracy shapes: 0.9516125\n",
      "Accuracy colors: 0.9593125\n",
      "--------------- TEST ----------------\n",
      "Flipped 3.84 % of all elements\n",
      "Mean IOU: 0.398332260356\n",
      "Mean dist: 0.107945743777\n",
      "Mean mse: 0.0084925695858\n",
      "Accuracy shapes: 0.9403\n",
      "Accuracy colors: 0.9496\n",
      "\n",
      "Epoch 25\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "48s - loss: 0.0303 - val_loss: 0.1203\n",
      "Flipped 3.1975 % of all elements\n",
      "Mean IOU: 0.399444738811\n",
      "Mean dist: 0.108970942113\n",
      "Mean mse: 0.00845952584794\n",
      "Accuracy shapes: 0.9536125\n",
      "Accuracy colors: 0.9592375\n",
      "--------------- TEST ----------------\n",
      "Flipped 3.88 % of all elements\n",
      "Mean IOU: 0.390908656053\n",
      "Mean dist: 0.110873455775\n",
      "Mean mse: 0.00864086471079\n",
      "Accuracy shapes: 0.94155\n",
      "Accuracy colors: 0.94905\n",
      "\n",
      "Epoch 26\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "48s - loss: 0.0298 - val_loss: 0.1240\n",
      "Flipped 2.7975 % of all elements\n",
      "Mean IOU: 0.403870853115\n",
      "Mean dist: 0.106289449844\n",
      "Mean mse: 0.00825212204863\n",
      "Accuracy shapes: 0.9546125\n",
      "Accuracy colors: 0.961625\n",
      "--------------- TEST ----------------\n",
      "Flipped 3.6 % of all elements\n",
      "Mean IOU: 0.394834154953\n",
      "Mean dist: 0.108304742144\n",
      "Mean mse: 0.00843570931543\n",
      "Accuracy shapes: 0.93965\n",
      "Accuracy colors: 0.95195\n",
      "\n",
      "Epoch 27\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "48s - loss: 0.0293 - val_loss: 0.1241\n",
      "Flipped 2.925 % of all elements\n",
      "Mean IOU: 0.405735203005\n",
      "Mean dist: 0.106087968668\n",
      "Mean mse: 0.00819873883474\n",
      "Accuracy shapes: 0.9556875\n",
      "Accuracy colors: 0.9621\n",
      "--------------- TEST ----------------\n",
      "Flipped 3.57 % of all elements\n",
      "Mean IOU: 0.397145989334\n",
      "Mean dist: 0.107796057447\n",
      "Mean mse: 0.00837177632156\n",
      "Accuracy shapes: 0.942\n",
      "Accuracy colors: 0.95165\n",
      "\n",
      "Epoch 28\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "48s - loss: 0.0291 - val_loss: 0.1231\n",
      "Flipped 2.9775 % of all elements\n",
      "Mean IOU: 0.408312534846\n",
      "Mean dist: 0.10639512336\n",
      "Mean mse: 0.00820833197094\n",
      "Accuracy shapes: 0.956175\n",
      "Accuracy colors: 0.9631625\n",
      "--------------- TEST ----------------\n",
      "Flipped 3.81 % of all elements\n",
      "Mean IOU: 0.399529933468\n",
      "Mean dist: 0.108522732611\n",
      "Mean mse: 0.00841127135779\n",
      "Accuracy shapes: 0.94125\n",
      "Accuracy colors: 0.9528\n",
      "\n",
      "Epoch 29\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "48s - loss: 0.0285 - val_loss: 0.1227\n",
      "Flipped 3.0825 % of all elements\n",
      "Mean IOU: 0.406425872149\n",
      "Mean dist: 0.105700829991\n",
      "Mean mse: 0.00819483775349\n",
      "Accuracy shapes: 0.95485\n",
      "Accuracy colors: 0.9620625\n",
      "--------------- TEST ----------------\n",
      "Flipped 3.5 % of all elements\n",
      "Mean IOU: 0.397577713655\n",
      "Mean dist: 0.108007960644\n",
      "Mean mse: 0.00840404783616\n",
      "Accuracy shapes: 0.94185\n",
      "Accuracy colors: 0.95285\n",
      "\n",
      "Epoch 30\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "48s - loss: 0.0284 - val_loss: 0.1213\n",
      "Flipped 2.79 % of all elements\n",
      "Mean IOU: 0.413987180595\n",
      "Mean dist: 0.102676895453\n",
      "Mean mse: 0.00793057668882\n",
      "Accuracy shapes: 0.9586875\n",
      "Accuracy colors: 0.96505\n",
      "--------------- TEST ----------------\n",
      "Flipped 3.37 % of all elements\n",
      "Mean IOU: 0.404215634116\n",
      "Mean dist: 0.104995147986\n",
      "Mean mse: 0.00815489321227\n",
      "Accuracy shapes: 0.9451\n",
      "Accuracy colors: 0.9537\n",
      "\n",
      "Epoch 31\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "48s - loss: 0.0279 - val_loss: 0.1233\n",
      "Flipped 3.2475 % of all elements\n",
      "Mean IOU: 0.408749845289\n",
      "Mean dist: 0.104841085992\n",
      "Mean mse: 0.00806649367049\n",
      "Accuracy shapes: 0.95715\n",
      "Accuracy colors: 0.9644375\n",
      "--------------- TEST ----------------\n",
      "Flipped 4.17 % of all elements\n",
      "Mean IOU: 0.399753682015\n",
      "Mean dist: 0.107149384649\n",
      "Mean mse: 0.00826373518683\n",
      "Accuracy shapes: 0.943\n",
      "Accuracy colors: 0.9547\n",
      "\n",
      "Epoch 32\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "48s - loss: 0.0280 - val_loss: 0.1238\n",
      "Flipped 3.3225 % of all elements\n",
      "Mean IOU: 0.404562073848\n",
      "Mean dist: 0.105805087248\n",
      "Mean mse: 0.00811353517253\n",
      "Accuracy shapes: 0.954875\n",
      "Accuracy colors: 0.9644\n",
      "--------------- TEST ----------------\n",
      "Flipped 4.24 % of all elements\n",
      "Mean IOU: 0.394797758417\n",
      "Mean dist: 0.107687113248\n",
      "Mean mse: 0.00827537918696\n",
      "Accuracy shapes: 0.9391\n",
      "Accuracy colors: 0.9532\n",
      "\n",
      "Epoch 33\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "48s - loss: 0.0279 - val_loss: 0.1233\n",
      "Flipped 3.22 % of all elements\n",
      "Mean IOU: 0.41176343598\n",
      "Mean dist: 0.103226231586\n",
      "Mean mse: 0.00787667297861\n",
      "Accuracy shapes: 0.960325\n",
      "Accuracy colors: 0.9651375\n",
      "--------------- TEST ----------------\n",
      "Flipped 4.2 % of all elements\n",
      "Mean IOU: 0.401999632475\n",
      "Mean dist: 0.105547302108\n",
      "Mean mse: 0.00809349588974\n",
      "Accuracy shapes: 0.9457\n",
      "Accuracy colors: 0.95315\n",
      "\n",
      "Epoch 34\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "48s - loss: 0.0275 - val_loss: 0.1243\n",
      "Flipped 2.6875 % of all elements\n",
      "Mean IOU: 0.410856653009\n",
      "Mean dist: 0.103246568922\n",
      "Mean mse: 0.00789210718039\n",
      "Accuracy shapes: 0.96095\n",
      "Accuracy colors: 0.9658625\n",
      "--------------- TEST ----------------\n",
      "Flipped 3.61 % of all elements\n",
      "Mean IOU: 0.401424900452\n",
      "Mean dist: 0.105338528183\n",
      "Mean mse: 0.00807141625986\n",
      "Accuracy shapes: 0.9486\n",
      "Accuracy colors: 0.9539\n",
      "\n",
      "Epoch 35\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "48s - loss: 0.0271 - val_loss: 0.1228\n",
      "Flipped 2.9125 % of all elements\n",
      "Mean IOU: 0.419634930029\n",
      "Mean dist: 0.100608753535\n",
      "Mean mse: 0.0076645926181\n",
      "Accuracy shapes: 0.9612\n",
      "Accuracy colors: 0.966575\n",
      "--------------- TEST ----------------\n",
      "Flipped 3.71 % of all elements\n",
      "Mean IOU: 0.409750196896\n",
      "Mean dist: 0.103056477395\n",
      "Mean mse: 0.00788790202367\n",
      "Accuracy shapes: 0.9468\n",
      "Accuracy colors: 0.9594\n",
      "\n",
      "Epoch 36\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "48s - loss: 0.0267 - val_loss: 0.1265\n",
      "Flipped 2.825 % of all elements\n",
      "Mean IOU: 0.416527663345\n",
      "Mean dist: 0.102040820816\n",
      "Mean mse: 0.00775374923278\n",
      "Accuracy shapes: 0.9611625\n",
      "Accuracy colors: 0.9653625\n",
      "--------------- TEST ----------------\n",
      "Flipped 3.94 % of all elements\n",
      "Mean IOU: 0.406900968201\n",
      "Mean dist: 0.103965654436\n",
      "Mean mse: 0.0079425805831\n",
      "Accuracy shapes: 0.94705\n",
      "Accuracy colors: 0.95465\n",
      "\n",
      "Epoch 37\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "48s - loss: 0.0265 - val_loss: 0.1250\n",
      "Flipped 2.875 % of all elements\n",
      "Mean IOU: 0.420418120747\n",
      "Mean dist: 0.0998739940593\n",
      "Mean mse: 0.00754436502007\n",
      "Accuracy shapes: 0.9629375\n",
      "Accuracy colors: 0.96775\n",
      "--------------- TEST ----------------\n",
      "Flipped 3.63 % of all elements\n",
      "Mean IOU: 0.411035767597\n",
      "Mean dist: 0.102277392054\n",
      "Mean mse: 0.00776329559205\n",
      "Accuracy shapes: 0.9476\n",
      "Accuracy colors: 0.958\n",
      "\n",
      "Epoch 38\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "48s - loss: 0.0263 - val_loss: 0.1265\n",
      "Flipped 2.8125 % of all elements\n",
      "Mean IOU: 0.415505721738\n",
      "Mean dist: 0.100898095252\n",
      "Mean mse: 0.00764424185577\n",
      "Accuracy shapes: 0.962\n",
      "Accuracy colors: 0.9677875\n",
      "--------------- TEST ----------------\n",
      "Flipped 3.36 % of all elements\n",
      "Mean IOU: 0.404941724999\n",
      "Mean dist: 0.103679823952\n",
      "Mean mse: 0.00786993413468\n",
      "Accuracy shapes: 0.9473\n",
      "Accuracy colors: 0.95655\n",
      "\n",
      "Epoch 39\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "48s - loss: 0.0261 - val_loss: 0.1257\n",
      "Flipped 2.69 % of all elements\n",
      "Mean IOU: 0.424564615499\n",
      "Mean dist: 0.0981725937415\n",
      "Mean mse: 0.00744081551778\n",
      "Accuracy shapes: 0.9642375\n",
      "Accuracy colors: 0.969925\n",
      "--------------- TEST ----------------\n",
      "Flipped 3.43 % of all elements\n",
      "Mean IOU: 0.414703773623\n",
      "Mean dist: 0.10096824487\n",
      "Mean mse: 0.00769844686877\n",
      "Accuracy shapes: 0.95005\n",
      "Accuracy colors: 0.9611\n",
      "\n",
      "Epoch 40\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "48s - loss: 0.0256 - val_loss: 0.1245\n",
      "Flipped 2.715 % of all elements\n",
      "Mean IOU: 0.416816940816\n",
      "Mean dist: 0.100795994962\n",
      "Mean mse: 0.00754879556114\n",
      "Accuracy shapes: 0.96505\n",
      "Accuracy colors: 0.9698375\n",
      "--------------- TEST ----------------\n",
      "Flipped 3.45 % of all elements\n",
      "Mean IOU: 0.406533501366\n",
      "Mean dist: 0.103350369615\n",
      "Mean mse: 0.00777985369066\n",
      "Accuracy shapes: 0.94995\n",
      "Accuracy colors: 0.9588\n",
      "\n",
      "Epoch 41\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "48s - loss: 0.0256 - val_loss: 0.1259\n",
      "Flipped 2.4375 % of all elements\n",
      "Mean IOU: 0.422697743081\n",
      "Mean dist: 0.0978656004839\n",
      "Mean mse: 0.00737206074562\n",
      "Accuracy shapes: 0.9668375\n",
      "Accuracy colors: 0.9706875\n",
      "--------------- TEST ----------------\n",
      "Flipped 3.29 % of all elements\n",
      "Mean IOU: 0.412824423675\n",
      "Mean dist: 0.100417530694\n",
      "Mean mse: 0.00760730055759\n",
      "Accuracy shapes: 0.95185\n",
      "Accuracy colors: 0.95975\n",
      "\n",
      "Epoch 42\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "48s - loss: 0.0252 - val_loss: 0.1257\n",
      "Flipped 2.3625 % of all elements\n",
      "Mean IOU: 0.422737025396\n",
      "Mean dist: 0.099141734367\n",
      "Mean mse: 0.0073701733643\n",
      "Accuracy shapes: 0.966375\n",
      "Accuracy colors: 0.9722125\n",
      "--------------- TEST ----------------\n",
      "Flipped 3.22 % of all elements\n",
      "Mean IOU: 0.412733299955\n",
      "Mean dist: 0.101737968558\n",
      "Mean mse: 0.00760280892309\n",
      "Accuracy shapes: 0.95125\n",
      "Accuracy colors: 0.96055\n",
      "\n",
      "Epoch 43\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "48s - loss: 0.0250 - val_loss: 0.1250\n",
      "Flipped 2.2025 % of all elements\n",
      "Mean IOU: 0.4252943166\n",
      "Mean dist: 0.0974690680787\n",
      "Mean mse: 0.00731648171445\n",
      "Accuracy shapes: 0.9673125\n",
      "Accuracy colors: 0.9718375\n",
      "--------------- TEST ----------------\n",
      "Flipped 3.08 % of all elements\n",
      "Mean IOU: 0.415227327158\n",
      "Mean dist: 0.100214300938\n",
      "Mean mse: 0.00757143193384\n",
      "Accuracy shapes: 0.95185\n",
      "Accuracy colors: 0.95995\n",
      "\n",
      "Epoch 44\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "48s - loss: 0.0247 - val_loss: 0.1247\n",
      "Flipped 2.34 % of all elements\n",
      "Mean IOU: 0.420353591867\n",
      "Mean dist: 0.0983021475068\n",
      "Mean mse: 0.00734623551059\n",
      "Accuracy shapes: 0.96725\n",
      "Accuracy colors: 0.9716375\n",
      "--------------- TEST ----------------\n",
      "Flipped 3.39 % of all elements\n",
      "Mean IOU: 0.409607089638\n",
      "Mean dist: 0.10132211842\n",
      "Mean mse: 0.00760024847677\n",
      "Accuracy shapes: 0.95135\n",
      "Accuracy colors: 0.95935\n",
      "\n",
      "Epoch 45\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "48s - loss: 0.0246 - val_loss: 0.1259\n",
      "Flipped 2.2925 % of all elements\n",
      "Mean IOU: 0.421604965372\n",
      "Mean dist: 0.0983385527068\n",
      "Mean mse: 0.00727052226842\n",
      "Accuracy shapes: 0.9667\n",
      "Accuracy colors: 0.9718625\n",
      "--------------- TEST ----------------\n",
      "Flipped 3.01 % of all elements\n",
      "Mean IOU: 0.411070076941\n",
      "Mean dist: 0.101445276816\n",
      "Mean mse: 0.00754435281682\n",
      "Accuracy shapes: 0.95165\n",
      "Accuracy colors: 0.9595\n",
      "\n",
      "Epoch 46\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "48s - loss: 0.0245 - val_loss: 0.1249\n",
      "Flipped 2.185 % of all elements\n",
      "Mean IOU: 0.423282165821\n",
      "Mean dist: 0.0981862960357\n",
      "Mean mse: 0.00729736474147\n",
      "Accuracy shapes: 0.968775\n",
      "Accuracy colors: 0.973225\n",
      "--------------- TEST ----------------\n",
      "Flipped 2.99 % of all elements\n",
      "Mean IOU: 0.412787918072\n",
      "Mean dist: 0.101639174462\n",
      "Mean mse: 0.00759521900113\n",
      "Accuracy shapes: 0.9503\n",
      "Accuracy colors: 0.9603\n",
      "\n",
      "Epoch 47\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "48s - loss: 0.0241 - val_loss: 0.1240\n",
      "Flipped 2.2125 % of all elements\n",
      "Mean IOU: 0.42385351604\n",
      "Mean dist: 0.0985899254351\n",
      "Mean mse: 0.00731583695927\n",
      "Accuracy shapes: 0.9679375\n",
      "Accuracy colors: 0.973025\n",
      "--------------- TEST ----------------\n",
      "Flipped 3.01 % of all elements\n",
      "Mean IOU: 0.413378821272\n",
      "Mean dist: 0.101914699819\n",
      "Mean mse: 0.00761308063193\n",
      "Accuracy shapes: 0.9504\n",
      "Accuracy colors: 0.95905\n",
      "\n",
      "Epoch 48\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "48s - loss: 0.0240 - val_loss: 0.1275\n",
      "Flipped 2.0875 % of all elements\n",
      "Mean IOU: 0.431438927168\n",
      "Mean dist: 0.0946749228976\n",
      "Mean mse: 0.00702593173618\n",
      "Accuracy shapes: 0.9698875\n",
      "Accuracy colors: 0.9731875\n",
      "--------------- TEST ----------------\n",
      "Flipped 3.04 % of all elements\n",
      "Mean IOU: 0.42033194224\n",
      "Mean dist: 0.0979559929002\n",
      "Mean mse: 0.00732273750272\n",
      "Accuracy shapes: 0.95295\n",
      "Accuracy colors: 0.95965\n",
      "\n",
      "Epoch 49\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "10713s - loss: 0.0237 - val_loss: 0.1272\n",
      "Flipped 2.0675 % of all elements\n",
      "Mean IOU: 0.427254585185\n",
      "Mean dist: 0.0958538932462\n",
      "Mean mse: 0.00711176623209\n",
      "Accuracy shapes: 0.9691625\n",
      "Accuracy colors: 0.9735875\n",
      "--------------- TEST ----------------\n",
      "Flipped 2.84 % of all elements\n",
      "Mean IOU: 0.41625295394\n",
      "Mean dist: 0.0993014937617\n",
      "Mean mse: 0.00741618199698\n",
      "Accuracy shapes: 0.95145\n",
      "Accuracy colors: 0.9619\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Flip bboxes during training.\n",
    "# Note: The validation loss is always quite big here because we don't flip the bounding boxes for the validation data. \n",
    "def IOU(bbox1, bbox2):\n",
    "    '''Calculate overlap between two bounding boxes [x, y, w, h] as the area of intersection over the area of unity'''\n",
    "    x1, y1, w1, h1 = bbox1[0], bbox1[1], bbox1[2], bbox1[3]  # TODO: Check if its more performant if tensor elements are accessed directly below.\n",
    "    x2, y2, w2, h2 = bbox2[0], bbox2[1], bbox2[2], bbox2[3]\n",
    "\n",
    "    w_I = min(x1 + w1, x2 + w2) - max(x1, x2)\n",
    "    h_I = min(y1 + h1, y2 + h2) - max(y1, y2)\n",
    "    if w_I <= 0 or h_I <= 0:  # no overlap\n",
    "        return 0\n",
    "    I = w_I * h_I\n",
    "\n",
    "    U = w1 * h1 + w2 * h2 - I\n",
    "\n",
    "    return I / U\n",
    "\n",
    "def dist(bbox1, bbox2):\n",
    "    return np.sqrt(np.sum(np.square(bbox1[:2] - bbox2[:2])))\n",
    "\n",
    "num_epochs_flipping = 50\n",
    "num_epochs_no_flipping = 0  # has no significant effect\n",
    "\n",
    "flipped_train_y = np.array(train_y)\n",
    "flipped = np.zeros((len(train_y), num_epochs_flipping + num_epochs_no_flipping))\n",
    "ious_epoch = np.zeros((len(train_y), num_epochs_flipping + num_epochs_no_flipping))\n",
    "dists_epoch = np.zeros((len(train_y), num_epochs_flipping + num_epochs_no_flipping))\n",
    "mses_epoch = np.zeros((len(train_y), num_epochs_flipping + num_epochs_no_flipping))\n",
    "acc_shapes_epoch = np.zeros((len(train_y), num_epochs_flipping + num_epochs_no_flipping))\n",
    "acc_colors_epoch = np.zeros((len(train_y), num_epochs_flipping + num_epochs_no_flipping))\n",
    "\n",
    "flipped_test_y = np.array(test_y)\n",
    "flipped_test = np.zeros((len(test_y), num_epochs_flipping + num_epochs_no_flipping))\n",
    "ious_test_epoch = np.zeros((len(test_y), num_epochs_flipping + num_epochs_no_flipping))\n",
    "dists_test_epoch = np.zeros((len(test_y), num_epochs_flipping + num_epochs_no_flipping))\n",
    "mses_test_epoch = np.zeros((len(test_y), num_epochs_flipping + num_epochs_no_flipping))\n",
    "acc_shapes_test_epoch = np.zeros((len(test_y), num_epochs_flipping + num_epochs_no_flipping))\n",
    "acc_colors_test_epoch = np.zeros((len(test_y), num_epochs_flipping + num_epochs_no_flipping))\n",
    "\n",
    "# TODO: Calculate ious directly for all samples (using slices of the array pred_y for x, y, w, h).\n",
    "for epoch in range(num_epochs_flipping):\n",
    "    print 'Epoch', epoch\n",
    "    model.fit(train_X, flipped_train_y, nb_epoch=1, validation_data=(test_X, test_y), verbose=2)\n",
    "    pred_y = model.predict(train_X)\n",
    "\n",
    "    for sample, (pred, exp) in enumerate(zip(pred_y, flipped_train_y)):\n",
    "        \n",
    "        # TODO: Make this simpler.\n",
    "        pred = pred.reshape(num_objects, -1)\n",
    "        exp = exp.reshape(num_objects, -1)\n",
    "        \n",
    "        pred_bboxes = pred[:, :4]\n",
    "        exp_bboxes = exp[:, :4]\n",
    "        \n",
    "        ious = np.zeros((num_objects, num_objects))\n",
    "        dists = np.zeros((num_objects, num_objects))\n",
    "        mses = np.zeros((num_objects, num_objects))\n",
    "        for i, exp_bbox in enumerate(exp_bboxes):\n",
    "            for j, pred_bbox in enumerate(pred_bboxes):\n",
    "                ious[i, j] = IOU(exp_bbox, pred_bbox)\n",
    "                dists[i, j] = dist(exp_bbox, pred_bbox)\n",
    "                mses[i, j] = np.mean(np.square(exp_bbox - pred_bbox))\n",
    "                \n",
    "        new_order = np.zeros(num_objects, dtype=int)\n",
    "        \n",
    "        for i in range(num_objects):\n",
    "            # Find pred and exp bbox with maximum iou and assign them to each other (i.e. switch the positions of the exp bboxes in y).\n",
    "            ind_exp_bbox, ind_pred_bbox = np.unravel_index(ious.argmax(), ious.shape)\n",
    "            ious_epoch[sample, epoch] += ious[ind_exp_bbox, ind_pred_bbox]\n",
    "            dists_epoch[sample, epoch] += dists[ind_exp_bbox, ind_pred_bbox]\n",
    "            mses_epoch[sample, epoch] += mses[ind_exp_bbox, ind_pred_bbox]\n",
    "            ious[ind_exp_bbox] = -1  # set iou of assigned bboxes to -1, so they don't get assigned again\n",
    "            ious[:, ind_pred_bbox] = -1\n",
    "            new_order[ind_pred_bbox] = ind_exp_bbox\n",
    "        \n",
    "        flipped_train_y[sample] = exp[new_order].flatten()\n",
    "        \n",
    "        flipped[sample, epoch] = 1. - np.mean(new_order == np.arange(num_objects, dtype=int))#np.array_equal(new_order, np.arange(num_objects, dtype=int))  # TODO: Change this to reflect the number of flips.\n",
    "        ious_epoch[sample, epoch] /= num_objects\n",
    "        dists_epoch[sample, epoch] /= num_objects\n",
    "        mses_epoch[sample, epoch] /= num_objects\n",
    "        \n",
    "        acc_shapes_epoch[sample, epoch] = np.mean(np.argmax(pred[:, 4:4+num_shapes], axis=-1) == np.argmax(exp[:, 4:4+num_shapes], axis=-1))\n",
    "        acc_colors_epoch[sample, epoch] = np.mean(np.argmax(pred[:, 4+num_shapes:4+num_shapes+num_colors], axis=-1) == np.argmax(exp[:, 4+num_shapes:4+num_shapes+num_colors], axis=-1))\n",
    "\n",
    "    \n",
    "    # Calculate metrics on test data. \n",
    "    pred_test_y = model.predict(test_X)\n",
    "    # TODO: Make this simpler.\n",
    "    for sample, (pred, exp) in enumerate(zip(pred_test_y, flipped_test_y)):\n",
    "        \n",
    "        # TODO: Make this simpler.\n",
    "        pred = pred.reshape(num_objects, -1)\n",
    "        exp = exp.reshape(num_objects, -1)\n",
    "        \n",
    "        pred_bboxes = pred[:, :4]\n",
    "        exp_bboxes = exp[:, :4]\n",
    "        \n",
    "        ious = np.zeros((num_objects, num_objects))\n",
    "        dists = np.zeros((num_objects, num_objects))\n",
    "        mses = np.zeros((num_objects, num_objects))\n",
    "        for i, exp_bbox in enumerate(exp_bboxes):\n",
    "            for j, pred_bbox in enumerate(pred_bboxes):\n",
    "                ious[i, j] = IOU(exp_bbox, pred_bbox)\n",
    "                dists[i, j] = dist(exp_bbox, pred_bbox)\n",
    "                mses[i, j] = np.mean(np.square(exp_bbox - pred_bbox))\n",
    "                \n",
    "        new_order = np.zeros(num_objects, dtype=int)\n",
    "        \n",
    "        for i in range(num_objects):\n",
    "            # Find pred and exp bbox with maximum iou and assign them to each other (i.e. switch the positions of the exp bboxes in y).\n",
    "            ind_exp_bbox, ind_pred_bbox = np.unravel_index(mses.argmin(), mses.shape)\n",
    "            ious_test_epoch[sample, epoch] += ious[ind_exp_bbox, ind_pred_bbox]\n",
    "            dists_test_epoch[sample, epoch] += dists[ind_exp_bbox, ind_pred_bbox]\n",
    "            mses_test_epoch[sample, epoch] += mses[ind_exp_bbox, ind_pred_bbox]\n",
    "            mses[ind_exp_bbox] = 1000000#-1  # set iou of assigned bboxes to -1, so they don't get assigned again\n",
    "            mses[:, ind_pred_bbox] = 10000000#-1\n",
    "            new_order[ind_pred_bbox] = ind_exp_bbox\n",
    "        \n",
    "        flipped_test_y[sample] = exp[new_order].flatten()\n",
    "        \n",
    "        flipped_test[sample, epoch] = 1. - np.mean(new_order == np.arange(num_objects, dtype=int))#np.array_equal(new_order, np.arange(num_objects, dtype=int))  # TODO: Change this to reflect the number of flips.\n",
    "        ious_test_epoch[sample, epoch] /= num_objects\n",
    "        dists_test_epoch[sample, epoch] /= num_objects\n",
    "        mses_test_epoch[sample, epoch] /= num_objects\n",
    "        \n",
    "        acc_shapes_test_epoch[sample, epoch] = np.mean(np.argmax(pred[:, 4:4+num_shapes], axis=-1) == np.argmax(exp[:, 4:4+num_shapes], axis=-1))\n",
    "        acc_colors_test_epoch[sample, epoch] = np.mean(np.argmax(pred[:, 4+num_shapes:4+num_shapes+num_colors], axis=-1) == np.argmax(exp[:, 4+num_shapes:4+num_shapes+num_colors], axis=-1))\n",
    "       \n",
    "            \n",
    "    print 'Flipped {} % of all elements'.format(np.mean(flipped[:, epoch]) * 100.)\n",
    "    print 'Mean IOU: {}'.format(np.mean(ious_epoch[:, epoch]))\n",
    "    print 'Mean dist: {}'.format(np.mean(dists_epoch[:, epoch]))\n",
    "    print 'Mean mse: {}'.format(np.mean(mses_epoch[:, epoch]))\n",
    "    print 'Accuracy shapes: {}'.format(np.mean(acc_shapes_epoch[:, epoch]))\n",
    "    print 'Accuracy colors: {}'.format(np.mean(acc_colors_epoch[:, epoch]))\n",
    "    \n",
    "    print '--------------- TEST ----------------'\n",
    "    print 'Flipped {} % of all elements'.format(np.mean(flipped_test[:, epoch]) * 100.)\n",
    "    print 'Mean IOU: {}'.format(np.mean(ious_test_epoch[:, epoch]))\n",
    "    print 'Mean dist: {}'.format(np.mean(dists_test_epoch[:, epoch]))\n",
    "    print 'Mean mse: {}'.format(np.mean(mses_test_epoch[:, epoch]))\n",
    "    print 'Accuracy shapes: {}'.format(np.mean(acc_shapes_test_epoch[:, epoch]))\n",
    "    print 'Accuracy colors: {}'.format(np.mean(acc_colors_test_epoch[:, epoch]))\n",
    "    print\n",
    "    \n",
    "# print '------------------------------------'\n",
    "# print 'Training now without flipping bboxes'\n",
    "# print '------------------------------------'\n",
    "    \n",
    "# for epoch in range(num_epochs_flipping, num_epochs_flipping + num_epochs_no_flipping):\n",
    "#     print 'Epoch', epoch\n",
    "#     model.fit(train_X, flipped_train_y, nb_epoch=1, validation_data=(test_X, test_y), verbose=2)\n",
    "#     pred_y = model.predict(train_X)\n",
    "\n",
    "#     # Calculate iou/dist, but don't flip.\n",
    "#     for sample, (pred_bboxes, exp_bboxes) in enumerate(zip(pred_y, flipped_train_y)):\n",
    "        \n",
    "#         pred_bboxes = pred_bboxes.reshape(num_objects, -1)\n",
    "#         exp_bboxes = exp_bboxes.reshape(num_objects, -1)        \n",
    "        \n",
    "#         for exp_bbox, pred_bbox in zip(exp_bboxes, pred_bboxes):\n",
    "#             ious_epoch[sample, epoch] += IOU(exp_bbox, pred_bbox)\n",
    "#             dists_epoch[sample, epoch] += dist(exp_bbox, pred_bbox)\n",
    "#             mses_epoch[sample, epoch] += np.mean(np.square(exp_bbox - pred_bbox))\n",
    "            \n",
    "#         ious_epoch[sample, epoch] /= num_objects\n",
    "#         dists_epoch[sample, epoch] /= num_objects \n",
    "#         mses_epoch[sample, epoch] /= num_objects \n",
    "            \n",
    "# #     print 'Flipped {} % of all elements'.format(np.mean(flipped[:, epoch]) * 100.)\n",
    "#     print 'Mean IOU: {}'.format(np.mean(ious_epoch[:, epoch]))\n",
    "#     print 'Mean dist: {}'.format(np.mean(dists_epoch[:, epoch]))\n",
    "#     print 'Mean mse: {}'.format(np.mean(mses_epoch[:, epoch]))\n",
    "#     print\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-07-06T00:26:47.814000",
     "start_time": "2016-07-06T00:26:43.399000"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(32L, 3L, 3L, 3L)\n",
      "-0.0109495 0.117415 -0.188681 0.263553\n",
      "125.208 29.9409 79.8864 195.206\n"
     ]
    },
    {
     "data": {
      "image/png": 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dlA4yBt/IGHwjY4gDOYNvZKzERfjZLzWSnpR0fpbbqtLjxZKe7mYexyX5S1yf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\nbt6ofn9c/r8RSfrojufLXuOsYc361cxrKi5jUnQ5MzN3xb8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z7kMzu1rScqU2pXc65zaZ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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa353f6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# model.layers\n",
    "weights = model.layers[0].get_weights()[0]\n",
    "weights = weights.transpose(3, 0, 1, 2)\n",
    "print weights.shape\n",
    "# plt.imshow(weights[0] * 255. + 128., interpolation='none', origin='lower')\n",
    "print np.mean(weights[0]), np.std(weights[0]), np.min(weights[0]), np.max(weights[0])\n",
    "adj_weights = (weights * 255.) + 128.\n",
    "print np.mean(adj_weights[0]), np.std(adj_weights[0]), np.min(adj_weights[0]), np.max(adj_weights[0])\n",
    "plt.figure(figsize=(16, 8))\n",
    "for i in range(24):\n",
    "    plt.subplot(4, 6, i+1)\n",
    "    plt.imshow(adj_weights[i, :, :], interpolation='none', origin='lower', cmap='Greys')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-07-05T23:54:37.858000",
     "start_time": "2016-07-05T23:54:35.466000"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x9f3e8cf8>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xa1d82978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pcolor(flipped[:1000], cmap='Greys', vmax=1.)\n",
    "# plt.axvline(num_epochs_flipping, c='r')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Training sample')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-07-05T23:54:38.252000",
     "start_time": "2016-07-05T23:54:37.860000"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 0.6)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5M23btuXJJ5/kqquu4vDhw3z22Wflc9NqrSkpKeGKK64453MkhGh8mk6gbwQay1SCZ6td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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9f165278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean_ious_epoch = np.mean(ious_epoch, axis=0)\n",
    "mean_dists_epoch = np.mean(dists_epoch, axis=0)\n",
    "mean_mses_epoch = np.mean(mses_epoch, axis=0)\n",
    "plt.plot(mean_ious_epoch, label='Mean IOU')  # between predicted and assigned true bboxes\n",
    "plt.plot(mean_dists_epoch, label='Mean distance')  # relative to image size\n",
    "plt.plot(mean_mses_epoch, label='Mean mse')  # relative to image size\n",
    "plt.annotate(np.round(np.max(mean_ious_epoch), 3), (len(mean_ious_epoch)-1, mean_ious_epoch[-1]+0.03), horizontalalignment='right', color='b')\n",
    "plt.annotate(np.round(np.min(mean_dists_epoch), 3), (len(mean_dists_epoch)-1, mean_dists_epoch[-1]+0.03), horizontalalignment='right', color='g')\n",
    "plt.annotate(np.round(np.min(mean_mses_epoch), 3), (len(mean_mses_epoch)-1, mean_mses_epoch[-1]+0.03), horizontalalignment='right', color='r')\n",
    "\n",
    "# TEST.\n",
    "mean_ious_epoch = np.mean(ious_test_epoch, axis=0)\n",
    "mean_dists_epoch = np.mean(dists_test_epoch, axis=0)\n",
    "mean_mses_epoch = np.mean(mses_test_epoch, axis=0)\n",
    "plt.plot(mean_ious_epoch, 'b--')  # between predicted and assigned true bboxes\n",
    "plt.plot(mean_dists_epoch, 'g--')  # relative to image size\n",
    "plt.plot(mean_mses_epoch, 'r--')  # relative to image size\n",
    "# plt.annotate(np.round(np.max(mean_ious_epoch), 3), (len(mean_ious_epoch)-1, mean_ious_epoch[-1]+0.03), horizontalalignment='right', color='b')\n",
    "# plt.annotate(np.round(np.min(mean_dists_epoch), 3), (len(mean_dists_epoch)-1, mean_dists_epoch[-1]+0.03), horizontalalignment='right', color='g')\n",
    "# plt.annotate(np.round(np.min(mean_mses_epoch), 3), (len(mean_mses_epoch)-1, mean_mses_epoch[-1]+0.03), horizontalalignment='right', color='r')\n",
    "\n",
    "# plt.axvline(num_epochs_flipping, c='r')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(loc='upper left')\n",
    "plt.ylim(0, 0.6)\n",
    "\n",
    "# plt.savefig('plots/color-multiple-shapes_three-colors_bboxes.png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-07-05T23:54:40.181000",
     "start_time": "2016-07-05T23:54:38.255000"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.5, 1)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MzFdZtKX8uDjZpOtGXJzdYefz7Z8zdd1U/tbzbywYtQB357OzS3JyzM3H6dPN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\n0I46DVsUwgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9f165f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean_acc_shapes_epoch = np.mean(acc_shapes_epoch, axis=0)\n",
    "mean_acc_colors_epoch = np.mean(acc_colors_epoch, axis=0)\n",
    "plt.plot(mean_acc_shapes_epoch, label='Accuracy shapes')  # between predicted and assigned true bboxes\n",
    "plt.plot(mean_acc_colors_epoch, label='Accuracy colors')\n",
    "plt.annotate(np.round(np.max(mean_acc_shapes_epoch), 3), (len(mean_acc_shapes_epoch)-1, mean_acc_shapes_epoch[-1]+0.03), horizontalalignment='right', color='b')\n",
    "plt.annotate(np.round(np.max(mean_acc_colors_epoch), 3), (len(mean_acc_colors_epoch)-1, mean_acc_colors_epoch[-1]+0.03), horizontalalignment='right', color='g')\n",
    "\n",
    "# TEST.\n",
    "mean_acc_shapes_epoch = np.mean(acc_shapes_test_epoch, axis=0)\n",
    "mean_acc_colors_epoch = np.mean(acc_colors_test_epoch, axis=0)\n",
    "plt.plot(mean_acc_shapes_epoch, 'b--')  # between predicted and assigned true bboxes\n",
    "plt.plot(mean_acc_colors_epoch, 'g--')\n",
    "# plt.annotate(np.round(np.max(mean_acc_shapes_epoch), 3), (len(mean_acc_shapes_epoch)-1, mean_acc_shapes_epoch[-1]+0.03), horizontalalignment='right', color='b')\n",
    "# plt.annotate(np.round(np.max(mean_acc_colors_epoch), 3), (len(mean_acc_colors_epoch)-1, mean_acc_colors_epoch[-1]+0.03), horizontalalignment='right', color='g')\n",
    "\n",
    "# plt.axvline(num_epochs_flipping, c='r')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(loc='lower right')\n",
    "plt.ylim(0.5, 1)\n",
    "\n",
    "# plt.savefig('plots/color-multiple-shapes_three-colors_classification.png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-07-05T23:54:55.731000",
     "start_time": "2016-07-05T23:54:54.739000"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((10000L, 3L, 4L), (10000L, 3L), (10000L, 3L))"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_y = model.predict(test_X)\n",
    "pred_y = pred_y.reshape(len(pred_y), num_objects, -1)\n",
    "pred_bboxes = pred_y[..., :4] * img_size\n",
    "pred_shapes = np.argmax(pred_y[..., 4:4+num_shapes], axis=-1).astype(int)  # take max from probabilities\n",
    "# print pred_y[..., 4+num_shapes:4+num_shapes+num_colors].shape\n",
    "# print np.argmax(pred_y[..., 5:8], axis=-1).shape\n",
    "pred_colors = np.argmax(pred_y[..., 4+num_shapes:4+num_shapes+num_colors], axis=-1).astype(int)\n",
    "pred_bboxes.shape, pred_shapes.shape, pred_colors.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-07-05T23:54:57.198000",
     "start_time": "2016-07-05T23:54:55.733000"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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TSnKytX337tVdbISCU0Vn/bamQNAJJ0rt2zfsNGGCdOml0oABTmKbmyvNmOHZ/gFAS5O0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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6bbd2cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 8))\n",
    "for i_subplot in range(1, 9):\n",
    "    plt.subplot(2, 4, i_subplot)\n",
    "    i = np.random.randint(len(test_X))\n",
    "    plt.imshow(test_imgs[i], interpolation='none', origin='lower', extent=[0, img_size, 0, img_size])\n",
    "    for bbox, shape, color in zip(pred_bboxes[i], pred_shapes[i], pred_colors[i]):\n",
    "        plt.gca().add_patch(matplotlib.patches.Rectangle((bbox[0], bbox[1]), bbox[2], bbox[3], ec='k', fc='none'))\n",
    "        plt.annotate(shape_labels[shape], (bbox[0], bbox[1] + bbox[3] + 0.7), color=color_labels[color], clip_on=False, bbox={'fc': 'w', 'ec': 'none', 'pad': 1, 'alpha': 0.6})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-07-05T18:09:07.838000",
     "start_time": "2016-07-05T18:09:07.831000"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9.4930956365744272, 1.1636418081717976)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(pred_bboxes[:, :, 2]), np.std(pred_bboxes[:, :, 2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
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