TFQuantum_starter.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Rishit-dagli/TFUG-Mysuru-2020/blob/master/TFQuantum_starter.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting started with [TensorFlow Quantum](https://www.tensorflow.org/quantum)\n",
    "\n",
    "In this notebook you will build your first hybrid quantum classical model with \n",
    "[Cirq](https://cirq.readthedocs.io/en/stable/) and TensorFlow Quantum (TFQ). We will build a very simple model to do\n",
    "binary classification in this notebook. You will then use Keras to create a wrapper for the model and simulate it to\n",
    "train and evluate the model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Note: This notebook is designed to be run in Google Colab if you want to run it locally or on a Jupyter notebook you \n",
    "would skip the code cells with the `Colab only` comment."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Install TensorFlow 2.x (Colab only)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Colab only\n",
    "pip install -q tensorflow==2.1.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Install TensorFlow Quantum (Colab only)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Colab only\n",
    "pip install -q tensorflow-quantum"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imports"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now import TensorFlow and the module dependencies:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cirq\n",
    "import random\n",
    "import numpy as np\n",
    "import sympy\n",
    "import tensorflow as tf\n",
    "import tensorflow_quantum as tfq\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from cirq.contrib.svg import SVGCircuit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Place a qubit on the grid\n",
    "\n",
    "You will then place a qubit on thee grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "qubit = cirq.GridQubit(0, 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare quantum data\n",
    "\n",
    "The first thing you would do is set up the labels and parameters for preparation of the quantum data. For simplicity\n",
    "here we have included just 2 data points `a` and `b`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "expected_labels = np.array([[1, 0], [0, 1]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Randonly rotate `x` and `z` axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "angle = np.random.uniform(0, 2 * np.pi)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building the quantum Circuit\n",
    "\n",
    "You will now build the quantum circuit and also convert it into a tensor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = cirq.Circuit(cirq.ry(angle)(qubit))\n",
    "b = cirq.Circuit(cirq.ry(angle + np.pi / 2)(qubit))\n",
    "quantum_data = tfq.convert_to_tensor([a, b])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Font family ['Arial'] not found. Falling back to DejaVu Sans.\n"
     ]
    },
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      ],
      "text/plain": [
       "<cirq.contrib.svg.svg.SVGCircuit at 0x7fb4a056a950>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SVGCircuit(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg xmlns=\"http://www.w3.org/2000/svg\" width=\"209.76\" height=\"50.0\"><line x1=\"34.7588671875\" x2=\"179.76\" y1=\"25.0\" y2=\"25.0\" stroke=\"#1967d2\" stroke-width=\"1\" /><rect x=\"10.0\" y=\"5.0\" width=\"49.517734375\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"0\" /><text x=\"34.7588671875\" y=\"25.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\">(0, 0): </text><rect x=\"79.517734375\" y=\"5.0\" width=\"80.242265625\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"119.63886718750001\" y=\"25.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\">Ry(-1.73π)</text></svg>"
      ],
      "text/plain": [
       "<cirq.contrib.svg.svg.SVGCircuit at 0x7fb416f02350>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SVGCircuit(b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building the hybrid model\n",
    "\n",
    "This section also shows the interoperatability between TensorFlow and Cirq. With the TFQ PQC layer you can easily\n",
    "embed your quantum part of the model within a standard classical Keras model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "q_data_input = tf.keras.Input(shape = (), dtype = tf.dtypes.string)\n",
    "theta = sympy.Symbol(\"theta\")\n",
    "q_model = cirq.Circuit(cirq.ry(theta)(qubit))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "expectation = tfq.layers.PQC(q_model, cirq.Z(qubit))\n",
    "expectation_output = expectation(q_data_input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier = tf.keras.layers.Dense(2, activation = tf.keras.activations.softmax)\n",
    "classifier_output = classifier(expectation_output)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You will now define the optimizer and loss functions for your model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = tf.keras.Model(inputs = q_data_input, \n",
    "                       outputs = classifier_output)\n",
    "model.compile(optimizer = tf.keras.optimizers.Adam(learning_rate = 0.1), \n",
    "              loss = tf.keras.losses.CategoricalCrossentropy())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Traaining the model\n",
    "\n",
    "Training the model is just like training any other Keras model and is made easy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 2 samples\n",
      "Epoch 1/250\n",
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      "Epoch 125/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0053\n",
      "Epoch 126/250\n",
      "2/2 [==============================] - 0s 5ms/sample - loss: 0.0052\n",
      "Epoch 127/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0052\n",
      "Epoch 128/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0051\n",
      "Epoch 129/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0051\n",
      "Epoch 130/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0050\n",
      "Epoch 131/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0050\n",
      "Epoch 132/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0049\n",
      "Epoch 133/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0049\n",
      "Epoch 134/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0049\n",
      "Epoch 135/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0048\n",
      "Epoch 136/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0048\n",
      "Epoch 137/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0047\n",
      "Epoch 138/250\n",
      "2/2 [==============================] - 0s 4ms/sample - loss: 0.0047\n",
      "Epoch 139/250\n",
      "2/2 [==============================] - 0s 5ms/sample - loss: 0.0046\n",
      "Epoch 140/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0046\n",
      "Epoch 141/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0046\n",
      "Epoch 142/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0045\n",
      "Epoch 143/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0045\n",
      "Epoch 144/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0045\n",
      "Epoch 145/250\n",
      "2/2 [==============================] - 0s 4ms/sample - loss: 0.0044\n",
      "Epoch 146/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0044\n",
      "Epoch 147/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0043\n",
      "Epoch 148/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0043\n",
      "Epoch 149/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0043\n",
      "Epoch 150/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0042\n",
      "Epoch 151/250\n",
      "2/2 [==============================] - 0s 4ms/sample - loss: 0.0042\n",
      "Epoch 152/250\n",
      "2/2 [==============================] - 0s 5ms/sample - loss: 0.0042\n",
      "Epoch 153/250\n",
      "2/2 [==============================] - 0s 4ms/sample - loss: 0.0041\n",
      "Epoch 154/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0041\n",
      "Epoch 155/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0041\n",
      "Epoch 156/250\n",
      "2/2 [==============================] - 0s 4ms/sample - loss: 0.0040\n",
      "Epoch 157/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0040\n",
      "Epoch 158/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0040\n",
      "Epoch 159/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0039\n",
      "Epoch 160/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0039\n",
      "Epoch 161/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0039\n",
      "Epoch 162/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0038\n",
      "Epoch 163/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0038\n",
      "Epoch 164/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0038\n",
      "Epoch 165/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0038\n",
      "Epoch 166/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0037\n",
      "Epoch 167/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0037\n",
      "Epoch 168/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0037\n",
      "Epoch 169/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0036\n",
      "Epoch 170/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0036\n",
      "Epoch 171/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0036\n",
      "Epoch 172/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0036\n",
      "Epoch 173/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0035\n",
      "Epoch 174/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0035\n",
      "Epoch 175/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0035\n",
      "Epoch 176/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0035\n",
      "Epoch 177/250\n",
      "2/2 [==============================] - 0s 4ms/sample - loss: 0.0034\n",
      "Epoch 178/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0034\n",
      "Epoch 179/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0034\n",
      "Epoch 180/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0034\n",
      "Epoch 181/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0033\n",
      "Epoch 182/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0033\n",
      "Epoch 183/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0033\n",
      "Epoch 184/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0033\n",
      "Epoch 185/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0032\n",
      "Epoch 186/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0032\n",
      "Epoch 187/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0032\n",
      "Epoch 188/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0032\n",
      "Epoch 189/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0032\n",
      "Epoch 190/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0031\n",
      "Epoch 191/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0031\n",
      "Epoch 192/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0031\n",
      "Epoch 193/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0031\n",
      "Epoch 194/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0030\n",
      "Epoch 195/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0030\n",
      "Epoch 196/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0030\n",
      "Epoch 197/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0030\n",
      "Epoch 198/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0030\n",
      "Epoch 199/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0029\n",
      "Epoch 200/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0029\n",
      "Epoch 201/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0029\n",
      "Epoch 202/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0029\n",
      "Epoch 203/250\n",
      "2/2 [==============================] - 0s 4ms/sample - loss: 0.0029\n",
      "Epoch 204/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0029\n",
      "Epoch 205/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0028\n",
      "Epoch 206/250\n",
      "2/2 [==============================] - 0s 5ms/sample - loss: 0.0028\n",
      "Epoch 207/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0028\n",
      "Epoch 208/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0028\n",
      "Epoch 209/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0028\n",
      "Epoch 210/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0027\n",
      "Epoch 211/250\n",
      "2/2 [==============================] - 0s 4ms/sample - loss: 0.0027\n",
      "Epoch 212/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0027\n",
      "Epoch 213/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0027\n",
      "Epoch 214/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0027\n",
      "Epoch 215/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0027\n",
      "Epoch 216/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0026\n",
      "Epoch 217/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0026\n",
      "Epoch 218/250\n",
      "2/2 [==============================] - 0s 4ms/sample - loss: 0.0026\n",
      "Epoch 219/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0026\n",
      "Epoch 220/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0026\n",
      "Epoch 221/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0026\n",
      "Epoch 222/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0025\n",
      "Epoch 223/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0025\n",
      "Epoch 224/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0025\n",
      "Epoch 225/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0025\n",
      "Epoch 226/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0025\n",
      "Epoch 227/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0025\n",
      "Epoch 228/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0025\n",
      "Epoch 229/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0024\n",
      "Epoch 230/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0024\n",
      "Epoch 231/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0024\n",
      "Epoch 232/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0024\n",
      "Epoch 233/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0024\n",
      "Epoch 234/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0024\n",
      "Epoch 235/250\n",
      "2/2 [==============================] - 0s 4ms/sample - loss: 0.0024\n",
      "Epoch 236/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0023\n",
      "Epoch 237/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0023\n",
      "Epoch 238/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0023\n",
      "Epoch 239/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0023\n",
      "Epoch 240/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0023\n",
      "Epoch 241/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0023\n",
      "Epoch 242/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0023\n",
      "Epoch 243/250\n",
      "2/2 [==============================] - 0s 4ms/sample - loss: 0.0023\n",
      "Epoch 244/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0022\n",
      "Epoch 245/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0022\n",
      "Epoch 246/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0022\n",
      "Epoch 247/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0022\n",
      "Epoch 248/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0022\n",
      "Epoch 249/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0022\n",
      "Epoch 250/250\n",
      "2/2 [==============================] - 0s 3ms/sample - loss: 0.0022\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(x = quantum_data, \n",
    "                    y = expected_labels, \n",
    "                    epochs = 250)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluating the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Performing inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "noise = np.random.uniform(-0.25, 0.25, 2)\n",
    "test_data = tfq.convert_to_tensor([\n",
    "    cirq.Circuit(cirq.ry(noise[0])(qubit)),\n",
    "    cirq.Circuit(cirq.ry(noise[1] + np.pi/2)(qubit)),    \n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see in the below cell that our model does a good job with this data though it was very easy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[9.0111643e-01, 9.8883577e-02],\n",
       "       [1.7436201e-04, 9.9982566e-01]], dtype=float32)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = model.predict(test_data)\n",
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "environment": {
   "name": "tf2-gpu.2-1.m47",
   "type": "gcloud",
   "uri": "gcr.io/deeplearning-platform-release/tf2-gpu.2-1:m47"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}