mnist_experimental.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "xLOXFOT5Q40E"
   },
   "source": [
    "##### Copyright 2020 The TensorFlow Authors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "cellView": "form",
    "colab": {},
    "colab_type": "code",
    "id": "iiQkM5ZgQ8r2",
    "tags": []
   },
   "outputs": [],
   "source": [
    "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#\n",
    "# https://www.apache.org/licenses/LICENSE-2.0\n",
    "#\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "j6331ZSsQGY3"
   },
   "source": [
    "# MNIST classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "i9Jcnb8bQQyd"
   },
   "source": [
    "Based on https://www.tensorflow.org/quantum/tutorials/mnist"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "udLObUVeGfTs"
   },
   "source": [
    "We build a quantum neural network (QNN) to classify a simplified version of MNIST, similar to the approach used in <a href=\"https://arxiv.org/pdf/1802.06002.pdf\" class=\"external\">Farhi et al</a>. The performance of the quantum neural network on this classical data problem is compared with a classical neural network."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "X35qHdh5Gzqg"
   },
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "TorxE5tnkvb2",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "tfq-nightly 0.5.0.dev20210516 requires grpcio==1.30.0, but you have grpcio 1.32.0 which is incompatible.\u001b[0m\n",
      "\u001b[33mWARNING: You are using pip version 21.1.1; however, version 21.1.2 is available.\n",
      "You should consider upgrading via the '/mnt/c/Users/mmints/Documents/Stanford/CS230/project/CS230_quantum_dropout/qenv_project/bin/python3.8 -m pip install --upgrade pip' command.\u001b[0m\n",
      "Requirement already satisfied: tensorboard_plugin_profile==2.3.0 in ./qenv_project/lib/python3.8/site-packages (2.3.0)\n",
      "Requirement already satisfied: protobuf>=3.6.0 in ./qenv_project/lib/python3.8/site-packages (from tensorboard_plugin_profile==2.3.0) (3.13.0)\n",
      "Requirement already satisfied: six>=1.10.0 in ./qenv_project/lib/python3.8/site-packages (from tensorboard_plugin_profile==2.3.0) (1.15.0)\n",
      "Requirement already satisfied: gviz-api>=1.9.0 in ./qenv_project/lib/python3.8/site-packages (from tensorboard_plugin_profile==2.3.0) (1.9.0)\n",
      "Requirement already satisfied: werkzeug>=0.11.15 in ./qenv_project/lib/python3.8/site-packages (from tensorboard_plugin_profile==2.3.0) (2.0.0)\n",
      "Requirement already satisfied: setuptools>=41.0.0 in ./qenv_project/lib/python3.8/site-packages (from tensorboard_plugin_profile==2.3.0) (56.2.0)\n",
      "\u001b[33mWARNING: You are using pip version 21.1.1; however, version 21.1.2 is available.\n",
      "You should consider upgrading via the '/mnt/c/Users/mmints/Documents/Stanford/CS230/project/CS230_quantum_dropout/qenv_project/bin/python3.8 -m pip install --upgrade pip' command.\u001b[0m\n",
      "The tensorboard extension is already loaded. To reload it, use:\n",
      "  %reload_ext tensorboard\n"
     ]
    }
   ],
   "source": [
    "#!pip3 install -q tensorflow==2.3.1\n",
    "!python3.8 -m pip install -q tensorflow==2.4.1\n",
    "!python3.8 -m pip install tensorboard_plugin_profile==2.3.0\n",
    "%load_ext tensorboard"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "FxkQA6oblNqI"
   },
   "source": [
    "Install TensorFlow Quantum:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "saFHsRDpkvkH",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Collecting grpcio==1.30.0\n",
      "  Using cached grpcio-1.30.0-cp38-cp38-manylinux2010_x86_64.whl (3.0 MB)\n",
      "Requirement already satisfied: google-api-core==1.21.0 in ./qenv_project/lib/python3.8/site-packages (from tfq-nightly) (1.21.0)\n",
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      "Requirement already satisfied: cirq-google==0.11.0 in ./qenv_project/lib/python3.8/site-packages (from cirq==0.11.0->tfq-nightly) (0.11.0)\n",
      "Requirement already satisfied: networkx~=2.4 in ./qenv_project/lib/python3.8/site-packages (from cirq-core==0.11.0->cirq==0.11.0->tfq-nightly) (2.5.1)\n",
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      "Requirement already satisfied: tqdm in ./qenv_project/lib/python3.8/site-packages (from cirq-core==0.11.0->cirq==0.11.0->tfq-nightly) (4.60.0)\n",
      "Requirement already satisfied: numpy~=1.16 in ./qenv_project/lib/python3.8/site-packages (from cirq-core==0.11.0->cirq==0.11.0->tfq-nightly) (1.19.5)\n",
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      "Requirement already satisfied: setuptools>=34.0.0 in ./qenv_project/lib/python3.8/site-packages (from google-api-core==1.21.0->tfq-nightly) (56.2.0)\n",
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      "Requirement already satisfied: mpmath>=0.19 in ./qenv_project/lib/python3.8/site-packages (from sympy==1.5->tfq-nightly) (1.2.1)\n",
      "Requirement already satisfied: cycler>=0.10 in ./qenv_project/lib/python3.8/site-packages (from matplotlib~=3.0->cirq-core==0.11.0->cirq==0.11.0->tfq-nightly) (0.10.0)\n",
      "Requirement already satisfied: pyparsing>=2.2.1 in ./qenv_project/lib/python3.8/site-packages (from matplotlib~=3.0->cirq-core==0.11.0->cirq==0.11.0->tfq-nightly) (2.4.7)\n",
      "Requirement already satisfied: pillow>=6.2.0 in ./qenv_project/lib/python3.8/site-packages (from matplotlib~=3.0->cirq-core==0.11.0->cirq==0.11.0->tfq-nightly) (8.2.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in ./qenv_project/lib/python3.8/site-packages (from matplotlib~=3.0->cirq-core==0.11.0->cirq==0.11.0->tfq-nightly) (1.3.1)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in ./qenv_project/lib/python3.8/site-packages (from matplotlib~=3.0->cirq-core==0.11.0->cirq==0.11.0->tfq-nightly) (2.8.1)\n",
      "Requirement already satisfied: decorator<5,>=4.3 in ./qenv_project/lib/python3.8/site-packages (from networkx~=2.4->cirq-core==0.11.0->cirq==0.11.0->tfq-nightly) (4.4.2)\n",
      "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in ./qenv_project/lib/python3.8/site-packages (from pyasn1-modules>=0.2.1->google-auth==1.18.0->tfq-nightly) (0.4.8)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in ./qenv_project/lib/python3.8/site-packages (from requests~=2.18->cirq-core==0.11.0->cirq==0.11.0->tfq-nightly) (2020.12.5)\n",
      "Requirement already satisfied: chardet<5,>=3.0.2 in ./qenv_project/lib/python3.8/site-packages (from requests~=2.18->cirq-core==0.11.0->cirq==0.11.0->tfq-nightly) (4.0.0)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in ./qenv_project/lib/python3.8/site-packages (from requests~=2.18->cirq-core==0.11.0->cirq==0.11.0->tfq-nightly) (1.26.4)\n",
      "Requirement already satisfied: idna<3,>=2.5 in ./qenv_project/lib/python3.8/site-packages (from requests~=2.18->cirq-core==0.11.0->cirq==0.11.0->tfq-nightly) (2.10)\n",
      "Installing collected packages: grpcio\n",
      "  Attempting uninstall: grpcio\n",
      "    Found existing installation: grpcio 1.32.0\n",
      "    Uninstalling grpcio-1.32.0:\n",
      "      Successfully uninstalled grpcio-1.32.0\n",
      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "tensorflow 2.4.1 requires grpcio~=1.32.0, but you have grpcio 1.30.0 which is incompatible.\u001b[0m\n",
      "Successfully installed grpcio-1.30.0\n",
      "\u001b[33mWARNING: You are using pip version 21.1.1; however, version 21.1.2 is available.\n",
      "You should consider upgrading via the '/mnt/c/Users/mmints/Documents/Stanford/CS230/project/CS230_quantum_dropout/qenv_project/bin/python3.8 -m pip install --upgrade pip' command.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "#!pip3 install -q tensorflow-quantum\n",
    "!python3.8 -m pip install tfq-nightly"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "hdgMMZEBGqyl"
   },
   "source": [
    "Now import TensorFlow and the module dependencies:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "enZ300Bflq80",
    "tags": []
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import tensorflow_quantum as tfq\n",
    "\n",
    "import cirq\n",
    "import sympy\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import collections\n",
    "import datetime\n",
    "# visualization tools\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from cirq.contrib.svg import SVGCircuit\n",
    "\n",
    "# set the random seed\n",
    "tf.random.set_seed(137)\n",
    "np.random.seed(137)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "b08Mmbs8lr81"
   },
   "source": [
    "## 1. Load the data\n",
    "\n",
    "In this tutorial you will build a binary classifier to distinguish between the digits 3 and 6, following <a href=\"https://arxiv.org/pdf/1802.06002.pdf\" class=\"external\">Farhi et al.</a> This section covers the data handling that:\n",
    "\n",
    "- Loads the raw data from Keras.\n",
    "- Filters the dataset to only 3s and 6s.\n",
    "- Downscales the images so they fit can fit in a quantum computer.\n",
    "- Removes any contradictory examples.\n",
    "- Converts the binary images to Cirq circuits.\n",
    "- Converts the Cirq circuits to TensorFlow Quantum circuits. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "pDUdGxn-ojgy"
   },
   "source": [
    "### 1.1 Load the raw data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "xZyGXlaKojgz"
   },
   "source": [
    "Load the MNIST dataset distributed with Keras. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "d9OSExvCojg0",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of original training examples: 60000\n",
      "Number of original test examples: 10000\n"
     ]
    }
   ],
   "source": [
    "(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()\n",
    "\n",
    "# Rescale the images from [0,255] to the [0.0,1.0] range.\n",
    "x_train, x_test = x_train[..., np.newaxis]/255.0, x_test[..., np.newaxis]/255.0\n",
    "\n",
    "print(\"Number of original training examples:\", len(x_train))\n",
    "print(\"Number of original test examples:\", len(x_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "fZpbygdGojg3"
   },
   "source": [
    "Filter the dataset to keep just the 3s and 6s,  remove the other classes. At the same time convert the label, `y`, to boolean: `True` for `3` and `False` for 6. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "hOw68cCZojg4",
    "tags": []
   },
   "outputs": [],
   "source": [
    "def filter_36(x, y):\n",
    "    keep = (y == 3) | (y == 6)\n",
    "    x, y = x[keep], y[keep]\n",
    "    y = y == 3\n",
    "    return x,y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "p-XEU8egGL6q",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of filtered training examples: 12049\n",
      "Number of filtered test examples: 1968\n"
     ]
    }
   ],
   "source": [
    "x_train, y_train = filter_36(x_train, y_train)\n",
    "x_test, y_test = filter_36(x_test, y_test)\n",
    "\n",
    "print(\"Number of filtered training examples:\", len(x_train))\n",
    "print(\"Number of filtered test examples:\", len(x_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3wyiaP0Xojg_"
   },
   "source": [
    "Show the first example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "j5STP7MbojhA",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7fdf1bec2b20>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(y_train[0])\n",
    "\n",
    "plt.imshow(x_train[0, :, :, 0])\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "wNS9sVPQojhC"
   },
   "source": [
    "### 1.2 Downscale the images"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "fmmtplIFGL6t"
   },
   "source": [
    "An image size of 28x28 is much too large for current quantum computers. Resize the image down to 4x4:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "lbhUdBFWojhE",
    "tags": []
   },
   "outputs": [],
   "source": [
    "x_train_small = tf.image.resize(x_train, (4,4)).numpy()\n",
    "x_test_small = tf.image.resize(x_test, (4,4)).numpy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "pOMd7zIjGL6x"
   },
   "source": [
    "Again, display the first training example—after resize: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "YIYOtCRIGL6y",
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7fdf1be7d310>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(y_train[0])\n",
    "\n",
    "plt.imshow(x_train_small[0,:,:,0], vmin=0, vmax=1)\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "gGeF1_qtojhK"
   },
   "source": [
    "### 1.3 Remove contradictory examples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7ZLkq2yeojhL"
   },
   "source": [
    "From section *3.3 Learning to Distinguish Digits* of <a href=\"https://arxiv.org/pdf/1802.06002.pdf\" class=\"external\">Farhi et al.</a>, filter the dataset to remove images that are labeled as belonging to both classes.\n",
    "\n",
    "This is not a standard machine-learning procedure, but is included in the interest of following the paper."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "LqOPW0C7ojhL",
    "tags": []
   },
   "outputs": [],
   "source": [
    "def remove_contradicting(xs, ys):\n",
    "    mapping = collections.defaultdict(set)\n",
    "    orig_x = {}\n",
    "    # Determine the set of labels for each unique image:\n",
    "    for x,y in zip(xs,ys):\n",
    "       orig_x[tuple(x.flatten())] = x\n",
    "       mapping[tuple(x.flatten())].add(y)\n",
    "    \n",
    "    new_x = []\n",
    "    new_y = []\n",
    "    for flatten_x in mapping:\n",
    "      x = orig_x[flatten_x]\n",
    "      labels = mapping[flatten_x]\n",
    "      if len(labels) == 1:\n",
    "          new_x.append(x)\n",
    "          new_y.append(next(iter(labels)))\n",
    "      else:\n",
    "          # Throw out images that match more than one label.\n",
    "          pass\n",
    "    \n",
    "    num_uniq_3 = sum(1 for value in mapping.values() if len(value) == 1 and True in value)\n",
    "    num_uniq_6 = sum(1 for value in mapping.values() if len(value) == 1 and False in value)\n",
    "    num_uniq_both = sum(1 for value in mapping.values() if len(value) == 2)\n",
    "\n",
    "    print(\"Number of unique images:\", len(mapping.values()))\n",
    "    print(\"Number of unique 3s: \", num_uniq_3)\n",
    "    print(\"Number of unique 6s: \", num_uniq_6)\n",
    "    print(\"Number of unique contradicting labels (both 3 and 6): \", num_uniq_both)\n",
    "    print()\n",
    "    print(\"Initial number of images: \", len(xs))\n",
    "    print(\"Remaining non-contradicting unique images: \", len(new_x))\n",
    "    \n",
    "    return np.array(new_x), np.array(new_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "VMOiJfz_ojhP"
   },
   "source": [
    "The resulting counts do not closely match the reported values, but the exact procedure is not specified.\n",
    "\n",
    "It is also worth noting here that applying filtering contradictory examples at this point does not totally prevent the model from receiving contradictory training examples: the next step binarizes the data which will cause more collisions. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zpnsAssWojhP",
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of unique images: 10387\n",
      "Number of unique 3s:  4912\n",
      "Number of unique 6s:  5426\n",
      "Number of unique contradicting labels (both 3 and 6):  49\n",
      "\n",
      "Initial number of images:  12049\n",
      "Remaining non-contradicting unique images:  10338\n"
     ]
    }
   ],
   "source": [
    "x_train_nocon, y_train_nocon = remove_contradicting(x_train_small, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10538, 4, 4, 1)\n",
      "(10538,)\n"
     ]
    }
   ],
   "source": [
    "# Duplicate some entries to illustrate the effects of dropout! Run this cell only if testing dropout\n",
    "NUM_DUPLICATE = 200\n",
    "\n",
    "for i in range(NUM_DUPLICATE):\n",
    "    x_train_nocon = np.insert(x_train_nocon, 0, x_train_nocon[0], axis=0)\n",
    "    y_train_nocon = np.insert(y_train_nocon, 0, y_train_nocon[0], axis=0)\n",
    "print(x_train_nocon.shape)\n",
    "print(y_train_nocon.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "SlJ5NVaPojhT"
   },
   "source": [
    "### 1.4 Encode the data as quantum circuits\n",
    "\n",
    "To process images using a quantum computer, <a href=\"https://arxiv.org/pdf/1802.06002.pdf\" class=\"external\">Farhi et al.</a> proposed representing each pixel with a qubit, with the state depending on the value of the pixel. The first step is to convert to a binary encoding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "1z8J7OyDojhV",
    "tags": []
   },
   "outputs": [],
   "source": [
    "THRESHOLD = 0.5\n",
    "\n",
    "x_train_bin = np.array(x_train_nocon > THRESHOLD, dtype=np.float32)\n",
    "x_test_bin = np.array(x_test_small > THRESHOLD, dtype=np.float32)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "SlJ5NVaPojhU"
   },
   "source": [
    "If you were to remove contradictory images at this point you would be left with only 193, likely not enough for effective training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "1z8J7OyDojhW",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of unique images: 193\n",
      "Number of unique 3s:  80\n",
      "Number of unique 6s:  69\n",
      "Number of unique contradicting labels (both 3 and 6):  44\n",
      "\n",
      "Initial number of images:  10538\n",
      "Remaining non-contradicting unique images:  149\n"
     ]
    }
   ],
   "source": [
    "_ = remove_contradicting(x_train_bin, y_train_nocon)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "oLyxS9KlojhZ"
   },
   "source": [
    "The qubits at pixel indices with values that exceed a threshold, are rotated through an $X$ gate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "aOu_3-3ZGL61",
    "tags": []
   },
   "outputs": [],
   "source": [
    "def convert_to_circuit(image):\n",
    "    \"\"\"Encode truncated classical image into quantum datapoint.\"\"\"\n",
    "    values = np.ndarray.flatten(image)\n",
    "    qubits = cirq.GridQubit.rect(4, 4)\n",
    "    circuit = cirq.Circuit()\n",
    "    for i, value in enumerate(values):\n",
    "        if value:\n",
    "            circuit.append(cirq.X(qubits[i]))\n",
    "    return circuit\n",
    "\n",
    "\n",
    "x_train_circ = [convert_to_circuit(x) for x in x_train_bin]\n",
    "x_test_circ = [convert_to_circuit(x) for x in x_test_bin]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "zSCXqzOzojhd"
   },
   "source": [
    "Here is the circuit created for the first example (circuit diagrams do not show qubits with zero gates):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "w3POmUEUojhe",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg xmlns=\"http://www.w3.org/2000/svg\" width=\"169.517734375\" height=\"100.0\"><line x1=\"34.7588671875\" x2=\"139.517734375\" y1=\"25.0\" y2=\"25.0\" stroke=\"#1967d2\" stroke-width=\"1\" /><line x1=\"34.7588671875\" x2=\"139.517734375\" y1=\"75.0\" y2=\"75.0\" 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\" font-family=\"Arial\">(2, 2): </text><rect x=\"10.0\" y=\"55.0\" width=\"49.517734375\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"0\" /><text x=\"34.7588671875\" y=\"75.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">(3, 1): </text><rect x=\"79.517734375\" y=\"5.0\" width=\"40\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"99.517734375\" y=\"25.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"18px\" font-family=\"Arial\">X</text><rect x=\"79.517734375\" y=\"55.0\" width=\"40\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"99.517734375\" y=\"75.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"18px\" font-family=\"Arial\">X</text></svg>"
      ],
      "text/plain": [
       "<cirq.contrib.svg.svg.SVGCircuit at 0x7fdf9cb686d0>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SVGCircuit(x_train_circ[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "AEQMxCcBojhg"
   },
   "source": [
    "Compare this circuit to the indices where the image value exceeds the threshold:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "TBIsiXdtojhh",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 2],\n",
       "       [3, 1]])"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bin_img = x_train_bin[0,:,:,0]\n",
    "indices = np.array(np.where(bin_img)).T\n",
    "indices"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "mWZ24w1Oojhk"
   },
   "source": [
    "Convert these `Cirq` circuits to tensors for `tfq`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "IZStEMk4ojhk",
    "tags": []
   },
   "outputs": [],
   "source": [
    "x_train_tfcirc = tfq.convert_to_tensor(x_train_circ)\n",
    "x_test_tfcirc = tfq.convert_to_tensor(x_test_circ)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "4USiqeOqGL67"
   },
   "source": [
    "## 2. Quantum neural network\n",
    "\n",
    "There is little guidance for a quantum circuit structure that classifies images. Since the classification is based on the expectation of the readout qubit, <a href=\"https://arxiv.org/pdf/1802.06002.pdf\" class=\"external\">Farhi et al.</a> propose using two qubit gates, with the readout qubit always acted upon. This is similar in some ways to running small a <a href=\"https://arxiv.org/abs/1511.06464\" class=\"external\">Unitary RNN</a> across the pixels."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Helper functions for Tensorboard.\n",
    "\n",
    "\n",
    "- The integer values of samples, so that you can create histograms of the distribution.\n",
    "- The linear XEB fidelity estimate of a set of samples, to give some indication of how \"truly quantum random\" the samples are.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "@tf.function\n",
    "def bits_to_ints(bits):\n",
    "    \"\"\"Convert tensor of bitstrings to tensor of ints.\"\"\"\n",
    "    sigs = tf.constant([1 << i for i in range(N_QUBITS)], dtype=tf.int32)\n",
    "    rounded_bits = tf.clip_by_value(tf.math.round(\n",
    "        tf.cast(bits, dtype=tf.dtypes.float32)), clip_value_min=0, clip_value_max=1)\n",
    "    return tf.einsum('jk,k->j', tf.cast(rounded_bits, dtype=tf.dtypes.int32), sigs)\n",
    "\n",
    "@tf.function\n",
    "def xeb_fid(bits):\n",
    "    \"\"\"Compute linear XEB fidelity of bitstrings.\"\"\"\n",
    "    final_probs = tf.squeeze(\n",
    "        tf.abs(tfq.layers.State()(REFERENCE_CIRCUIT).to_tensor()) ** 2)\n",
    "    nums = bits_to_ints(bits)\n",
    "    return (2 ** N_QUBITS) * tf.reduce_mean(tf.gather(final_probs, nums)) - 1.0\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "knIzawEeojho"
   },
   "source": [
    "### 2.1 Build the model circuit\n",
    "\n",
    "This following example shows this layered approach. Each layer uses *n* instances of the same gate, with each of the data qubits acting on the readout qubit.\n",
    "\n",
    "Start with a simple class that will add a layer of these gates to a circuit:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-hjxxgU5ojho",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys([cirq.X, cirq.XX, cirq.Y, cirq.YY, cirq.Z, cirq.ZZ, cirq.H, cirq.CZ, cirq.CNOT, cirq.SWAP, cirq.ISWAP, cirq.PhasedXPowGate(phase_exponent=0.123), cirq.PhasedISwapPowGate(phase_exponent=0.123), cirq.FSimGate(theta=0.123, phi=0.456), cirq.I])\n",
      "dict_keys([cirq.depolarize(p=0.01), cirq.asymmetric_depolarize(error_probabilities={'I': 0.94, 'X': 0.01, 'Y': 0.02, 'Z': 0.03}), cirq.generalized_amplitude_damp(p=0.01,gamma=0.02), cirq.amplitude_damp(gamma=0.01), cirq.ResetChannel(), cirq.phase_damp(gamma=0.01), cirq.phase_flip(p=0.01), cirq.bit_flip(p=0.01)])\n"
     ]
    }
   ],
   "source": [
    "print(tfq.util.get_supported_gates().keys())\n",
    "print(tfq.util.get_supported_channels().keys())\n",
    "\n",
    "class CircuitLayerBuilder():\n",
    "    def __init__(self, circuit, data_qubits, readout):\n",
    "        \n",
    "        self.data_qubits = data_qubits\n",
    "        self.readout = readout\n",
    "        self.curr_layer_id = 0\n",
    "        self.circuit = circuit\n",
    "        \n",
    "        # maps str(symbol) to (is_dropped_out, weight)\n",
    "        self.symbol_map = {}\n",
    "        \n",
    "        self.drop_out_applied = False\n",
    "        \n",
    "    def rebuild(self, circuit, data_qubits, readout, computed_weights):\n",
    "        # prev_weights is the result of get_weights from the prior epoch.\n",
    "        \n",
    "        self.data_qubits = data_qubits\n",
    "        self.readout = readout\n",
    "        self.curr_layer_id = 0\n",
    "        self.circuit = circuit\n",
    "        \n",
    "        j = 0\n",
    "        \n",
    "        #print('Rebuilding; prev self.symbol_map:')\n",
    "        #print(self.symbol_map)\n",
    "        \n",
    "        for k in sorted(self.symbol_map.keys()):\n",
    "            prev_is_dropped_out, _ = self.symbol_map[k]\n",
    "            if not prev_is_dropped_out:\n",
    "                # Re-use the prev weight. This isn't directly used as TFQ inputs, but we later use it to generate\n",
    "                # sane inputs for set_weights().\n",
    "                # Also, is-dropped-out is True by default to handle the case when a rebuilt circuit might not add a layer for some reason\n",
    "                self.symbol_map[k] = (True, computed_weights[j])\n",
    "\n",
    "                # prev_weights is a list that only has entries for non-dropped-out elems\n",
    "                j += 1\n",
    "                \n",
    "        #print('Rebuilding done; updated self.symbol_map:')\n",
    "        #print(self.symbol_map)\n",
    "    \n",
    "    def add_layer(self, gate, apply_dropout=False):\n",
    "        # at most one dropped qubit added per layer\n",
    "        if apply_dropout and self.curr_layer_id > 0:\n",
    "            keep_prob = 0.8\n",
    "            D1 = np.random.rand(len(self.data_qubits), 1)\n",
    "            D1 = (D1 < keep_prob).astype(int)\n",
    "        \n",
    "        for i in range(len(self.data_qubits)):\n",
    "            qubit = self.data_qubits[i]\n",
    "            symbol = sympy.Symbol('sym_l' + str(self.curr_layer_id) + '_q' + str(i))\n",
    "            \n",
    "            if str(symbol) in self.symbol_map:\n",
    "                _, weight = self.symbol_map[str(symbol)]\n",
    "            else:\n",
    "                # default value for weight is 0\n",
    "                weight = 0\n",
    "\n",
    "            if apply_dropout and self.curr_layer_id > 0 and D1[i] == 0:\n",
    "                print(\"Dropped out qubit id \" + str(i))\n",
    "                self.symbol_map[str(symbol)] = (True, weight)\n",
    "                self.drop_out_applied = True\n",
    "                \n",
    "                assert str(symbol) in self.symbol_map\n",
    "                \n",
    "                self.circuit.append(gate(qubit, self.readout)**weight) # freeze current weight if dropout\n",
    "            else:\n",
    "                self.symbol_map[str(symbol)] = (False, weight)\n",
    "                self.circuit.append(gate(qubit, self.readout)**symbol)\n",
    "        self.curr_layer_id += 1\n",
    "    \n",
    "    def get_builder_weights(self):\n",
    "        # returns symbol names in sorted order, which should match the order used for PQC (kinda hacky and can potentially break later)\n",
    "        # used as the next input for set_weights()\n",
    "        #\n",
    "        # Order of calls:\n",
    "        # tf.fit();\n",
    "        # builder.rebuild(tf.get_weights());\n",
    "        # add_layer()...;\n",
    "        # tf.set_weights(builder.get_builder_weights());\n",
    "        # tf.fit()\n",
    "        weights_list = []\n",
    "        \n",
    "        for k in sorted(self.symbol_map.keys()):\n",
    "            curr_is_dropout, curr_weight = self.symbol_map[k]\n",
    "            if not curr_is_dropout:\n",
    "                weights_list.append(curr_weight)\n",
    "        \n",
    "        return weights_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Sjo5hANFojhr"
   },
   "source": [
    "Build an example circuit layer to see how it looks:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dropped out qubit id 0\n"
     ]
    },
    {
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      ],
      "text/plain": [
       "<cirq.contrib.svg.svg.SVGCircuit at 0x7fdf1be54220>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TEST CELL to make sure CircuitLayerBuilder works properly\n",
    "circuit = cirq.Circuit()\n",
    "demo_builder = CircuitLayerBuilder(circuit=circuit,\n",
    "                                   data_qubits = cirq.GridQubit.rect(4,1),\n",
    "                                   readout=cirq.GridQubit(-1,-1))\n",
    "\n",
    "demo_builder.add_layer(gate=cirq.XX)\n",
    "demo_builder.add_layer(gate=cirq.ZZ, apply_dropout=True)\n",
    "demo_builder.add_layer(gate=cirq.XX, apply_dropout=False)\n",
    "demo_builder.add_layer(gate=cirq.ZZ, apply_dropout=False)\n",
    "\n",
    "SVGCircuit(circuit)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "T-QhPE1pojhu"
   },
   "source": [
    "Now build a two-layered model, matching the data-circuit size, and include the preparation and readout operations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "JiALbpwRGL69",
    "tags": []
   },
   "outputs": [],
   "source": [
    "def create_quantum_model(apply_dropout=False, n_dropout=5, builder=None, computed_weights_arr=None):\n",
    "    \"\"\"Create a QNN model circuit and readout operation to go along with it.\"\"\"\n",
    "    data_qubits = cirq.GridQubit.rect(4, 4)  # a 4x4 grid.\n",
    "    readout = cirq.GridQubit(-1, -1)         # a single qubit at [-1,-1]\n",
    "    circuit = cirq.Circuit()\n",
    "    \n",
    "    # Prepare the readout qubit.\n",
    "    circuit.append(cirq.X(readout))\n",
    "    circuit.append(cirq.H(readout))\n",
    "    \n",
    "    if builder is None or computed_weights_arr is None:\n",
    "        builder = CircuitLayerBuilder(\n",
    "            circuit=circuit,\n",
    "            data_qubits=data_qubits,\n",
    "            readout=readout)\n",
    "    else:\n",
    "        builder.rebuild(circuit, data_qubits, readout, computed_weights_arr)\n",
    "\n",
    "    # Then add layers (TODO: experiment by adding more).\n",
    "    builder.add_layer(cirq.XX, False)\n",
    "    builder.add_layer(cirq.ZZ, apply_dropout)\n",
    "    builder.add_layer(cirq.XX, apply_dropout)\n",
    "    builder.add_layer(cirq.ZZ, apply_dropout)\n",
    "\n",
    "    # Finally, prepare the readout qubit.\n",
    "    circuit.append(cirq.H(readout))\n",
    "\n",
    "    return builder, circuit, cirq.Z(readout)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "2QZvVh7vojhx",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dropped out qubit id 2\n",
      "Dropped out qubit id 4\n",
      "Dropped out qubit id 6\n",
      "Dropped out qubit id 12\n",
      "Dropped out qubit id 0\n",
      "Dropped out qubit id 1\n",
      "Dropped out qubit id 2\n",
      "Dropped out qubit id 5\n",
      "Dropped out qubit id 12\n",
      "Dropped out qubit id 13\n",
      "Dropped out qubit id 6\n",
      "Dropped out qubit id 14\n"
     ]
    },
    {
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      ],
      "text/plain": [
       "<cirq.contrib.svg.svg.SVGCircuit at 0x7fdf1be59640>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_, model_circuit, model_readout = create_quantum_model(apply_dropout=True)\n",
    "SVGCircuit(model_circuit)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "LY7vbY6yfABE"
   },
   "source": [
    "### 2.2 Wrap the model-circuit in a tfq-keras model\n",
    "\n",
    "Build the Keras model with the quantum components. This model is fed the \"quantum data\", from `x_train_circ`, that encodes the classical data. It uses a *Parametrized Quantum Circuit* layer, `tfq.layers.PQC`, to train the model circuit, on the quantum data.\n",
    "\n",
    "To classify these images, <a href=\"https://arxiv.org/pdf/1802.06002.pdf\" class=\"external\">Farhi et al.</a> proposed taking the expectation of a readout qubit in a parameterized circuit. The expectation returns a value between 1 and -1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ZYdf_KOxojh0",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "52\n"
     ]
    }
   ],
   "source": [
    "# Build the Keras model.\n",
    "qlayer = tfq.layers.PQC(model_circuit, model_readout)\n",
    "print(len(qlayer.get_weights()[0]))\n",
    "\n",
    "model = tf.keras.Sequential([\n",
    "    # The input is the data-circuit, encoded as a tf.string\n",
    "    tf.keras.layers.Input(shape=(), dtype=tf.string),\n",
    "    # The PQC layer returns the expected value of the readout gate, range [-1,1].\n",
    "    qlayer,\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "jz-FbVc9ojh3"
   },
   "source": [
    "Next, describe the training procedure to the model, using the `compile` method.\n",
    "\n",
    "Since the the expected readout is in the range `[-1,1]`, optimizing the hinge loss is a somewhat natural fit. \n",
    "\n",
    "Note: Another valid approach would be to shift the output range to `[0,1]`, and treat it as the probability the model assigns to class `3`. This could be used with a standard a `tf.losses.BinaryCrossentropy` loss.\n",
    "\n",
    "To use the hinge loss here you need to make two small adjustments. First convert the labels, `y_train_nocon`, from boolean to `[-1,1]`, as expected by the hinge loss."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "CgMNkC1Fojh5",
    "tags": []
   },
   "outputs": [],
   "source": [
    "y_train_hinge = 2.0*y_train_nocon-1.0\n",
    "y_test_hinge = 2.0*y_test-1.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5nwnveDiojh7"
   },
   "source": [
    "Second, use a custiom `hinge_accuracy` metric that correctly handles `[-1, 1]` as the `y_true` labels argument. \n",
    "`tf.losses.BinaryAccuracy(threshold=0.0)` expects `y_true` to be a boolean, and so can't be used with hinge loss)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3XKtZ_TEojh8",
    "tags": []
   },
   "outputs": [],
   "source": [
    "def hinge_accuracy(y_true, y_pred):\n",
    "    y_true = tf.squeeze(y_true) > 0.0\n",
    "    y_pred = tf.squeeze(y_pred) > 0.0\n",
    "    result = tf.cast(y_true == y_pred, tf.float32)\n",
    "\n",
    "    return tf.reduce_mean(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "FlpETlLRojiA",
    "tags": []
   },
   "outputs": [],
   "source": [
    "model.compile(\n",
    "    loss=tf.keras.losses.Hinge(),\n",
    "    optimizer=tf.keras.optimizers.Adam(),\n",
    "    metrics=[hinge_accuracy])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "jkHq2RstojiC",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "pqc (PQC)                    (None, 1)                 52        \n",
      "=================================================================\n",
      "Total params: 52\n",
      "Trainable params: 52\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. TensorBoard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "logdir = \"tb_logs/\" + datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\")\n",
    "file_writer = tf.summary.create_file_writer(logdir + \"/metrics\")\n",
    "file_writer.set_as_default()\n",
    "tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=logdir, histogram_freq=1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "lsuOzDYblA9s"
   },
   "source": [
    "### Train the quantum model\n",
    "\n",
    "Now train the model—this takes about 45 min. If you don't want to wait that long, use a small subset of the data (set `NUM_EXAMPLES=500`, below). This doesn't really affect the model's progress during training (it only has 32 parameters, and doesn't need much data to constrain these). Using fewer examples just ends training earlier (5min), but runs long enough to show that it is making progress in the validation logs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "n8vuQpSLlBV2",
    "tags": []
   },
   "outputs": [],
   "source": [
    "EPOCHS = 20\n",
    "DROPOUT_EPOCHS = 2 # keep qubits dropped out for this num of epochs\n",
    "BATCH_SIZE = 32\n",
    "NUM_EXAMPLES = 500\n",
    "#NUM_EXAMPLES=None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "qJnNG-3JojiI",
    "tags": []
   },
   "outputs": [],
   "source": [
    "x_train_tfcirc_sub = x_train_tfcirc[:NUM_EXAMPLES]\n",
    "y_train_hinge_sub = y_train_hinge[:NUM_EXAMPLES]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "QMSdgGC1GL7D"
   },
   "source": [
    "Training this model to convergence should achieve >85% accuracy on the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_model(apply_dropout=False):\n",
    "    dropout_backup_flag = False\n",
    "    \n",
    "    for i in range(EPOCHS):        \n",
    "        # HACK - build new quantum circuit for each epoch and copy over the weights from the old one\n",
    "        \n",
    "        # Toggle apply_dropout off on first epoch to have something reasonable to freeze in dropout\n",
    "        if dropout_backup_flag:\n",
    "            apply_dropout = True\n",
    "        elif i == 0 and apply_dropout:\n",
    "            apply_dropout = False\n",
    "            dropout_backup_flag = True\n",
    "        \n",
    "        #if i == EPOCHS-2:\n",
    "            # Never use dropout on the last epoch; we can't get sane results otherwise\n",
    "            # grab the epoch before last too for better results\n",
    "            #dropout_backup_flag = False\n",
    "            #apply_dropout = False\n",
    "        \n",
    "        if i == 0:\n",
    "            model_builder, model_circuit, model_readout = create_quantum_model(apply_dropout, DROPOUT_EPOCHS)\n",
    "            qlayer = tfq.layers.PQC(model_circuit, model_readout)\n",
    "        else:\n",
    "            model_builder, model_circuit, model_readout = create_quantum_model(apply_dropout, -1, model_builder, qlayer.get_weights()[0])\n",
    "            qlayer_new = tfq.layers.PQC(model_circuit, model_readout,\n",
    "                                        initializer=tf.keras.initializers.Zeros)\n",
    "            curr_l = model_builder.get_builder_weights()\n",
    "            qlayer_new.set_weights([np.array(curr_l, dtype=np.float32)])\n",
    "            qlayer = qlayer_new\n",
    "\n",
    "        model = tf.keras.Sequential([\n",
    "            tf.keras.layers.Input(shape=(), dtype=tf.string),\n",
    "            qlayer,\n",
    "        ])\n",
    "        model.compile(\n",
    "            loss=tf.keras.losses.Hinge(),\n",
    "            optimizer=tf.keras.optimizers.Adam(),\n",
    "            metrics=[hinge_accuracy])\n",
    "\n",
    "        # Now fit the model for this epoch\n",
    "        model.fit(\n",
    "              x_train_tfcirc_sub, y_train_hinge_sub,\n",
    "              batch_size=32,\n",
    "              epochs=1,\n",
    "              verbose=1,\n",
    "              validation_data=(x_test_tfcirc, y_test_hinge),\n",
    "              callbacks=[tensorboard_callback])\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model failed to serialize as JSON. Ignoring... Layer PQC has arguments in `__init__` and therefore must override `get_config`.\n",
      "16/16 [==============================] - 47s 3s/step - loss: 1.0239 - hinge_accuracy: 0.2058 - val_loss: 1.0008 - val_hinge_accuracy: 0.4748\n",
      "WARNING:tensorflow:Model failed to serialize as JSON. Ignoring... Layer PQC has arguments in `__init__` and therefore must override `get_config`.\n",
      "16/16 [==============================] - 36s 2s/step - loss: 1.0025 - hinge_accuracy: 0.4009 - val_loss: 0.9954 - val_hinge_accuracy: 0.5378\n",
      "WARNING:tensorflow:Model failed to serialize as JSON. Ignoring... Layer PQC has arguments in `__init__` and therefore must override `get_config`.\n",
      "16/16 [==============================] - 38s 2s/step - loss: 0.9794 - hinge_accuracy: 0.7105 - val_loss: 0.9909 - val_hinge_accuracy: 0.6381\n",
      "WARNING:tensorflow:Model failed to serialize as JSON. Ignoring... Layer PQC has arguments in `__init__` and therefore must override `get_config`.\n",
      "16/16 [==============================] - 39s 2s/step - loss: 0.9564 - hinge_accuracy: 0.7217 - val_loss: 0.9830 - val_hinge_accuracy: 0.6568\n",
      "WARNING:tensorflow:Model failed to serialize as JSON. Ignoring... Layer PQC has arguments in `__init__` and therefore must override `get_config`.\n",
      "16/16 [==============================] - 45s 3s/step - loss: 0.9301 - hinge_accuracy: 0.7837 - val_loss: 0.9692 - val_hinge_accuracy: 0.7036\n",
      "WARNING:tensorflow:Model failed to serialize as JSON. Ignoring... Layer PQC has arguments in `__init__` and therefore must override `get_config`.\n",
      "16/16 [==============================] - 55s 3s/step - loss: 0.8974 - hinge_accuracy: 0.8027 - val_loss: 0.9497 - val_hinge_accuracy: 0.7293\n",
      "WARNING:tensorflow:Model failed to serialize as JSON. Ignoring... Layer PQC has arguments in `__init__` and therefore must override `get_config`.\n",
      "16/16 [==============================] - 41s 3s/step - loss: 0.8595 - hinge_accuracy: 0.8133 - val_loss: 0.9243 - val_hinge_accuracy: 0.7273\n",
      "WARNING:tensorflow:Model failed to serialize as JSON. Ignoring... Layer PQC has arguments in `__init__` and therefore must override `get_config`.\n",
      "16/16 [==============================] - 33s 2s/step - loss: 0.8185 - hinge_accuracy: 0.8343 - val_loss: 0.8954 - val_hinge_accuracy: 0.7994\n",
      "WARNING:tensorflow:Model failed to serialize as JSON. Ignoring... Layer PQC has arguments in `__init__` and therefore must override `get_config`.\n",
      "16/16 [==============================] - 31s 2s/step - loss: 0.7767 - hinge_accuracy: 0.8640 - val_loss: 0.8650 - val_hinge_accuracy: 0.8100\n",
      "WARNING:tensorflow:Model failed to serialize as JSON. Ignoring... Layer PQC has arguments in `__init__` and therefore must override `get_config`.\n",
      "16/16 [==============================] - 35s 2s/step - loss: 0.7355 - hinge_accuracy: 0.8675 - val_loss: 0.8334 - val_hinge_accuracy: 0.8150\n",
      "WARNING:tensorflow:Model failed to serialize as JSON. Ignoring... Layer PQC has arguments in `__init__` and therefore must override `get_config`.\n",
      "16/16 [==============================] - 73s 5s/step - loss: 0.6962 - hinge_accuracy: 0.8685 - val_loss: 0.8024 - val_hinge_accuracy: 0.8110\n",
      "WARNING:tensorflow:Model failed to serialize as JSON. Ignoring... Layer PQC has arguments in `__init__` and therefore must override `get_config`.\n",
      "16/16 [==============================] - 48s 3s/step - loss: 0.6600 - hinge_accuracy: 0.8637 - val_loss: 0.7738 - val_hinge_accuracy: 0.8090\n",
      "WARNING:tensorflow:Model failed to serialize as JSON. Ignoring... Layer PQC has arguments in `__init__` and therefore must override `get_config`.\n",
      "16/16 [==============================] - 46s 3s/step - loss: 0.6289 - hinge_accuracy: 0.8652 - val_loss: 0.7495 - val_hinge_accuracy: 0.8100\n",
      "WARNING:tensorflow:Model failed to serialize as JSON. Ignoring... Layer PQC has arguments in `__init__` and therefore must override `get_config`.\n",
      "16/16 [==============================] - 32s 2s/step - loss: 0.6026 - hinge_accuracy: 0.8652 - val_loss: 0.7299 - val_hinge_accuracy: 0.8054\n",
      "WARNING:tensorflow:Model failed to serialize as JSON. Ignoring... Layer PQC has arguments in `__init__` and therefore must override `get_config`.\n",
      "16/16 [==============================] - 38s 2s/step - loss: 0.5800 - hinge_accuracy: 0.8625 - val_loss: 0.7132 - val_hinge_accuracy: 0.8024\n",
      "WARNING:tensorflow:Model failed to serialize as JSON. Ignoring... Layer PQC has arguments in `__init__` and therefore must override `get_config`.\n",
      "16/16 [==============================] - 33s 2s/step - loss: 0.5569 - hinge_accuracy: 0.8625 - val_loss: 0.6929 - val_hinge_accuracy: 0.8019\n",
      "WARNING:tensorflow:Model failed to serialize as JSON. Ignoring... Layer PQC has arguments in `__init__` and therefore must override `get_config`.\n",
      "16/16 [==============================] - 34s 2s/step - loss: 0.5298 - hinge_accuracy: 0.8625 - val_loss: 0.6646 - val_hinge_accuracy: 0.8009\n",
      "WARNING:tensorflow:Model failed to serialize as JSON. Ignoring... Layer PQC has arguments in `__init__` and therefore must override `get_config`.\n",
      "16/16 [==============================] - 32s 2s/step - loss: 0.4969 - hinge_accuracy: 0.8612 - val_loss: 0.6264 - val_hinge_accuracy: 0.8014\n",
      "WARNING:tensorflow:Model failed to serialize as JSON. Ignoring... Layer PQC has arguments in `__init__` and therefore must override `get_config`.\n",
      "16/16 [==============================] - 32s 2s/step - loss: 0.4596 - hinge_accuracy: 0.8612 - val_loss: 0.5824 - val_hinge_accuracy: 0.8029\n",
      "WARNING:tensorflow:Model failed to serialize as JSON. Ignoring... Layer PQC has arguments in `__init__` and therefore must override `get_config`.\n",
      "16/16 [==============================] - 36s 2s/step - loss: 0.4213 - hinge_accuracy: 0.8549 - val_loss: 0.5376 - val_hinge_accuracy: 0.8029\n"
     ]
    }
   ],
   "source": [
    "model = train_model(apply_dropout=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Non-dropout training set perf:\n",
      "16/16 [==============================] - 4s 246ms/step - loss: 0.3842 - hinge_accuracy: 0.8609\n",
      "Non-dropout test set perf:\n",
      "62/62 [==============================] - 14s 216ms/step - loss: 0.5376 - hinge_accuracy: 0.8029\n"
     ]
    }
   ],
   "source": [
    "print(\"Non-dropout training set perf:\")\n",
    "model.evaluate(x_train_tfcirc_sub, y_train_hinge_sub)\n",
    "print(\"Non-dropout test set perf:\")\n",
    "qnn_results = model.evaluate(x_test_tfcirc, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Ya9qP3KkojiM",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model failed to serialize as JSON. Ignoring... Layer PQC has arguments in `__init__` and therefore must override `get_config`.\n",
      "16/16 [==============================] - 36s 2s/step - loss: 0.9993 - hinge_accuracy: 0.5312 - val_loss: 1.0009 - val_hinge_accuracy: 0.3911\n",
      "Dropped out qubit id 10\n",
      "Dropped out qubit id 12\n",
      "Dropped out qubit id 2\n",
      "Dropped out qubit id 7\n",
      "Dropped out qubit id 8\n",
      "Dropped out qubit id 12\n",
      "Dropped out qubit id 14\n",
      "Dropped out qubit id 1\n",
      "Dropped out qubit id 2\n",
      "Dropped out qubit id 3\n",
      "Dropped out qubit id 7\n",
      "Dropped out qubit id 11\n",
      "Dropped out qubit id 15\n",
      "WARNING:tensorflow:Model failed to serialize as JSON. Ignoring... Layer PQC has arguments in `__init__` and therefore must override `get_config`.\n",
      "16/16 [==============================] - 37s 2s/step - loss: 0.9912 - hinge_accuracy: 0.6622 - val_loss: 1.0004 - val_hinge_accuracy: 0.4254\n",
      "Dropped out qubit id 0\n",
      "Dropped out qubit id 11\n",
      "Dropped out qubit id 4\n",
      "Dropped out qubit id 6\n",
      "Dropped out qubit id 12\n",
      "Dropped out qubit id 1\n",
      "Dropped out qubit id 7\n",
      "Dropped out qubit id 13\n",
      "WARNING:tensorflow:Model failed to serialize as JSON. Ignoring... Layer PQC has arguments in `__init__` and therefore must override `get_config`.\n",
      "16/16 [==============================] - 37s 2s/step - loss: 0.9813 - hinge_accuracy: 0.6541 - val_loss: 1.0004 - val_hinge_accuracy: 0.4249\n",
      "Dropped out qubit id 2\n",
      "Dropped out qubit id 8\n",
      "Dropped out qubit id 12\n",
      "Dropped out qubit id 13\n",
      "Dropped out qubit id 2\n",
      "Dropped out qubit id 3\n",
      "Dropped out qubit id 4\n",
      "Dropped out qubit id 8\n",
      "Dropped out qubit id 13\n",
      "Dropped out qubit id 8\n",
      "Dropped out qubit id 10\n",
      "WARNING:tensorflow:Model failed to serialize as JSON. Ignoring... Layer PQC has arguments in `__init__` and therefore must override `get_config`.\n",
      "16/16 [==============================] - 37s 2s/step - loss: 0.9672 - hinge_accuracy: 0.6564 - val_loss: 0.9989 - val_hinge_accuracy: 0.4264\n",
      "Dropped out qubit id 3\n",
      "Dropped out qubit id 6\n",
      "Dropped out qubit id 13\n",
      "Dropped out qubit id 15\n",
      "Dropped out qubit id 1\n",
      "Dropped out qubit id 4\n",
      "Dropped out qubit id 5\n",
      "Dropped out qubit id 9\n",
      "Dropped out qubit id 13\n",
      "Dropped out qubit id 15\n",
      "WARNING:tensorflow:Model failed to serialize as JSON. Ignoring... Layer PQC has arguments in `__init__` and therefore must override `get_config`.\n",
      "16/16 [==============================] - 38s 2s/step - loss: 0.9491 - hinge_accuracy: 0.6799 - val_loss: 0.9949 - val_hinge_accuracy: 0.5413\n",
      "Dropped out qubit id 0\n",
      "Dropped out qubit id 8\n",
      "Dropped out qubit id 12\n",
      "Dropped out qubit id 13\n",
      "Dropped out qubit id 14\n",
      "Dropped out qubit id 15\n",
      "Dropped out qubit id 3\n",
      "Dropped out qubit id 8\n",
      "Dropped out qubit id 15\n",
      "Dropped out qubit id 1\n",
      "WARNING:tensorflow:Model failed to serialize as JSON. Ignoring... Layer PQC has arguments in `__init__` and therefore must override `get_config`.\n",
      "16/16 [==============================] - 38s 2s/step - loss: 0.9253 - hinge_accuracy: 0.7335 - val_loss: 0.9871 - val_hinge_accuracy: 0.5383\n",
      "Dropped out qubit id 5\n",
      "Dropped out qubit id 14\n",
      "Dropped out qubit id 2\n",
      "Dropped out qubit id 6\n",
      "Dropped out qubit id 8\n",
      "Dropped out qubit id 14\n",
      "Dropped out qubit id 0\n",
      "Dropped out qubit id 1\n",
      "Dropped out qubit id 3\n",
      "Dropped out qubit id 4\n",
      "Dropped out qubit id 8\n",
      "Dropped out qubit id 10\n",
      "Dropped out qubit id 12\n",
      "Dropped out qubit id 13\n",
      "WARNING:tensorflow:Model failed to serialize as JSON. Ignoring... Layer PQC has arguments in `__init__` and therefore must override `get_config`.\n",
      "16/16 [==============================] - 37s 2s/step - loss: 0.8987 - hinge_accuracy: 0.7122 - val_loss: 0.9809 - val_hinge_accuracy: 0.5141\n",
      "Dropped out qubit id 2\n",
      "Dropped out qubit id 11\n",
      "Dropped out qubit id 15\n",
      "Dropped out qubit id 0\n",
      "Dropped out qubit id 1\n",
      "Dropped out qubit id 8\n",
      "Dropped out qubit id 12\n",
      "WARNING:tensorflow:Model failed to serialize as JSON. Ignoring... Layer PQC has arguments in `__init__` and therefore must override `get_config`.\n",
      "16/16 [==============================] - 36s 2s/step - loss: 0.8701 - hinge_accuracy: 0.7016 - val_loss: 0.9643 - val_hinge_accuracy: 0.5691\n",
      "Dropped out qubit id 0\n",
      "Dropped out qubit id 2\n",
      "Dropped out qubit id 5\n",
      "Dropped out qubit id 11\n",
      "Dropped out qubit id 12\n",
      "Dropped out qubit id 1\n",
      "Dropped out qubit id 3\n",
      "Dropped out qubit id 3\n",
      "Dropped out qubit id 7\n",
      "Dropped out qubit id 10\n",
      "WARNING:tensorflow:Model failed to serialize as JSON. Ignoring... Layer PQC has arguments in `__init__` and therefore must override `get_config`.\n",
      "16/16 [==============================] - 37s 2s/step - loss: 0.8353 - hinge_accuracy: 0.7040 - val_loss: 0.9463 - val_hinge_accuracy: 0.5736\n",
      "Dropped out qubit id 2\n",
      "Dropped out qubit id 6\n",
      "Dropped out qubit id 11\n",
      "Dropped out qubit id 1\n",
      "Dropped out qubit id 8\n",
      "Dropped out qubit id 13\n",
      "Dropped out qubit id 15\n",
      "Dropped out qubit id 6\n",
      "Dropped out qubit id 10\n",
      "Dropped out qubit id 11\n",
      "WARNING:tensorflow:Model failed to serialize as JSON. Ignoring... Layer PQC has arguments in `__init__` and therefore must override `get_config`.\n",
      "16/16 [==============================] - 35s 2s/step - loss: 0.8011 - hinge_accuracy: 0.7383 - val_loss: 0.9284 - val_hinge_accuracy: 0.6532\n",
      "Dropped out qubit id 4\n",
      "Dropped out qubit id 10\n",
      "Dropped out qubit id 13\n",
      "Dropped out qubit id 14\n",
      "Dropped out qubit id 15\n",
      "Dropped out qubit id 3\n",
      "Dropped out qubit id 11\n",
      "WARNING:tensorflow:Model failed to serialize as JSON. Ignoring... Layer PQC has arguments in `__init__` and therefore must override `get_config`.\n",
      "16/16 [==============================] - 35s 2s/step - loss: 0.7658 - hinge_accuracy: 0.7575 - val_loss: 0.8945 - val_hinge_accuracy: 0.6573\n",
      "Dropped out qubit id 12\n",
      "Dropped out qubit id 4\n",
      "Dropped out qubit id 15\n",
      "Dropped out qubit id 1\n",
      "Dropped out qubit id 2\n",
      "Dropped out qubit id 4\n",
      "Dropped out qubit id 5\n",
      "WARNING:tensorflow:Model failed to serialize as JSON. Ignoring... Layer PQC has arguments in `__init__` and therefore must override `get_config`.\n",
      "16/16 [==============================] - 35s 2s/step - loss: 0.7223 - hinge_accuracy: 0.7616 - val_loss: 0.8570 - val_hinge_accuracy: 0.7510\n",
      "Dropped out qubit id 0\n",
      "Dropped out qubit id 5\n",
      "Dropped out qubit id 9\n",
      "Dropped out qubit id 0\n",
      "Dropped out qubit id 10\n",
      "Dropped out qubit id 11\n",
      "Dropped out qubit id 13\n",
      "Dropped out qubit id 6\n",
      "Dropped out qubit id 11\n",
      "WARNING:tensorflow:Model failed to serialize as JSON. Ignoring... Layer PQC has arguments in `__init__` and therefore must override `get_config`.\n",
      "16/16 [==============================] - 34s 2s/step - loss: 0.6792 - hinge_accuracy: 0.8442 - val_loss: 0.8159 - val_hinge_accuracy: 0.8266\n",
      "Dropped out qubit id 4\n",
      "Dropped out qubit id 6\n",
      "Dropped out qubit id 8\n",
      "Dropped out qubit id 12\n",
      "Dropped out qubit id 0\n",
      "Dropped out qubit id 2\n",
      "Dropped out qubit id 3\n",
      "Dropped out qubit id 13\n",
      "Dropped out qubit id 15\n",
      "Dropped out qubit id 4\n",
      "Dropped out qubit id 9\n",
      "Dropped out qubit id 13\n",
      "WARNING:tensorflow:Model failed to serialize as JSON. Ignoring... Layer PQC has arguments in `__init__` and therefore must override `get_config`.\n",
      "16/16 [==============================] - 34s 2s/step - loss: 0.6365 - hinge_accuracy: 0.8617 - val_loss: 0.7775 - val_hinge_accuracy: 0.8276\n",
      "Dropped out qubit id 4\n",
      "Dropped out qubit id 9\n",
      "Dropped out qubit id 11\n",
      "Dropped out qubit id 14\n",
      "Dropped out qubit id 3\n",
      "Dropped out qubit id 6\n",
      "Dropped out qubit id 7\n",
      "Dropped out qubit id 9\n",
      "Dropped out qubit id 11\n",
      "Dropped out qubit id 13\n",
      "Dropped out qubit id 5\n",
      "Dropped out qubit id 10\n",
      "Dropped out qubit id 13\n",
      "Dropped out qubit id 15\n",
      "WARNING:tensorflow:Model failed to serialize as JSON. Ignoring... Layer PQC has arguments in `__init__` and therefore must override `get_config`.\n",
      "16/16 [==============================] - 33s 2s/step - loss: 0.5979 - hinge_accuracy: 0.8662 - val_loss: 0.7591 - val_hinge_accuracy: 0.8271\n",
      "Dropped out qubit id 3\n",
      "Dropped out qubit id 4\n",
      "Dropped out qubit id 7\n",
      "Dropped out qubit id 11\n",
      "Dropped out qubit id 14\n",
      "Dropped out qubit id 15\n",
      "Dropped out qubit id 4\n",
      "Dropped out qubit id 5\n",
      "Dropped out qubit id 6\n",
      "Dropped out qubit id 10\n",
      "Dropped out qubit id 1\n",
      "Dropped out qubit id 2\n",
      "Dropped out qubit id 11\n",
      "WARNING:tensorflow:Model failed to serialize as JSON. Ignoring... Layer PQC has arguments in `__init__` and therefore must override `get_config`.\n",
      "16/16 [==============================] - 33s 2s/step - loss: 0.5664 - hinge_accuracy: 0.8666 - val_loss: 0.7203 - val_hinge_accuracy: 0.8256\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dropped out qubit id 0\n",
      "Dropped out qubit id 1\n",
      "Dropped out qubit id 6\n",
      "Dropped out qubit id 8\n",
      "Dropped out qubit id 13\n",
      "Dropped out qubit id 15\n",
      "Dropped out qubit id 1\n",
      "Dropped out qubit id 5\n",
      "Dropped out qubit id 9\n",
      "Dropped out qubit id 15\n",
      "Dropped out qubit id 2\n",
      "Dropped out qubit id 3\n",
      "Dropped out qubit id 4\n",
      "Dropped out qubit id 5\n",
      "Dropped out qubit id 11\n",
      "Dropped out qubit id 12\n",
      "WARNING:tensorflow:Model failed to serialize as JSON. Ignoring... Layer PQC has arguments in `__init__` and therefore must override `get_config`.\n",
      "16/16 [==============================] - 32s 2s/step - loss: 0.5308 - hinge_accuracy: 0.8695 - val_loss: 0.6870 - val_hinge_accuracy: 0.8266\n",
      "Dropped out qubit id 1\n",
      "Dropped out qubit id 3\n",
      "Dropped out qubit id 4\n",
      "Dropped out qubit id 8\n",
      "Dropped out qubit id 12\n",
      "Dropped out qubit id 14\n",
      "Dropped out qubit id 4\n",
      "Dropped out qubit id 7\n",
      "Dropped out qubit id 3\n",
      "Dropped out qubit id 7\n",
      "Dropped out qubit id 10\n",
      "Dropped out qubit id 13\n",
      "WARNING:tensorflow:Model failed to serialize as JSON. Ignoring... Layer PQC has arguments in `__init__` and therefore must override `get_config`.\n",
      "16/16 [==============================] - 34s 2s/step - loss: 0.5006 - hinge_accuracy: 0.8691 - val_loss: 0.6725 - val_hinge_accuracy: 0.8261\n",
      "Dropped out qubit id 4\n",
      "Dropped out qubit id 6\n",
      "Dropped out qubit id 11\n",
      "Dropped out qubit id 12\n",
      "Dropped out qubit id 15\n",
      "Dropped out qubit id 2\n",
      "Dropped out qubit id 13\n",
      "Dropped out qubit id 14\n",
      "Dropped out qubit id 3\n",
      "Dropped out qubit id 9\n",
      "Dropped out qubit id 13\n",
      "Dropped out qubit id 14\n",
      "WARNING:tensorflow:Model failed to serialize as JSON. Ignoring... Layer PQC has arguments in `__init__` and therefore must override `get_config`.\n",
      "16/16 [==============================] - 36s 2s/step - loss: 0.4761 - hinge_accuracy: 0.8691 - val_loss: 0.6443 - val_hinge_accuracy: 0.8246\n",
      "Dropped out qubit id 2\n",
      "Dropped out qubit id 9\n",
      "Dropped out qubit id 12\n",
      "Dropped out qubit id 14\n",
      "Dropped out qubit id 3\n",
      "Dropped out qubit id 9\n",
      "Dropped out qubit id 12\n",
      "Dropped out qubit id 13\n",
      "Dropped out qubit id 15\n",
      "Dropped out qubit id 8\n",
      "WARNING:tensorflow:Model failed to serialize as JSON. Ignoring... Layer PQC has arguments in `__init__` and therefore must override `get_config`.\n",
      "16/16 [==============================] - 38s 2s/step - loss: 0.4477 - hinge_accuracy: 0.8691 - val_loss: 0.6071 - val_hinge_accuracy: 0.8256\n"
     ]
    }
   ],
   "source": [
    "dropout_model = train_model(apply_dropout=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dropout training set perf:\n",
      "16/16 [==============================] - 5s 284ms/step - loss: 0.4129 - hinge_accuracy: 0.8797\n",
      "Dropout test set perf:\n",
      "62/62 [==============================] - 16s 256ms/step - loss: 0.6071 - hinge_accuracy: 0.8256\n"
     ]
    }
   ],
   "source": [
    "print(\"Dropout training set perf:\")\n",
    "dropout_model.evaluate(x_train_tfcirc_sub, y_train_hinge_sub)\n",
    "print(\"Dropout test set perf:\")\n",
    "qnn_results = dropout_model.evaluate(x_test_tfcirc, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3ER7B7aaojiP"
   },
   "source": [
    "Note: The training accuracy reports the average over the epoch. The validation accuracy is evaluated at the end of each epoch."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8952YvuWGL7J"
   },
   "source": [
    "## 4. Classical neural network\n",
    "\n",
    "While the quantum neural network works for this simplified MNIST problem, a basic classical neural network can easily outperform a QNN on this task. After a single epoch, a classical neural network can achieve >98% accuracy on the holdout set.\n",
    "\n",
    "In the following example, a classical neural network is used for for the 3-6 classification problem using the entire 28x28 image instead of subsampling the image. This easily converges to nearly 100% accuracy of the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "pZofEHhLGL7L",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_16\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_2 (Conv2D)            (None, 26, 26, 32)        320       \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 24, 24, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 12, 12, 64)        0         \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 12, 12, 64)        0         \n",
      "_________________________________________________________________\n",
      "flatten_2 (Flatten)          (None, 9216)              0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 128)               1179776   \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 1)                 129       \n",
      "=================================================================\n",
      "Total params: 1,198,721\n",
      "Trainable params: 1,198,721\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "def create_classical_model():\n",
    "    # A simple model based off LeNet from https://keras.io/examples/mnist_cnn/\n",
    "    model = tf.keras.Sequential()\n",
    "    model.add(tf.keras.layers.Conv2D(32, [3, 3], activation='relu', input_shape=(28,28,1)))\n",
    "    model.add(tf.keras.layers.Conv2D(64, [3, 3], activation='relu'))\n",
    "    model.add(tf.keras.layers.MaxPooling2D(pool_size=(2, 2)))\n",
    "    model.add(tf.keras.layers.Dropout(0.25))\n",
    "    model.add(tf.keras.layers.Flatten())\n",
    "    model.add(tf.keras.layers.Dense(128, activation='relu'))\n",
    "    model.add(tf.keras.layers.Dropout(0.5))\n",
    "    model.add(tf.keras.layers.Dense(1))\n",
    "    return model\n",
    "\n",
    "\n",
    "model = create_classical_model()\n",
    "model.compile(loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),\n",
    "              optimizer=tf.keras.optimizers.Adam(),\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "CiAJl7sZojiU",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "95/95 [==============================] - 19s 188ms/step - loss: 0.1138 - accuracy: 0.9467 - val_loss: 0.0057 - val_accuracy: 0.9990\n",
      "62/62 [==============================] - 1s 13ms/step - loss: 0.0057 - accuracy: 0.9990\n"
     ]
    }
   ],
   "source": [
    "model.fit(x_train,\n",
    "          y_train,\n",
    "          batch_size=128,\n",
    "          epochs=1,\n",
    "          verbose=1,\n",
    "          validation_data=(x_test, y_test))\n",
    "\n",
    "cnn_results = model.evaluate(x_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "X5-5BVJaojiZ"
   },
   "source": [
    "The above model has nearly 1.2M parameters. For a more fair comparison, try a 37-parameter model, on the subsampled images:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "70TOM6r-ojiZ",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_17\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "flatten_3 (Flatten)          (None, 16)                0         \n",
      "_________________________________________________________________\n",
      "dense_6 (Dense)              (None, 2)                 34        \n",
      "_________________________________________________________________\n",
      "dense_7 (Dense)              (None, 1)                 3         \n",
      "=================================================================\n",
      "Total params: 37\n",
      "Trainable params: 37\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "def create_fair_classical_model():\n",
    "    # A simple model based off LeNet from https://keras.io/examples/mnist_cnn/\n",
    "    model = tf.keras.Sequential()\n",
    "    model.add(tf.keras.layers.Flatten(input_shape=(4,4,1)))\n",
    "    model.add(tf.keras.layers.Dense(2, activation='relu'))\n",
    "    model.add(tf.keras.layers.Dense(1))\n",
    "    return model\n",
    "\n",
    "\n",
    "model = create_fair_classical_model()\n",
    "model.compile(loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),\n",
    "              optimizer=tf.keras.optimizers.Adam(),\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "lA_Fx-8gojid",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "81/81 - 1s - loss: 0.6579 - accuracy: 0.5250 - val_loss: 0.6457 - val_accuracy: 0.4878\n",
      "Epoch 2/20\n",
      "81/81 - 0s - loss: 0.6325 - accuracy: 0.5525 - val_loss: 0.6172 - val_accuracy: 0.5701\n",
      "Epoch 3/20\n",
      "81/81 - 0s - loss: 0.5962 - accuracy: 0.6614 - val_loss: 0.5704 - val_accuracy: 0.7642\n",
      "Epoch 4/20\n",
      "81/81 - 0s - loss: 0.5455 - accuracy: 0.8182 - val_loss: 0.5161 - val_accuracy: 0.8237\n",
      "Epoch 5/20\n",
      "81/81 - 0s - loss: 0.4965 - accuracy: 0.8508 - val_loss: 0.4702 - val_accuracy: 0.8318\n",
      "Epoch 6/20\n",
      "81/81 - 0s - loss: 0.4564 - accuracy: 0.8564 - val_loss: 0.4347 - val_accuracy: 0.8308\n",
      "Epoch 7/20\n",
      "81/81 - 0s - loss: 0.4255 - accuracy: 0.8578 - val_loss: 0.4075 - val_accuracy: 0.8308\n",
      "Epoch 8/20\n",
      "81/81 - 0s - loss: 0.4008 - accuracy: 0.8582 - val_loss: 0.3854 - val_accuracy: 0.8323\n",
      "Epoch 9/20\n",
      "81/81 - 0s - loss: 0.3807 - accuracy: 0.8667 - val_loss: 0.3675 - val_accuracy: 0.8709\n",
      "Epoch 10/20\n",
      "81/81 - 0s - loss: 0.3637 - accuracy: 0.8803 - val_loss: 0.3522 - val_accuracy: 0.8709\n",
      "Epoch 11/20\n",
      "81/81 - 0s - loss: 0.3493 - accuracy: 0.8822 - val_loss: 0.3394 - val_accuracy: 0.8709\n",
      "Epoch 12/20\n",
      "81/81 - 0s - loss: 0.3369 - accuracy: 0.8829 - val_loss: 0.3284 - val_accuracy: 0.8709\n",
      "Epoch 13/20\n",
      "81/81 - 0s - loss: 0.3261 - accuracy: 0.8829 - val_loss: 0.3188 - val_accuracy: 0.8709\n",
      "Epoch 14/20\n",
      "81/81 - 0s - loss: 0.3167 - accuracy: 0.8829 - val_loss: 0.3102 - val_accuracy: 0.8709\n",
      "Epoch 15/20\n",
      "81/81 - 0s - loss: 0.3083 - accuracy: 0.8829 - val_loss: 0.3027 - val_accuracy: 0.8709\n",
      "Epoch 16/20\n",
      "81/81 - 0s - loss: 0.3009 - accuracy: 0.8831 - val_loss: 0.2960 - val_accuracy: 0.8714\n",
      "Epoch 17/20\n",
      "81/81 - 0s - loss: 0.2943 - accuracy: 0.8833 - val_loss: 0.2901 - val_accuracy: 0.8714\n",
      "Epoch 18/20\n",
      "81/81 - 0s - loss: 0.2884 - accuracy: 0.8834 - val_loss: 0.2847 - val_accuracy: 0.8720\n",
      "Epoch 19/20\n",
      "81/81 - 0s - loss: 0.2830 - accuracy: 0.8835 - val_loss: 0.2798 - val_accuracy: 0.8720\n",
      "Epoch 20/20\n",
      "81/81 - 0s - loss: 0.2782 - accuracy: 0.8835 - val_loss: 0.2754 - val_accuracy: 0.8720\n",
      "62/62 [==============================] - 0s 721us/step - loss: 0.2754 - accuracy: 0.8720\n"
     ]
    }
   ],
   "source": [
    "model.fit(x_train_bin,\n",
    "          y_train_nocon,\n",
    "          batch_size=128,\n",
    "          epochs=20,\n",
    "          verbose=2,\n",
    "          validation_data=(x_test_bin, y_test))\n",
    "\n",
    "fair_nn_results = model.evaluate(x_test_bin, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "RH3mam7EGL7N"
   },
   "source": [
    "## 5. Comparison\n",
    "\n",
    "Higher resolution input and a more powerful model make this problem easy for the CNN. While a classical model of similar power (~32 parameters) trains to a similar accuracy in a fraction of the time. One way or the other, the classical neural network easily outperforms the quantum neural network. For classical data, it is difficult to beat a classical neural network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "NOMeN7pMGL7P",
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rickyyoung/opt/anaconda3/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "qnn_accuracy = qnn_results[1]\n",
    "cnn_accuracy = cnn_results[1]\n",
    "fair_nn_accuracy = fair_nn_results[1]\n",
    "\n",
    "sns.barplot([\"Quantum\", \"Classical, full\", \"Classical, fair\"],\n",
    "            [qnn_accuracy, cnn_accuracy, fair_nn_accuracy])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "mnist.ipynb",
   "private_outputs": true,
   "provenance": [],
   "toc_visible": true
  },
  "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}