VAE_CNN.ipynb

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  "metadata": {
    "colab": {
      "name": "VAE_CNN.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "TPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ml1APfMmvCc9",
        "colab_type": "code",
        "outputId": "0e896e55-ec32-4eec-d48a-a4dd6fd980a9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "%tensorflow_version 2.x\n",
        "import tensorflow as tf\n",
        "\n",
        "from __future__ import absolute_import\n",
        "from __future__ import division\n",
        "from __future__ import print_function\n",
        "\n",
        "from tensorflow.keras.layers import Lambda, Input, Dense, BatchNormalization, LeakyReLU\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.datasets import mnist\n",
        "from tensorflow.keras.losses import mse, binary_crossentropy\n",
        "from tensorflow.keras.utils import plot_model\n",
        "from tensorflow.keras import backend as K\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from tensorflow.keras import metrics\n",
        "from tensorflow.keras.optimizers import Adam, SGD\n",
        "from tensorflow.keras.layers import Conv2D, Flatten\n",
        "from tensorflow.keras.layers import Reshape, Conv2DTranspose\n",
        "\n",
        "import numpy as np\n",
        "from sklearn import preprocessing\n",
        "import matplotlib.pyplot as plt\n",
        "import argparse\n",
        "import os\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TensorFlow 2.x selected.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QD3UwH4HvHQb",
        "colab_type": "code",
        "outputId": "fbfb8bf2-2658-4710-fa1b-c016c8095350",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 745
        }
      },
      "source": [
        "######################\n",
        "### Load/Check TPU ###\n",
        "######################\n",
        "\n",
        "print(\"Tensorflow version \" + tf.__version__)\n",
        "\n",
        "try:\n",
        "  tpu = tf.distribute.cluster_resolver.TPUClusterResolver()\n",
        "  print('Running on TPU ', tpu.cluster_spec().as_dict()['worker'])\n",
        "except ValueError:\n",
        "  raise BaseException('Error: Not connected to a TPU runtime')\n",
        "\n",
        "tf.config.experimental_connect_to_cluster(tpu)\n",
        "tf.tpu.experimental.initialize_tpu_system(tpu)\n",
        "tpu_strategy = tf.distribute.experimental.TPUStrategy(tpu)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Tensorflow version 2.1.0\n",
            "Running on TPU  ['10.60.94.234:8470']\n",
            "INFO:tensorflow:Initializing the TPU system: 10.60.94.234:8470\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Initializing the TPU system: 10.60.94.234:8470\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Clearing out eager caches\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Clearing out eager caches\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Finished initializing TPU system.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Finished initializing TPU system.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Found TPU system:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Found TPU system:\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Num TPU Cores: 8\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Num TPU Cores: 8\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Num TPU Workers: 1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Num TPU Workers: 1\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Num TPU Cores Per Worker: 8\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Num TPU Cores Per Worker: 8\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:localhost/replica:0/task:0/device:CPU:0, CPU, 0, 0)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:localhost/replica:0/task:0/device:CPU:0, CPU, 0, 0)\n"
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          "name": "stderr"
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          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:localhost/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 0, 0)\n"
          ],
          "name": "stdout"
        },
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          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:localhost/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 0, 0)\n"
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            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:0, TPU, 0, 0)\n"
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            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:0, TPU, 0, 0)\n"
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            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:1, TPU, 0, 0)\n"
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          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:1, TPU, 0, 0)\n"
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          "name": "stderr"
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          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:2, TPU, 0, 0)\n"
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        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:2, TPU, 0, 0)\n"
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          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:3, TPU, 0, 0)\n"
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          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:3, TPU, 0, 0)\n"
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            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:4, TPU, 0, 0)\n"
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          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:4, TPU, 0, 0)\n"
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            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:5, TPU, 0, 0)\n"
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            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:7, TPU, 0, 0)\n"
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            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:7, TPU, 0, 0)\n"
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        },
        {
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          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU_SYSTEM:0, TPU_SYSTEM, 0, 0)\n"
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        },
        {
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          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU_SYSTEM:0, TPU_SYSTEM, 0, 0)\n"
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          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 0, 0)\n"
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          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 0, 0)\n"
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        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V7yDLlfMvIwy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#############################\n",
        "### Sampling and Plotting ###\n",
        "#############################\n",
        "\n",
        "\n",
        "# reparameterization trick: instead of sampling from Q(z|X), sample epsilon = N(0,I)\n",
        "# z = z_mean + sqrt(var) * epsilon\n",
        "def sampling(args):   #args (tensor): mean and log of variance of Q(z|X)\n",
        "    z_mean, z_log_var = args\n",
        "    batch = K.shape(z_mean)[0]\n",
        "    dim = K.int_shape(z_mean)[1]\n",
        "    # by default, random_normal has mean = 0 and std = 1.0\n",
        "    epsilon = K.random_normal(shape=(batch, dim))\n",
        "    return z_mean + K.exp(0.5 * z_log_var) * epsilon  # Returns ampled latent vector\n",
        "\n",
        "\n",
        "#plots latent space and samples\n",
        "def plot_results(models,\n",
        "                 data,\n",
        "                 batch_size=128,\n",
        "                 model_name=\"vae_eeg\"):\n",
        "    #plot each row for a eeg signle\n",
        "\n",
        "    encoder, decoder = models\n",
        "    x_test, y_test = data\n",
        "    print(x_test.shape)\n",
        "    print(y_test.shape)\n",
        "    os.makedirs(model_name, exist_ok=True)\n",
        "\n",
        "    filename = os.path.join(model_name, \"vae_mean.png\")\n",
        "    # display a 2D plot of the classes in the latent space\n",
        "    z_mean, _, _ = encoder.predict(x_test,\n",
        "                                   batch_size=batch_size)\n",
        "    plt.figure(figsize=(12, 10))\n",
        "    plt.scatter(z_mean[:, 0], z_mean[:, 1], c=y_test)\n",
        "    plt.colorbar()\n",
        "    plt.xlabel(\"z[0]\")\n",
        "    plt.ylabel(\"z[1]\")\n",
        "    plt.savefig(filename)\n",
        "    plt.show()\n",
        "\n",
        "    filename = os.path.join(model_name, \"digits_over_latent.png\")\n",
        "\n",
        "    z_sample = np.array([[22,1000]])\n",
        "    x_decoded = decoder.predict(z_sample)\n",
        "    print(x_decoded.shape)\n",
        "    fs = 250\n",
        "    increments = np.linspace(0,1000,1000)\n",
        "    \n",
        "    plt.figure()\n",
        "\n",
        "    for i in range(0, 22):\n",
        "      y = np.row_stack((x_decoded[:,i,:,:]))\n",
        "\n",
        "      #print(y)\n",
        "      plt.plot(increments, (y), '.')\n",
        "      plt.xlabel(\"Sample Index [n]\")\n",
        "      plt.ylabel(\"z[n]\")\n",
        "      plt.title(\"Single Trial, all EEG\")\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "#plot_results(models, data, batch_size=batch_size, model_name=\"vae_eed\")\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tw8D0AskvMK6",
        "colab_type": "code",
        "outputId": "b1b7023d-de02-437b-e0c6-ffd8f65afee4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 124
        }
      },
      "source": [
        "###################\n",
        "### mount drive ###\n",
        "###################\n",
        "\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive',force_remount=True)\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qRxr4nB3v7-6",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7e7IRP6VvN17",
        "colab_type": "code",
        "outputId": "d436934e-4a6f-48ab-be6f-96b010af6776",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        }
      },
      "source": [
        "##########################\n",
        "### Preprocessing Data ###\n",
        "##########################\n",
        "\n",
        "import os\n",
        "import numpy as np\n",
        "import random\n",
        "from importlib import reload\n",
        "try:  \n",
        "  project_fname = '/content/drive/My Drive/c247/'\n",
        "  os.chdir(project_fname)\n",
        "  project_data_file = os.path.join(project_fname,'project_data/')\n",
        "except FileNotFoundError: \n",
        "  project_fname = '/content/drive/My Drive/Colab Notebooks/c247/'\n",
        "  os.chdir(project_fname)\n",
        "  project_data_file = os.path.join(project_fname,'project_data/')\n",
        "\n",
        "\n",
        "%cd /content/drive/My Drive/c247/\n",
        "from load_data import *\n",
        "from data_preprocessing import * \n",
        "\n",
        "X_test, y_test, person_train_valid, X_train_valid, y_train_valid, person_test = load_data(dir_path = project_data_file)   #load data\n",
        "\n",
        "#normalize data\n",
        "N_trials,N_eeg,N_bins,_ = X_train_valid.shape\n",
        "X_train_valid_norm = np.reshape(preprocessing.scale(np.reshape(X_train_valid,(N_trials*N_eeg,N_bins)),axis=1),(N_trials,N_eeg,N_bins,1))\n",
        "N_trials,N_eeg,N_bins,_ = X_test.shape\n",
        "X_test_norm = np.reshape(preprocessing.scale(np.reshape(X_test,(N_trials*N_eeg,N_bins)),axis=1),(N_trials,N_eeg,N_bins,1))\n",
        "\n",
        "# print data\n",
        "print ('Training/Valid data shape: {}'.format(X_train_valid.shape))\n",
        "print ('Test data shape: {}'.format(X_test.shape))\n",
        "print ('Training/Valid target shape: {}'.format(y_train_valid.shape))\n",
        "print ('Test target shape: {}'.format(y_test.shape))\n",
        "print ('Person train/valid shape: {}'.format(person_train_valid.shape))\n",
        "print ('Person test shape: {}'.format(person_test.shape))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content\n",
            "Training/Valid data shape: (2115, 22, 1000, 1)\n",
            "Test data shape: (443, 22, 1000, 1)\n",
            "Training/Valid target shape: (2115,)\n",
            "Test target shape: (443,)\n",
            "Person train/valid shape: (2115, 1)\n",
            "Person test shape: (443, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5PBcxIduvVhO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "######################\n",
        "### Network Params ###\n",
        "######################\n",
        "\n",
        "#function is unessary, but was made for upscaling. Process was halted due to \"mode collapse\"\n",
        "def NetworkParameters (image_size,batch_size,kernel_size, filters, latent_dim, epochs):\n",
        "  # network parameters\n",
        "  input_shape = (image_size[0], image_size[1],1)\n",
        "  image_size = input_shape\n",
        "  batch_size = batch_size\n",
        "  kernel_size = kernel_size\n",
        "  filters = filters\n",
        "  latent_dim = latent_dim\n",
        "  epochs = epochs\n",
        "  return input_shape,image_size,batch_size,kernel_size, filters, latent_dim, epochs\n",
        "\n",
        "(input_shape, \n",
        " image_size,\n",
        " batch_size, \n",
        " kernel_size, \n",
        " filters, \n",
        " latent_dim, \n",
        " epochs) = NetworkParameters(image_size = X_train_valid.shape[1:3],\n",
        "                                                    batch_size = 128,\n",
        "                                                    kernel_size = 7,\n",
        "                                                    filters = 16,\n",
        "                                                    latent_dim = 2,\n",
        "                                                    epochs = 30)\n",
        " "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ztz3KuS7vWp0",
        "colab_type": "code",
        "outputId": "9ddde230-d6e9-4824-a407-dca8cf50770a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 936
        }
      },
      "source": [
        "#####################\n",
        "### CNN VAE Model ###\n",
        "#####################\n",
        "#VAE model = encoder + decoder\n",
        "\n",
        "#encoder model\n",
        "inputs = Input(shape=input_shape, name='encoder_input')\n",
        "print(inputs)\n",
        "x = inputs\n",
        "filters *= 2\n",
        "\n",
        "#Temporal conv\n",
        "x = Conv2D(filters=filters,\n",
        "          kernel_size=(1, 50),\n",
        "          strides=(1,25),\n",
        "          )(x)\n",
        "#x = BatchNormalization(epsilon=1e-5)(x) #Not normally used\n",
        "x = LeakyReLU(alpha=0.3)(x)\n",
        "\n",
        "\n",
        "filters *= 2\n",
        "#Spatial Conv\n",
        "x = Conv2D(filters=filters,\n",
        "          kernel_size=(22, 1),\n",
        "          )(x)\n",
        "#x = BatchNormalization(epsilon=1e-5)(x) #Not normally used\n",
        "x = LeakyReLU(alpha=0.3)(x)\n",
        "\n",
        "#shape info needed to build decoder model\n",
        "shape = K.int_shape(x)\n",
        "\n",
        "#latent vector Q(z|X)\n",
        "x = Flatten()(x)\n",
        "x = Dense(16, activation='relu')(x)\n",
        "z_mean = Dense(latent_dim, name='z_mean')(x)\n",
        "z_log_var = Dense(latent_dim, name='z_log_var')(x)\n",
        "z_log_var = z_log_var + 1e-8 \n",
        "print(\"zlog \", z_log_var)\n",
        "\n",
        "#reparameterization trick: instead of sampling from Q(z|X), sample epsilon = N(0,I)\n",
        "z = Lambda(sampling, output_shape=(latent_dim,), name='z')([z_mean, z_log_var]) #sample output as input\n",
        "\n",
        "#compile encoder\n",
        "encoder = Model(inputs, [z_mean, z_log_var, z], name='encoder')\n",
        "encoder.summary()\n",
        "plot_model(encoder, to_file='vae_cnn_encoder.png', show_shapes=True)\n",
        "\n",
        "#decoder model\n",
        "latent_inputs = Input(shape=(latent_dim,), name='z_sampling')\n",
        "x = Dense(shape[1] * shape[2] * shape[3], activation='relu')(latent_inputs)\n",
        "x = Reshape((shape[1], shape[2], shape[3]))(x)\n",
        "\n",
        "#reverse: spatial conv\n",
        "x = Conv2DTranspose(filters=filters,\n",
        "              kernel_size=(22, 1),\n",
        "              activation='relu',\n",
        "              )(x)\n",
        "#x = BatchNormalization(epsilon=1e-5)(x)\n",
        "\n",
        "filters //= 2\n",
        "\n",
        "#reverse: temporal conv\n",
        "x = Conv2DTranspose(filters=filters,\n",
        "          kernel_size=(1, 50),\n",
        "          activation='relu',\n",
        "          strides=(1,25))(x)\n",
        "#x = BatchNormalization(epsilon=1e-5)(x)\n",
        "\n",
        "filters //= 2\n",
        "\n",
        "outputs = Conv2DTranspose(filters=1,\n",
        "                          kernel_size=kernel_size,\n",
        "                          padding='same',\n",
        "                          name='decoder_output')(x)\n",
        "\n",
        "#compile decoder model\n",
        "decoder = Model(latent_inputs, outputs, name='decoder')\n",
        "decoder.summary()\n",
        "plot_model(decoder, to_file='vae_cnn_decoder.png', show_shapes=True)\n",
        "\n",
        "#compile VAE model\n",
        "outputs = decoder(encoder(inputs)[2])\n",
        "vae = Model(inputs, outputs, name='vae')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Tensor(\"encoder_input:0\", shape=(None, 22, 1000, 1), dtype=float32)\n",
            "zlog  Tensor(\"add:0\", shape=(None, 2), dtype=float32)\n",
            "Model: \"encoder\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "encoder_input (InputLayer)      [(None, 22, 1000, 1) 0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d (Conv2D)                 (None, 22, 39, 32)   1632        encoder_input[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu (LeakyReLU)         (None, 22, 39, 32)   0           conv2d[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_1 (Conv2D)               (None, 1, 39, 64)    45120       leaky_re_lu[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_1 (LeakyReLU)       (None, 1, 39, 64)    0           conv2d_1[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "flatten (Flatten)               (None, 2496)         0           leaky_re_lu_1[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dense (Dense)                   (None, 16)           39952       flatten[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "z_log_var (Dense)               (None, 2)            34          dense[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "z_mean (Dense)                  (None, 2)            34          dense[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_add (TensorFlowOpLa [(None, 2)]          0           z_log_var[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "z (Lambda)                      (None, 2)            0           z_mean[0][0]                     \n",
            "                                                                 tf_op_layer_add[0][0]            \n",
            "==================================================================================================\n",
            "Total params: 86,772\n",
            "Trainable params: 86,772\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n",
            "Model: \"decoder\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "z_sampling (InputLayer)      [(None, 2)]               0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 2496)              7488      \n",
            "_________________________________________________________________\n",
            "reshape (Reshape)            (None, 1, 39, 64)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_transpose (Conv2DTran (None, 22, 39, 64)        90176     \n",
            "_________________________________________________________________\n",
            "conv2d_transpose_1 (Conv2DTr (None, 22, 1000, 32)      102432    \n",
            "_________________________________________________________________\n",
            "decoder_output (Conv2DTransp (None, 22, 1000, 1)       1569      \n",
            "=================================================================\n",
            "Total params: 201,665\n",
            "Trainable params: 201,665\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BG25_-tavYJ4",
        "colab_type": "code",
        "outputId": "d0c9544b-7d07-47a2-e54c-6344abfb6d64",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "######################################\n",
        "### Optimize CNN VAE Loss Function ###\n",
        "######################################\n",
        "\n",
        "if __name__ == '__main__':\n",
        "    parser = argparse.ArgumentParser()\n",
        "    help_ = \"Load h5 model trained weights\"\n",
        "    parser.add_argument(\"-w\", \"--weights\", help=help_)\n",
        "    help_ = \"Use mse loss instead of binary cross entropy (default)\"\n",
        "    parser.add_argument(\"-m\", \"--mse\", help=help_, action='store_true')\n",
        "    args = parser.parse_args(args = [])\n",
        "    models = (encoder, decoder)\n",
        "    data = (X_test, y_test)\n",
        "\n",
        "\n",
        "    \n",
        "    #VAE loss = mse_loss or xent_loss + kl_loss\n",
        "    if args.mse:\n",
        "        reconstruction_loss = mse(K.flatten(inputs), K.flatten(outputs))\n",
        "    else:\n",
        "        reconstruction_loss = mse(K.flatten(inputs), K.flatten(outputs))\n",
        "        #reconstruction_loss = binary_crossentropy(K.flatten(inputs),\n",
        "                                                  #K.flatten(outputs))\n",
        "\n",
        "    reconstruction_loss *= image_size[0] * image_size[1]\n",
        "    kl_loss = 1 + z_log_var - K.square(z_mean) - K.exp(z_log_var)\n",
        "    kl_loss = K.sum(kl_loss, axis=-1)\n",
        "    kl_loss *= -0.5\n",
        "    vae_loss = K.mean(reconstruction_loss + kl_loss)\n",
        "    vae.add_loss(vae_loss)\n",
        "    optimizer = Adam(learning_rate=0.001, beta_1=0.1, beta_2=0.999, amsgrad=False)\n",
        "\n",
        "    vae.compile(optimizer=optimizer, loss=None)\n",
        "\n",
        "    vae.summary()\n",
        "    plot_model(vae, to_file='vae_cnn.png', show_shapes=True)\n",
        "\n",
        "    if args.weights:\n",
        "        #Load\n",
        "        vae.load_weights(args.weights)\n",
        "    else:\n",
        "        #Train\n",
        "        history = vae.fit(x = X_train_valid,\n",
        "                epochs=epochs,\n",
        "                batch_size=batch_size,\n",
        "                validation_data=(X_test, None))\n",
        "    #plot\n",
        "    plot_results(models, data, batch_size=batch_size, model_name=\"vae_cnn\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Output decoder missing from loss dictionary. We assume this was done on purpose. The fit and evaluate APIs will not be expecting any data to be passed to decoder.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Output decoder missing from loss dictionary. We assume this was done on purpose. The fit and evaluate APIs will not be expecting any data to be passed to decoder.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Model: \"vae\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "encoder_input (InputLayer)      [(None, 22, 1000, 1) 0                                            \n",
            "__________________________________________________________________________________________________\n",
            "encoder (Model)                 [(None, 2), (None, 2 86772       encoder_input[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "decoder (Model)                 (None, 22, 1000, 1)  201665      encoder[1][2]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d (Conv2D)                 (None, 22, 39, 32)   1632        encoder_input[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu (LeakyReLU)         (None, 22, 39, 32)   0           conv2d[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_1 (Conv2D)               (None, 1, 39, 64)    45120       leaky_re_lu[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_1 (LeakyReLU)       (None, 1, 39, 64)    0           conv2d_1[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "flatten (Flatten)               (None, 2496)         0           leaky_re_lu_1[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dense (Dense)                   (None, 16)           39952       flatten[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "z_log_var (Dense)               (None, 2)            34          dense[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_add (TensorFlowOpLa multiple             0           z_log_var[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "z_mean (Dense)                  (None, 2)            34          dense[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_add_1 (TensorFlowOp [(None, 2)]          0           tf_op_layer_add[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Square (TensorFlowO [(None, 2)]          0           z_mean[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Reshape_2 (TensorFl [(None,)]            0           decoder[1][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Reshape_1 (TensorFl [(None,)]            0           encoder_input[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_sub (TensorFlowOpLa [(None, 2)]          0           tf_op_layer_add_1[0][0]          \n",
            "                                                                 tf_op_layer_Square[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Exp (TensorFlowOpLa [(None, 2)]          0           tf_op_layer_add[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_SquaredDifference ( [(None,)]            0           tf_op_layer_Reshape_2[0][0]      \n",
            "                                                                 tf_op_layer_Reshape_1[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_sub_1 (TensorFlowOp [(None, 2)]          0           tf_op_layer_sub[0][0]            \n",
            "                                                                 tf_op_layer_Exp[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Mean (TensorFlowOpL [()]                 0           tf_op_layer_SquaredDifference[0][\n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Sum (TensorFlowOpLa [(None,)]            0           tf_op_layer_sub_1[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_mul (TensorFlowOpLa [()]                 0           tf_op_layer_Mean[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_mul_1 (TensorFlowOp [(None,)]            0           tf_op_layer_Sum[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_add_2 (TensorFlowOp [(None,)]            0           tf_op_layer_mul[0][0]            \n",
            "                                                                 tf_op_layer_mul_1[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Mean_1 (TensorFlowO [()]                 0           tf_op_layer_add_2[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "add_loss (AddLoss)              ()                   0           tf_op_layer_Mean_1[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_add_3 (TensorFlowOp [(None, 2)]          0           tf_op_layer_add[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Square_1 (TensorFlo [(None, 2)]          0           z_mean[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Reshape_4 (TensorFl [(None,)]            0           decoder[1][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Reshape_3 (TensorFl [(None,)]            0           encoder_input[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_sub_2 (TensorFlowOp [(None, 2)]          0           tf_op_layer_add_3[0][0]          \n",
            "                                                                 tf_op_layer_Square_1[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Exp_1 (TensorFlowOp [(None, 2)]          0           tf_op_layer_add[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_SquaredDifference_1 [(None,)]            0           tf_op_layer_Reshape_4[0][0]      \n",
            "                                                                 tf_op_layer_Reshape_3[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_sub_3 (TensorFlowOp [(None, 2)]          0           tf_op_layer_sub_2[0][0]          \n",
            "                                                                 tf_op_layer_Exp_1[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Mean_2 (TensorFlowO [()]                 0           tf_op_layer_SquaredDifference_1[0\n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Sum_1 (TensorFlowOp [(None,)]            0           tf_op_layer_sub_3[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_mul_2 (TensorFlowOp [()]                 0           tf_op_layer_Mean_2[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_mul_3 (TensorFlowOp [(None,)]            0           tf_op_layer_Sum_1[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_add_4 (TensorFlowOp [(None,)]            0           tf_op_layer_mul_2[0][0]          \n",
            "                                                                 tf_op_layer_mul_3[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Mean_3 (TensorFlowO [()]                 0           tf_op_layer_add_4[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "add_loss_1 (AddLoss)            ()                   0           tf_op_layer_Mean_3[0][0]         \n",
            "==================================================================================================\n",
            "Total params: 288,437\n",
            "Trainable params: 288,437\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n",
            "Train on 2115 samples, validate on 443 samples\n",
            "Epoch 1/30\n",
            "2115/2115 [==============================] - 10s 5ms/sample - loss: 4765803.5709 - val_loss: 4834813.5384\n",
            "Epoch 2/30\n",
            "2115/2115 [==============================] - 8s 4ms/sample - loss: 4772632.8882 - val_loss: 4831754.1783\n",
            "Epoch 3/30\n",
            "2115/2115 [==============================] - 8s 4ms/sample - loss: 4756151.3444 - val_loss: 4838629.7686\n",
            "Epoch 4/30\n",
            "2115/2115 [==============================] - 8s 4ms/sample - loss: 4753708.1747 - val_loss: 4873610.8578\n",
            "Epoch 5/30\n",
            "2115/2115 [==============================] - 9s 4ms/sample - loss: 4764763.2548 - val_loss: 4838135.4255\n",
            "Epoch 6/30\n",
            "2115/2115 [==============================] - 8s 4ms/sample - loss: 4750110.9511 - val_loss: 4843288.4470\n",
            "Epoch 7/30\n",
            "2115/2115 [==============================] - 8s 4ms/sample - loss: 4756296.0582 - val_loss: 4858822.1716\n",
            "Epoch 8/30\n",
            "2115/2115 [==============================] - 8s 4ms/sample - loss: 4752979.4872 - val_loss: 4840516.4695\n",
            "Epoch 9/30\n",
            "2115/2115 [==============================] - 8s 4ms/sample - loss: 4753045.6991 - val_loss: 4846210.4131\n",
            "Epoch 10/30\n",
            "2115/2115 [==============================] - 8s 4ms/sample - loss: 4746762.2950 - val_loss: 4848371.1163\n",
            "Epoch 11/30\n",
            "2115/2115 [==============================] - 8s 4ms/sample - loss: 4746225.2414 - val_loss: 4848502.7540\n",
            "Epoch 12/30\n",
            "2115/2115 [==============================] - 8s 4ms/sample - loss: 4743714.7234 - val_loss: 4856708.7122\n",
            "Epoch 13/30\n",
            "2115/2115 [==============================] - 8s 4ms/sample - loss: 4742637.1260 - val_loss: 4845685.2088\n",
            "Epoch 14/30\n",
            "2115/2115 [==============================] - 8s 4ms/sample - loss: 4742490.9593 - val_loss: 4842907.3939\n",
            "Epoch 15/30\n",
            "2115/2115 [==============================] - 8s 4ms/sample - loss: 4742744.8745 - val_loss: 4862299.0214\n",
            "Epoch 16/30\n",
            "2115/2115 [==============================] - 9s 4ms/sample - loss: 4737851.7870 - val_loss: 4866849.3962\n",
            "Epoch 17/30\n",
            "2115/2115 [==============================] - 9s 4ms/sample - loss: 4733361.7508 - val_loss: 4856722.2122\n",
            "Epoch 18/30\n",
            "2115/2115 [==============================] - 9s 4ms/sample - loss: 4733830.8116 - val_loss: 4845352.7698\n",
            "Epoch 19/30\n",
            "2115/2115 [==============================] - 9s 4ms/sample - loss: 4734937.4735 - val_loss: 4862796.2980\n",
            "Epoch 20/30\n",
            "2115/2115 [==============================] - 9s 4ms/sample - loss: 4728444.7974 - val_loss: 4860034.9086\n",
            "Epoch 21/30\n",
            "2115/2115 [==============================] - 9s 4ms/sample - loss: 4731201.9868 - val_loss: 4866087.9503\n",
            "Epoch 22/30\n",
            "2115/2115 [==============================] - 9s 4ms/sample - loss: 4727332.5506 - val_loss: 4853450.6625\n",
            "Epoch 23/30\n",
            "2115/2115 [==============================] - 9s 4ms/sample - loss: 4727822.9187 - val_loss: 4856268.7427\n",
            "Epoch 24/30\n",
            "2115/2115 [==============================] - 8s 4ms/sample - loss: 4733370.0499 - val_loss: 4885255.2122\n",
            "Epoch 25/30\n",
            "2115/2115 [==============================] - 8s 4ms/sample - loss: 4726505.0681 - val_loss: 4867153.6084\n",
            "Epoch 26/30\n",
            "2115/2115 [==============================] - 8s 4ms/sample - loss: 4725479.1943 - val_loss: 4867314.3589\n",
            "Epoch 27/30\n",
            "2115/2115 [==============================] - 8s 4ms/sample - loss: 4718073.2931 - val_loss: 4883684.8804\n",
            "Epoch 28/30\n",
            "2115/2115 [==============================] - 8s 4ms/sample - loss: 4725478.6253 - val_loss: 4875402.7607\n",
            "Epoch 29/30\n",
            "2115/2115 [==============================] - 8s 4ms/sample - loss: 4714021.6092 - val_loss: 4870138.0034\n",
            "Epoch 30/30\n",
            "2115/2115 [==============================] - 8s 4ms/sample - loss: 4714952.6920 - val_loss: 4880059.8205\n",
            "(443, 22, 1000, 1)\n",
            "(443,)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Vex2eiIjkOGNMD2Aw8OlOT3UGVuzw/UrqJqy1aJheWoDQXj6X3XoXteelo3/C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\nQmCC\n",
            "text/plain": [
              "<Figure size 864x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "(1, 22, 1000, 1)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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TdVZ0mSzuMfDmJpz1+zeSkPB1XlLAPTtOUtgh/EQ0dGVhISFDuBPc53BOhCLAh4cBRU9s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\n22eIvPhBPj+ynbvVN7EF+ojb0UmTbnwywKaTGh2uU3XJQuiqQgYMRnC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jQZmehy8zlvF4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\n4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U3VNFpGWujya6yOTbHbbEx4D1s3fpL2+k2zhoNoSKQlYY3kPm0Y76CXJW2LWaC1zpFf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\nRqPRRIwWGhqNRqOJGC00NBqNRhMx/w9ZpYmylL7n3AAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pcG9QiJjX4Xl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cI04CdWJX4u5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pHtoKO9VJIPS",
        "colab_type": "code",
        "outputId": "1e88ab89-cf77-4db1-d5d9-a57e0ff3ece7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "#########################################\n",
        "### CNN VAE Model Loss for 500 epochs ###\n",
        "#########################################\n",
        "\n",
        "\n",
        "#Plot training & validation loss values\n",
        "fig = plt.figure()\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('Model loss')\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Train', 'Test'], loc='upper left')\n",
        "plt.savefig(\"Model_Loss.png\")\n",
        "\n",
        "plt.show()\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Px4lB36L3roGYRMhfCi9/C+Z8CEOnwE7vAbj1ffjtePjqn2HCZe4bfJk3v1Te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\nj8g4pYsAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}