vae_rene.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "from keras import backend as K\n",
    "import os\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import cv2\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "output = os. getcwd()+\"/output/scatt\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "&#39;/home/saehrimnir/Projects/students/jm/Thesis/output/scatt&#39;"
     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "source": [
    "output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class VAE(keras.Model):\n",
    "    def __init__(self, encoder, decoder, **kwargs):\n",
    "        super(VAE, self).__init__(**kwargs)\n",
    "        self.encoder = encoder\n",
    "        self.decoder = decoder\n",
    "\n",
    "    def train_step(self, data):\n",
    "        if isinstance(data, tuple):\n",
    "            data = data[0]\n",
    "        with tf.GradientTape() as tape:\n",
    "            z_mean, z_log_var, z = encoder(data)\n",
    "            reconstruction = decoder(z)\n",
    "            reconstruction_loss = tf.reduce_mean(\n",
    "                keras.losses.binary_crossentropy(data, reconstruction)\n",
    "            )\n",
    "            reconstruction_loss *= 128 * 128*3\n",
    "            kl_loss = 1 + z_log_var - tf.square(z_mean) - tf.exp(z_log_var)\n",
    "            kl_loss = tf.reduce_mean(kl_loss)\n",
    "            kl_loss *= -0.5\n",
    "            total_loss = reconstruction_loss + kl_loss\n",
    "        grads = tape.gradient(total_loss, self.trainable_weights)\n",
    "        self.optimizer.apply_gradients(zip(grads, self.trainable_weights))\n",
    "        return {\n",
    "            \"loss\": total_loss,\n",
    "            \"reconstruction_loss\": reconstruction_loss,\n",
    "            \"kl_loss\": kl_loss,\n",
    "        }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_data = []\n",
    "\n",
    "def create_training_data():\n",
    "    for img in os.listdir(output):\n",
    "        try:\n",
    "            img_array = cv2.imread(os.path.join(output,img))  # convert to array\n",
    "            img_array = img_array/ 255\n",
    "            training_data.append(img_array)  # add this to our training_data\n",
    "        except Exception as e:  # in the interest in keeping the output clean...\n",
    "            pass\n",
    "        \n",
    "create_training_data()\n",
    "training_data = np.array(training_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sampling(args):\n",
    "    z_mean, z_log_sigma = args\n",
    "    epsilon = K.random_normal(shape=(K.shape(z_mean)[0], latent_dim),\n",
    "                              mean=0., stddev=0.1)\n",
    "    return z_mean + K.exp(z_log_sigma) * epsilon\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "class SamplingLayer(tf.keras.layers.Layer):\n",
    "    def __init__(self):#, z_mean, z_log_sigma):\n",
    "        super(SamplingLayer, self).__init__()\n",
    "        #self.z_mean = z_mean\n",
    "        #self.z_log_sigma = z_log_sigma\n",
    "        \n",
    "    def call(self, inputs):\n",
    "        #epsilon = K.random_normal(shape=(K.shape(self.z_mean)[0], latent_dim), mean=0., stddev=0.1))\n",
    "        z_mean, z_log_sigma = inputs\n",
    "        return z_mean + K.exp(z_log_sigma) * K.random_normal(shape=(K.shape(z_mean)[0], latent_dim), mean=0., stddev=0.1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Model: &quot;encoder&quot;\n__________________________________________________________________________________________________\nLayer (type)                    Output Shape         Param #     Connected to                     \n==================================================================================================\ninput_1 (InputLayer)            [(None, 128, 128, 3) 0                                            \n__________________________________________________________________________________________________\nconv2d (Conv2D)                 (None, 64, 64, 64)   1792        input_1[0][0]                    \n__________________________________________________________________________________________________\nconv2d_1 (Conv2D)               (None, 32, 32, 64)   36928       conv2d[0][0]                     \n__________________________________________________________________________________________________\nmax_pooling2d (MaxPooling2D)    (None, 16, 16, 64)   0           conv2d_1[0][0]                   \n__________________________________________________________________________________________________\nflatten (Flatten)               (None, 16384)        0           max_pooling2d[0][0]              \n__________________________________________________________________________________________________\ndense (Dense)                   (None, 16)           262160      flatten[0][0]                    \n__________________________________________________________________________________________________\ndense_1 (Dense)                 (None, 7)            119         dense[0][0]                      \n__________________________________________________________________________________________________\ndense_2 (Dense)                 (None, 7)            119         dense[0][0]                      \n__________________________________________________________________________________________________\nsampling_layer (SamplingLayer)  (None, 7)            0           dense_1[0][0]                    \n                                                                 dense_2[0][0]                    \n==================================================================================================\nTotal params: 301,118\nTrainable params: 301,118\nNon-trainable params: 0\n__________________________________________________________________________________________________\n"
    }
   ],
   "source": [
    "original_dim = 128 * 128 * 1\n",
    "intermediate_dim = 128\n",
    "latent_dim = 7\n",
    "\n",
    "encoder_inputs = keras.Input(shape=(128, 128, 3))\n",
    "x = layers.Conv2D(64, 3, activation=\"relu\", strides=2, padding=\"same\")(encoder_inputs)\n",
    "x = layers.Conv2D(64, 3, activation=\"relu\", strides=2, padding=\"same\")(x)\n",
    "x = layers.MaxPool2D(pool_size=(2, 2), strides=None)(x)\n",
    "x = layers.Flatten()(x)\n",
    "x = layers.Dense(16, activation=\"relu\")(x)\n",
    "z_mean = layers.Dense(latent_dim)(x)\n",
    "z_log_sigma = layers.Dense(latent_dim)(x)\n",
    "# z = layers.Lambda(sampling)([z_mean, z_log_sigma])\n",
    "z = SamplingLayer()([z_mean, z_log_sigma])\n",
    "# Create encoder\n",
    "encoder = keras.Model(encoder_inputs, [z_mean, z_log_sigma, z], name='encoder')\n",
    "encoder.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Model: &quot;decoder&quot;\n_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\nz_sampling (InputLayer)      [(None, 7)]               0         \n_________________________________________________________________\ndense_3 (Dense)              (None, 16384)             131072    \n_________________________________________________________________\nreshape (Reshape)            (None, 16, 16, 64)        0         \n_________________________________________________________________\nconv2d_transpose (Conv2DTran (None, 32, 32, 64)        36928     \n_________________________________________________________________\nconv2d_transpose_1 (Conv2DTr (None, 64, 64, 32)        18464     \n_________________________________________________________________\nconv2d_transpose_2 (Conv2DTr (None, 128, 128, 32)      9248      \n_________________________________________________________________\nconv2d_transpose_3 (Conv2DTr (None, 128, 128, 3)       867       \n=================================================================\nTotal params: 196,579\nTrainable params: 196,579\nNon-trainable params: 0\n_________________________________________________________________\n"
    }
   ],
   "source": [
    "# Create decoder\n",
    "latent_inputs = keras.Input(shape=(latent_dim,), name='z_sampling')\n",
    "x = layers.Dense(16 * 16 * 64, activation=\"relu\")(latent_inputs)\n",
    "x = layers.Reshape((16, 16, 64))(x)\n",
    "x = layers.Conv2DTranspose(64, 3, activation=\"relu\", strides=2, padding=\"same\")(x)\n",
    "x = layers.Conv2DTranspose(32, 3, activation=\"relu\", strides=2, padding=\"same\")(x)\n",
    "x = layers.Conv2DTranspose(32, 3, activation=\"relu\", strides=2, padding=\"same\")(x)\n",
    "decoder_outputs = layers.Conv2DTranspose(3, 3, activation=\"sigmoid\", padding=\"same\")(x)\n",
    "decoder = keras.Model(latent_inputs, decoder_outputs, name=\"decoder\")\n",
    "\n",
    "decoder.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "reconstruction_loss = keras.losses.binary_crossentropy(encoder_inputs, outputs)\n",
    "reconstruction_loss *= original_dim\n",
    "kl_loss = 1 + z_log_sigma - K.square(z_mean) - K.exp(z_log_sigma)\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",
    "vae.compile(optimizer='adam')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# it crashes on my computer if i use the full data\n",
    "\n",
    "X_train, X_test = train_test_split(training_data[:-500], test_size=0.2)\n",
    "#x_train = X_train.reshape((len(X_train), np.prod(X_train.shape[1:])))\n",
    "#x_test = X_test.reshape((len(X_test), np.prod(X_test.shape[1:])))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "(3072, 128, 128, 3)"
     },
     "metadata": {},
     "execution_count": 11
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Epoch 1/50\n16/96 [====&gt;.........................] - ETA: 30s - loss: 55524.1945 - reconstruction_loss: 50148.5243 - kl_loss: 5375.6706"
    },
    {
     "output_type": "error",
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m&lt;ipython-input-12-7af1d292c540&gt;\u001b[0m in \u001b[0;36m&lt;module&gt;\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mvae\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moptimizer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptimizers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mAdam\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----&gt; 4\u001b[0;31m \u001b[0mvae\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m50\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36m_method_wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    106\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_method_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    107\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_in_multi_worker_mode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--&gt; 108\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    109\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    110\u001b[0m     \u001b[0;31m# Running inside `run_distribute_coordinator` already.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1096\u001b[0m                 batch_size=batch_size):\n\u001b[1;32m   1097\u001b[0m               \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-&gt; 1098\u001b[0;31m               \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1099\u001b[0m               \u001b[0;32mif\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_sync\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1100\u001b[0m                 \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masync_wait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    778\u001b[0m       \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    779\u001b[0m         \u001b[0mcompiler\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m&quot;nonXla&quot;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--&gt; 780\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    781\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    782\u001b[0m       \u001b[0mnew_tracing_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    805\u001b[0m       \u001b[0;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    806\u001b[0m       \u001b[0;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--&gt; 807\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateless_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=not-callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    808\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    809\u001b[0m       \u001b[0;31m# Release the lock early so that multiple threads can perform the call\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2827\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2828\u001b[0m       \u001b[0mgraph_function\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_define_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-&gt; 2829\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_filtered_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2830\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2831\u001b[0m   \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_filtered_call\u001b[0;34m(self, args, kwargs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1841\u001b[0m       \u001b[0;31m`\u001b[0m\u001b[0margs\u001b[0m\u001b[0;31m`\u001b[0m \u001b[0;32mand\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1842\u001b[0m     &quot;&quot;&quot;\n\u001b[0;32m-&gt; 1843\u001b[0;31m     return self._call_flat(\n\u001b[0m\u001b[1;32m   1844\u001b[0m         [t for t in nest.flatten((args, kwargs), expand_composites=True)\n\u001b[1;32m   1845\u001b[0m          if isinstance(t, (ops.Tensor,\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1921\u001b[0m         and executing_eagerly):\n\u001b[1;32m   1922\u001b[0m       \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-&gt; 1923\u001b[0;31m       return self._build_call_outputs(self._inference_function.call(\n\u001b[0m\u001b[1;32m   1924\u001b[0m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[1;32m   1925\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m    543\u001b[0m       \u001b[0;32mwith\u001b[0m \u001b[0m_InterpolateFunctionError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    544\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcancellation_manager\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--&gt; 545\u001b[0;31m           outputs = execute.execute(\n\u001b[0m\u001b[1;32m    546\u001b[0m               \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msignature\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    547\u001b[0m               \u001b[0mnum_outputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_outputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     57\u001b[0m   \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     58\u001b[0m     \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---&gt; 59\u001b[0;31m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0m\u001b[1;32m     60\u001b[0m                                         inputs, attrs, num_outputs)\n\u001b[1;32m     61\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "\n",
    "vae = VAE(encoder, decoder)\n",
    "vae.compile(optimizer=keras.optimizers.Adam())\n",
    "\n",
    "vae.fit(X_train, X_train,epochs=50,batch_size=32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n11493376/11490434 [==============================] - 4s 0us/step\n"
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "def plot_label_clusters(encoder, decoder, data):\n",
    "    # display a 2D plot of the digit classes in the latent space\n",
    "    z_mean, _, _ = encoder.predict(data)\n",
    "    plt.figure(figsize=(12, 10))\n",
    "    plt.scatter(z_mean[:, 4], z_mean[:, 1])\n",
    "    plt.colorbar()\n",
    "    plt.xlabel(\"z[0]\")\n",
    "    plt.ylabel(\"z[1]\")\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "(x_train, y_train), _ = keras.datasets.mnist.load_data()\n",
    "x_train = np.expand_dims(x_train, -1).astype(\"float32\") / 255\n",
    "\n",
    "plot_label_clusters(encoder, decoder, X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import tensorflowjs as tfjs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "tfjs.converters.save_keras_model(encoder, \"./vae/public/encoder\")\n",
    "tfjs.converters.save_keras_model(decoder, \"./vae/public/decoder\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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-final"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}