AE.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.python.keras import Input\n",
    "from tensorflow.python.keras.models import Model\n",
    "from tensorflow.python.keras.engine.keras_tensor import KerasTensor\n",
    "from tensorflow.python.keras.engine.functional import Functional\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.python.keras.regularizers import l2\n",
    "import os\n",
    "\n",
    "from tensorflow_model_optimization.python.core.sparsity.keras import prune, pruning_callbacks, pruning_schedule\n",
    "from tensorflow_model_optimization.sparsity.keras import strip_pruning, prune_low_magnitude\n",
    "\n",
    "from callbacks import all_callbacks\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import the MNIST data class and call it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Original dataset and modified dataset with half resolution is shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 100)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import MNIST_dataset as mnist\n",
    "size_final = 10\n",
    "\n",
    "data_zoom = mnist.MNISTData(size_final=size_final, color_depth=5)\n",
    "test = data_zoom.x_test\n",
    "\n",
    "plt.imshow(test[0].reshape(size_final,size_final), cmap='gray')\n",
    "print(data_zoom.x_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "float64\n"
     ]
    }
   ],
   "source": [
    "print(data_zoom.x_train.dtype)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "markdown",
     "checksum": "cec58d97b4c05e29e9afdeb102569f20",
     "grade": false,
     "grade_id": "cell-6ee6a27aac2a7e5b",
     "locked": true,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "source": [
    "# Autoencoder using Keras\n",
    "The aim of this class is to implement a simple autoencoder for the MNIST data and then build an autoencoder that is\n",
    "able to classify MNIST data in its latent dimension.\n",
    "\n",
    "Code partially adapted from [Keras Documentation](https://blog.keras.io/building-autoencoders-in-keras.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "8f093c3123887a615df510d5cb9d3bee",
     "grade": false,
     "grade_id": "cell-b3452809a3ed4ee3",
     "locked": true,
     "schema_version": 3,
     "solution": false,
     "task": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "class Autoencoder:\n",
    "    \"\"\"Autoencoder Class\"\"\"\n",
    "\n",
    "    def __init__(self, data: mnist.MNISTData, num_classes=10, pruned = False):\n",
    "        self.x_train = data.x_train\n",
    "        self.x_test = data.x_test\n",
    "        self.y_train = data.y_train\n",
    "        self.y_test = data.y_test\n",
    "\n",
    "        self.num_classes = num_classes\n",
    "        self.pruned = pruned\n",
    "        self.input_shape = (self.x_train.shape[-1],)\n",
    "        self.input = Input(shape=self.input_shape, name='encoder_input')\n",
    "        self.latent_dim = 2\n",
    "\n",
    "        self.encoder = None\n",
    "        self.encoder_model = None\n",
    "        self.decoder = None\n",
    "        self.latent_classifier = None\n",
    "        self.autoencoder = None\n",
    "\n",
    "        self.history = None\n",
    "\n",
    "        self.pruning_params = {\"pruning_schedule\" : pruning_schedule.ConstantSparsity(0.75, begin_step=2000, frequency=100)}\n",
    "    \n",
    "    def build_encoder(self):\n",
    "        \"\"\"Build your encoder architecture and store the output in self.encoder. \n",
    "        The final encoding dimension is 2.\"\"\"\n",
    "\n",
    "        self.encoder = Dense(32,  activation='relu')(self.input)\n",
    "        \n",
    "        if self.pruned:\n",
    "            self.encoder = prune_low_magnitude(Dense(16,  activation='relu'), **self.pruning_params)(self.encoder) \n",
    "        \n",
    "        if not self.pruned:\n",
    "            self.encoder = Dense(16,  activation='relu')(self.encoder)   \n",
    "\n",
    "        self.encoder = Dense(self.latent_dim,  activation='relu', name = 'encoder_output')(self.encoder)\n",
    "        #building a model for the encoder in order to be able to predict and plot the latent dimension\n",
    "        self.encoder_model = Model(self.input, self.encoder, name='encoder')\n",
    "\n",
    "    def build_decoder(self):\n",
    "        \"\"\"Build the decoder architecture.\"\"\"\n",
    "        if self.encoder is None:\n",
    "            raise RuntimeError(\"The encoder has to be built before you can build the decoder!\")\n",
    "        pruning_params = {\"pruning_schedule\" : pruning_schedule.ConstantSparsity(0.75, begin_step=250, frequency=100)}\n",
    "\n",
    "        if self.pruned:\n",
    "            self.decoder = prune_low_magnitude(Dense(16,  activation='relu'), **self.pruning_params)(self.encoder)\n",
    "            #self.decoder = prune_low_magnitude(Dense(32,  activation='relu'), **self.pruning_params)(self.decoder)\n",
    "\n",
    "        if not self.pruned:\n",
    "            self.decoder = Dense(16, activation='relu')(self.encoder)\n",
    "        self.decoder = Dense(32,activation='relu')(self.decoder)     \n",
    "        \n",
    "        self.decoder = Dense(self.input_shape[0],activation='sigmoid', name='ecoder_output')(self.decoder)\n",
    "\n",
    "    def build_classifier(self):\n",
    "        \"\"\" Building the classifier architecture.\"\"\"\n",
    "        if self.encoder is None:\n",
    "            raise RuntimeError(\"The encoder has to be built before you can build the classifier!\")\n",
    "        \n",
    "        if self.pruned:\n",
    "            self.latent_classifier = prune_low_magnitude(Dense(16,  activation='relu'), **self.pruning_params)(self.encoder)\n",
    "            #self.latent_classifier = prune_low_magnitude(Dense(32,  activation='relu'), **self.pruning_params)(self.latent_classifier)\n",
    "        \n",
    "        if not self.pruned:\n",
    "            self.latent_classifier = Dense(16, activation='relu')(self.encoder)\n",
    "        \n",
    "        self.latent_classifier = Dense(32, activation='relu')(self.latent_classifier)\n",
    "        self.latent_classifier = Dense(self.num_classes, activation='softmax',name='classifier_output')(self.latent_classifier)\n",
    "\n",
    "    def build_autoencoder(self, use_latent_classifier=False):\n",
    "        \"\"\" Build the whole autoencoder Model from self.encoder and self.decoder.\"\"\"\n",
    "        self.dir()\n",
    "        \n",
    "        if self.encoder is None:\n",
    "            raise RuntimeError(\"The encoder has to be built before you can build the autoencoder!\")\n",
    "        if self.decoder is None:\n",
    "            raise RuntimeError(\"The decoder has to be built before you can build the autoencoder!\")\n",
    "        \n",
    "        if use_latent_classifier:\n",
    "            if self.latent_classifier is None:\n",
    "                raise RuntimeError(\"If you want to use the option with the latent classifier, you have to build it \"\n",
    "                                   \"beforehand!\")\n",
    "            self.autoencoder = Model(self.input, outputs=[self.decoder, self.latent_classifier])\n",
    "            self.autoencoder.compile(loss=['mse', 'categorical_crossentropy'], loss_weights=[1, 0.1], optimizer='adam',\n",
    "                                     metrics=\"accuracy\")\n",
    "            if not self.pruned:\n",
    "                self.autoencoder.save('model/AE_model/KERAS_check_model_w_classifier.h5')\n",
    "            if self.pruned:\n",
    "                self.autoencoder.save('model/AE_model/KERAS_check_pruned_model_w_classifier.h5')\n",
    "    \n",
    "        else:\n",
    "            self.autoencoder = Model(self.input, outputs=self.decoder, name = 'autoencoder')\n",
    "            self.autoencoder.compile(loss='mse', optimizer='adam') \n",
    "            if not self.pruned:\n",
    "                self.autoencoder.save('model/AE_model/KERAS_check_model_wo_classifier.h5')\n",
    "            if self.pruned:\n",
    "                self.autoencoder.save('model/AE_model/KERAS_check_pruned_model_wo_classifier.h5')\n",
    "        self.autoencoder.summary()\n",
    "\n",
    "    def fit_data(self, batch_size=256, epochs=40, use_latent_classifier=False):\n",
    "        \"\"\"Write the fit function for the autoencoder. \n",
    "        Storing the fit history in self.history to be able to plot the fitting scores.\"\"\"\n",
    "        \n",
    "        callbacks = all_callbacks(stop_patience = 1000,\n",
    "                              lr_factor = 0.5,\n",
    "                              lr_patience = 10,\n",
    "                              lr_epsilon = 0.000001,\n",
    "                              lr_cooldown = 2,\n",
    "                              lr_minimum = 0.0000001,\n",
    "                              outputDir = 'model/AE_model/model_2')\n",
    "        callbacks.callbacks.append(pruning_callbacks.UpdatePruningStep()) \n",
    "        \n",
    "        if use_latent_classifier:\n",
    "            self.history = self.autoencoder.fit(self.x_train, [self.x_train, self.y_train],\n",
    "                                                validation_data=(self.x_test, [self.x_test, self.y_test]),\n",
    "                                                batch_size=batch_size, epochs=epochs,\n",
    "                                                shuffle=True, callbacks=callbacks.callbacks\n",
    "                                                )\n",
    "            if not self.pruned:\n",
    "                self.autoencoder.save('model/AE_model/KERAS_check_best_model_classifier.h5')\n",
    "            if self.pruned:\n",
    "                self.autoencoder.save('model/AE_model/KERAS_check_pruned_best_model_classifier.h5')\n",
    "        else:                         \n",
    "            self.history = self.autoencoder.fit(self.x_train, self.x_train,\n",
    "                                                validation_data=(self.x_test, self.x_test),\n",
    "                                                batch_size=batch_size, epochs=epochs,\n",
    "                                                shuffle=True, callbacks=callbacks.callbacks)  \n",
    "            self.autoencoder = strip_pruning(self.autoencoder)\n",
    "            if not self.pruned:\n",
    "                self.autoencoder.save('model/AE_model/KERAS_check_best_model.h5')\n",
    "            if self.pruned:\n",
    "                self.autoencoder.save('model/AE_model/KERAS_check_pruned_best_model.h5')\n",
    "\n",
    "        self.history = self.history.history\n",
    "\n",
    "    def dir(self):\n",
    "        \"\"\"Creation of the folders path to store the results.\"\"\"\n",
    "        dir = os.path.join(\"images\")\n",
    "        if not os.path.exists(dir):\n",
    "            os.mkdir(dir)\n",
    "        dir2 = os.path.join(\"./images/AE\")\n",
    "        if not os.path.exists(dir2):\n",
    "            os.mkdir(dir2)\n",
    "        dir3 = os.path.join(\"model\")\n",
    "        if not os.path.exists(dir3):\n",
    "            os.mkdir(dir3)\n",
    "        dir4 = os.path.join(\"./model/AE_model\")\n",
    "        if not os.path.exists(dir4):\n",
    "            os.mkdir(dir4)\n",
    "        \n",
    "    \n",
    "    def plot_score(self, use_latent_classifier = False, model_name=None):\n",
    "        \"\"\"Plots the scores achieved during the fitting.\"\"\"\n",
    "        if use_latent_classifier is False:\n",
    "            plt.plot(self.history['loss'])\n",
    "            plt.plot(self.history['val_loss'])\n",
    "            plt.ylabel('Model Accuracy')\n",
    "        else:\n",
    "            plt.plot(self.history['classifier_output_accuracy'])\n",
    "            plt.plot(self.history['val_classifier_output_accuracy'])\n",
    "            plt.ylabel('Classifier Accuracy')\n",
    "        \n",
    "        plt.xlabel('Epoch')        \n",
    "        plt.legend(['Train', 'Test'], loc='best')\n",
    "        plt.title('Accuracy of {model_name}'.format(model_name=model_name))\n",
    "        \n",
    "        plt.savefig('./images/AE/Accuracy of {model_name}.png'.format(model_name=model_name))\n",
    "        \n",
    "        plt.show()\n",
    "\n",
    "    def plot_latent_dimension(self, model_name = None):\n",
    "        \"\"\"Plots the latent dimension of the autoencoder.\"\"\"\n",
    "        prediction = self.encoder_model.predict(self.x_test)\n",
    "        fig = plt.figure(figsize=(10, 10))\n",
    "        fig.patch.set_facecolor(\"white\")\n",
    "        plt.scatter(prediction[:, 0], prediction[:, 1], c=np.argmax(self.y_test, axis=1), cmap=\"Set3\")\n",
    "        plt.colorbar()\n",
    "        plt.title('Latent space {model_name}'.format(model_name=model_name))\n",
    "\n",
    "\n",
    "        plt.savefig('./images/AE/Latent dimension of {model_name}.png'.format(model_name=model_name))\n",
    "\n",
    "        plt.show()\n",
    "\n",
    "    def plot_reco(self, use_latent_classifier = False):\n",
    "        \"\"\"Plotting reconstructed images\"\"\"\n",
    "        \n",
    "        n = 6\n",
    "        plt.figure(figsize=(10, 3))\n",
    "        reco_imgs = self.autoencoder.predict(self.x_test)\n",
    "        img_size = int(np.sqrt(self.input_shape[0]))\n",
    "\n",
    "        \n",
    "        if use_latent_classifier :\n",
    "            raise RuntimeError(\"You are considering the autoencoder with classifier!\")\n",
    "        else:\n",
    "        \n",
    "            for i in range(n):\n",
    "                # Display original MNIST\n",
    "                ax = plt.subplot(2, n, i + 1)\n",
    "                plt.imshow(self.x_test[i].reshape(img_size,img_size), cmap='gray_r')\n",
    "                ax.get_xaxis().set_visible(False)\n",
    "                ax.get_yaxis().set_visible(False)\n",
    "\n",
    "                # Display reconstructed MNIST            \n",
    "                ax = plt.subplot(2, n, i + n + 1)\n",
    "                plt.imshow(reco_imgs[i].reshape(img_size,img_size), cmap='gray_r')\n",
    "                ax.get_xaxis().set_visible(False)\n",
    "                ax.get_yaxis().set_visible(False)\n",
    "            plt.savefig('./images/AE/reconstructed images.png')\n",
    "            plt.show()\n",
    " \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is built the standard autoencoder, *without classifier* here, and the MNIST data class called before is used. \n",
    "The model is fitted and adequate plots are reproduced, \n",
    "as well as the comparison between orginal MNIST dataset and reconstructed images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"autoencoder\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "encoder_input (InputLayer)   [(None, 100)]             0         \n",
      "_________________________________________________________________\n",
      "dense_46 (Dense)             (None, 32)                3232      \n",
      "_________________________________________________________________\n",
      "dense_47 (Dense)             (None, 16)                528       \n",
      "_________________________________________________________________\n",
      "encoder_output (Dense)       (None, 2)                 34        \n",
      "_________________________________________________________________\n",
      "dense_48 (Dense)             (None, 16)                48        \n",
      "_________________________________________________________________\n",
      "dense_49 (Dense)             (None, 32)                544       \n",
      "_________________________________________________________________\n",
      "ecoder_output (Dense)        (None, 100)               3300      \n",
      "=================================================================\n",
      "Total params: 7,686\n",
      "Trainable params: 7,686\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "WARNING:tensorflow:`epsilon` argument is deprecated and will be removed, use `min_delta` instead.\n",
      "WARNING:tensorflow:`period` argument is deprecated. Please use `save_freq` to specify the frequency in number of batches seen.\n",
      "Epoch 1/20\n",
      "  2/235 [..............................] - ETA: 2:06 - loss: 0.2077WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0000s vs `on_train_batch_end` time: 1.0931s). Check your callbacks.\n",
      "213/235 [==========================>...] - ETA: 0s - loss: 0.1210\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 0.09099, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 0.09099, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00001: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00001: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 2s 8ms/step - loss: 0.1183 - val_loss: 0.0910\n",
      "Epoch 2/20\n",
      "200/235 [========================>.....] - ETA: 0s - loss: 0.0885\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00002: val_loss improved from 0.09099 to 0.08557, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00002: val_loss improved from 0.09099 to 0.08557, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00002: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00002: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 3ms/step - loss: 0.0881 - val_loss: 0.0856\n",
      "Epoch 3/20\n",
      "227/235 [===========================>..] - ETA: 0s - loss: 0.0839\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00003: val_loss improved from 0.08557 to 0.08190, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00003: val_loss improved from 0.08557 to 0.08190, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00003: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00003: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 4ms/step - loss: 0.0839 - val_loss: 0.0819\n",
      "Epoch 4/20\n",
      "228/235 [============================>.] - ETA: 0s - loss: 0.0815\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00004: val_loss improved from 0.08190 to 0.08024, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00004: val_loss improved from 0.08190 to 0.08024, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00004: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00004: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 3ms/step - loss: 0.0815 - val_loss: 0.0802\n",
      "Epoch 5/20\n",
      "231/235 [============================>.] - ETA: 0s - loss: 0.0802\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00005: val_loss improved from 0.08024 to 0.07887, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00005: val_loss improved from 0.08024 to 0.07887, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00005: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00005: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 3ms/step - loss: 0.0802 - val_loss: 0.0789\n",
      "Epoch 6/20\n",
      "212/235 [==========================>...] - ETA: 0s - loss: 0.0789\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00006: val_loss improved from 0.07887 to 0.07733, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00006: val_loss improved from 0.07887 to 0.07733, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00006: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00006: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 3ms/step - loss: 0.0787 - val_loss: 0.0773\n",
      "Epoch 7/20\n",
      "230/235 [============================>.] - ETA: 0s - loss: 0.0772\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00007: val_loss improved from 0.07733 to 0.07605, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00007: val_loss improved from 0.07733 to 0.07605, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00007: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00007: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 3ms/step - loss: 0.0771 - val_loss: 0.0761\n",
      "Epoch 8/20\n",
      "229/235 [============================>.] - ETA: 0s - loss: 0.0759\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00008: val_loss improved from 0.07605 to 0.07492, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00008: val_loss improved from 0.07605 to 0.07492, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00008: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00008: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 3ms/step - loss: 0.0759 - val_loss: 0.0749\n",
      "Epoch 9/20\n",
      "212/235 [==========================>...] - ETA: 0s - loss: 0.0748\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00009: val_loss improved from 0.07492 to 0.07358, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00009: val_loss improved from 0.07492 to 0.07358, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00009: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00009: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 4ms/step - loss: 0.0748 - val_loss: 0.0736\n",
      "Epoch 10/20\n",
      "218/235 [==========================>...] - ETA: 0s - loss: 0.0736\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00010: val_loss improved from 0.07358 to 0.07276, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00010: val_loss improved from 0.07358 to 0.07276, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00010: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00010: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "Epoch 00010: saving model to model/AE_model/model_2\\KERAS_check_model_epoch10.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 4ms/step - loss: 0.0736 - val_loss: 0.0728\n",
      "Epoch 11/20\n",
      "229/235 [============================>.] - ETA: 0s - loss: 0.0729\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00011: val_loss improved from 0.07276 to 0.07211, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00011: val_loss improved from 0.07276 to 0.07211, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00011: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00011: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 3ms/step - loss: 0.0729 - val_loss: 0.0721\n",
      "Epoch 12/20\n",
      "215/235 [==========================>...] - ETA: 0s - loss: 0.0725\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00012: val_loss improved from 0.07211 to 0.07183, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00012: val_loss improved from 0.07211 to 0.07183, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00012: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00012: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 3ms/step - loss: 0.0724 - val_loss: 0.0718\n",
      "Epoch 13/20\n",
      "217/235 [==========================>...] - ETA: 0s - loss: 0.0720\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00013: val_loss improved from 0.07183 to 0.07141, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00013: val_loss improved from 0.07183 to 0.07141, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00013: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00013: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 3ms/step - loss: 0.0720 - val_loss: 0.0714\n",
      "Epoch 14/20\n",
      "233/235 [============================>.] - ETA: 0s - loss: 0.0717\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00014: val_loss improved from 0.07141 to 0.07117, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00014: val_loss improved from 0.07141 to 0.07117, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00014: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00014: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 3ms/step - loss: 0.0717 - val_loss: 0.0712\n",
      "Epoch 15/20\n",
      "224/235 [===========================>..] - ETA: 0s - loss: 0.0714\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00015: val_loss improved from 0.07117 to 0.07088, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00015: val_loss improved from 0.07117 to 0.07088, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00015: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00015: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 3ms/step - loss: 0.0714 - val_loss: 0.0709\n",
      "Epoch 16/20\n",
      "216/235 [==========================>...] - ETA: 0s - loss: 0.0711\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00016: val_loss improved from 0.07088 to 0.07063, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00016: val_loss improved from 0.07088 to 0.07063, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00016: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00016: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 3ms/step - loss: 0.0711 - val_loss: 0.0706\n",
      "Epoch 17/20\n",
      "227/235 [===========================>..] - ETA: 0s - loss: 0.0709\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00017: val_loss improved from 0.07063 to 0.07043, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00017: val_loss improved from 0.07063 to 0.07043, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00017: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00017: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 3ms/step - loss: 0.0709 - val_loss: 0.0704\n",
      "Epoch 18/20\n",
      "227/235 [===========================>..] - ETA: 0s - loss: 0.0706\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00018: val_loss improved from 0.07043 to 0.07012, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00018: val_loss improved from 0.07043 to 0.07012, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00018: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00018: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 3ms/step - loss: 0.0706 - val_loss: 0.0701\n",
      "Epoch 19/20\n",
      "235/235 [==============================] - ETA: 0s - loss: 0.0704\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00019: val_loss improved from 0.07012 to 0.06999, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00019: val_loss improved from 0.07012 to 0.06999, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00019: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00019: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 3ms/step - loss: 0.0704 - val_loss: 0.0700\n",
      "Epoch 20/20\n",
      "233/235 [============================>.] - ETA: 0s - loss: 0.0701\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00020: val_loss improved from 0.06999 to 0.06979, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00020: val_loss improved from 0.06999 to 0.06979, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00020: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00020: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "Epoch 00020: saving model to model/AE_model/model_2\\KERAS_check_model_epoch20.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 4ms/step - loss: 0.0701 - val_loss: 0.0698\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x216 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "network = Autoencoder(data_zoom, pruned=False)\n",
    "network.build_encoder()\n",
    "network.build_decoder()\n",
    "network.build_autoencoder()\n",
    "network.fit_data(epochs=20)\n",
    "network.plot_latent_dimension(\"Autoencoder without classifier\")\n",
    "network.plot_reco()\n",
    "network.plot_score(use_latent_classifier=False,model_name=\"Autoencoder without classifier\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"autoencoder\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "encoder_input (InputLayer)   [(None, 100)]             0         \n",
      "_________________________________________________________________\n",
      "dense_102 (Dense)            (None, 32)                3232      \n",
      "_________________________________________________________________\n",
      "prune_low_magnitude_dense_10 (None, 16)                1042      \n",
      "_________________________________________________________________\n",
      "encoder_output (Dense)       (None, 2)                 34        \n",
      "_________________________________________________________________\n",
      "prune_low_magnitude_dense_10 (None, 16)                82        \n",
      "_________________________________________________________________\n",
      "dense_105 (Dense)            (None, 32)                544       \n",
      "_________________________________________________________________\n",
      "ecoder_output (Dense)        (None, 100)               3300      \n",
      "=================================================================\n",
      "Total params: 8,234\n",
      "Trainable params: 7,686\n",
      "Non-trainable params: 548\n",
      "_________________________________________________________________\n",
      "WARNING:tensorflow:`epsilon` argument is deprecated and will be removed, use `min_delta` instead.\n",
      "WARNING:tensorflow:`period` argument is deprecated. Please use `save_freq` to specify the frequency in number of batches seen.\n",
      "Epoch 1/20\n",
      "WARNING:tensorflow:AutoGraph could not transform <function Pruning.conditional_mask_update.<locals>.maybe_update_masks at 0x000002C6D2C27040> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: module 'gast' has no attribute 'Index'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function Pruning.conditional_mask_update.<locals>.maybe_update_masks at 0x000002C6D2C27040> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: module 'gast' has no attribute 'Index'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING:tensorflow:AutoGraph could not transform <function Pruning.conditional_mask_update.<locals>.maybe_update_masks at 0x000002C6CC41A8B0> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: module 'gast' has no attribute 'Index'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function Pruning.conditional_mask_update.<locals>.maybe_update_masks at 0x000002C6CC41A8B0> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: module 'gast' has no attribute 'Index'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING:tensorflow:AutoGraph could not transform <function Pruning.conditional_mask_update.<locals>.maybe_update_masks at 0x000002C6CD279700> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: module 'gast' has no attribute 'Index'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function Pruning.conditional_mask_update.<locals>.maybe_update_masks at 0x000002C6CD279700> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: module 'gast' has no attribute 'Index'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING:tensorflow:AutoGraph could not transform <function Pruning.conditional_mask_update.<locals>.maybe_update_masks at 0x000002C6D270C700> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: module 'gast' has no attribute 'Index'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function Pruning.conditional_mask_update.<locals>.maybe_update_masks at 0x000002C6D270C700> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: module 'gast' has no attribute 'Index'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "  2/235 [..............................] - ETA: 2:03 - loss: 0.2070WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0000s vs `on_train_batch_end` time: 1.0559s). Check your callbacks.\n",
      "229/235 [============================>.] - ETA: 0s - loss: 0.1201\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 0.09853, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 0.09853, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00001: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00001: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 2s 9ms/step - loss: 0.1196 - val_loss: 0.0985\n",
      "Epoch 2/20\n",
      "189/235 [=======================>......] - ETA: 0s - loss: 0.0958\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00002: val_loss improved from 0.09853 to 0.08951, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00002: val_loss improved from 0.09853 to 0.08951, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00002: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00002: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 2ms/step - loss: 0.0949 - val_loss: 0.0895\n",
      "Epoch 3/20\n",
      "227/235 [===========================>..] - ETA: 0s - loss: 0.0861\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00003: val_loss improved from 0.08951 to 0.08367, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00003: val_loss improved from 0.08951 to 0.08367, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00003: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00003: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 3ms/step - loss: 0.0861 - val_loss: 0.0837\n",
      "Epoch 4/20\n",
      "205/235 [=========================>....] - ETA: 0s - loss: 0.0821\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00004: val_loss improved from 0.08367 to 0.07974, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00004: val_loss improved from 0.08367 to 0.07974, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00004: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00004: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 3ms/step - loss: 0.0819 - val_loss: 0.0797\n",
      "Epoch 5/20\n",
      "215/235 [==========================>...] - ETA: 0s - loss: 0.0793\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00005: val_loss improved from 0.07974 to 0.07822, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00005: val_loss improved from 0.07974 to 0.07822, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00005: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00005: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 3ms/step - loss: 0.0793 - val_loss: 0.0782\n",
      "Epoch 6/20\n",
      "207/235 [=========================>....] - ETA: 0s - loss: 0.0779\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00006: val_loss improved from 0.07822 to 0.07679, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00006: val_loss improved from 0.07822 to 0.07679, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00006: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00006: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 3ms/step - loss: 0.0778 - val_loss: 0.0768\n",
      "Epoch 7/20\n",
      "202/235 [========================>.....] - ETA: 0s - loss: 0.0766\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00007: val_loss improved from 0.07679 to 0.07572, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00007: val_loss improved from 0.07679 to 0.07572, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00007: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00007: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 0s 2ms/step - loss: 0.0764 - val_loss: 0.0757\n",
      "Epoch 8/20\n",
      "191/235 [=======================>......] - ETA: 0s - loss: 0.0754\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00008: val_loss improved from 0.07572 to 0.07485, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00008: val_loss improved from 0.07572 to 0.07485, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00008: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00008: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 0s 2ms/step - loss: 0.0753 - val_loss: 0.0749\n",
      "Epoch 9/20\n",
      "223/235 [===========================>..] - ETA: 0s - loss: 0.0769\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00009: val_loss did not improve from 0.07485\n",
      "\n",
      "Epoch 00009: val_loss did not improve from 0.07485\n",
      "\n",
      "Epoch 00009: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00009: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 3ms/step - loss: 0.0768 - val_loss: 0.0752\n",
      "Epoch 10/20\n",
      "225/235 [===========================>..] - ETA: 0s - loss: 0.0745\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00010: val_loss improved from 0.07485 to 0.07383, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00010: val_loss improved from 0.07485 to 0.07383, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00010: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00010: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "Epoch 00010: saving model to model/AE_model/model_2\\KERAS_check_model_epoch10.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 4ms/step - loss: 0.0745 - val_loss: 0.0738\n",
      "Epoch 11/20\n",
      "209/235 [=========================>....] - ETA: 0s - loss: 0.0735\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00011: val_loss improved from 0.07383 to 0.07297, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00011: val_loss improved from 0.07383 to 0.07297, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00011: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00011: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 3ms/step - loss: 0.0734 - val_loss: 0.0730\n",
      "Epoch 12/20\n",
      "213/235 [==========================>...] - ETA: 0s - loss: 0.0727\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00012: val_loss improved from 0.07297 to 0.07234, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00012: val_loss improved from 0.07297 to 0.07234, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00012: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00012: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 3ms/step - loss: 0.0727 - val_loss: 0.0723\n",
      "Epoch 13/20\n",
      "213/235 [==========================>...] - ETA: 0s - loss: 0.0721\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00013: val_loss improved from 0.07234 to 0.07178, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00013: val_loss improved from 0.07234 to 0.07178, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00013: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00013: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 0s 2ms/step - loss: 0.0721 - val_loss: 0.0718\n",
      "Epoch 14/20\n",
      "226/235 [===========================>..] - ETA: 0s - loss: 0.0716\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00014: val_loss improved from 0.07178 to 0.07127, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00014: val_loss improved from 0.07178 to 0.07127, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00014: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00014: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 3ms/step - loss: 0.0716 - val_loss: 0.0713\n",
      "Epoch 15/20\n",
      "228/235 [============================>.] - ETA: 0s - loss: 0.0711\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00015: val_loss improved from 0.07127 to 0.07093, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00015: val_loss improved from 0.07127 to 0.07093, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00015: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00015: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 4ms/step - loss: 0.0711 - val_loss: 0.0709\n",
      "Epoch 16/20\n",
      "229/235 [============================>.] - ETA: 0s - loss: 0.0707\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00016: val_loss improved from 0.07093 to 0.07054, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00016: val_loss improved from 0.07093 to 0.07054, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00016: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00016: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 3ms/step - loss: 0.0707 - val_loss: 0.0705\n",
      "Epoch 17/20\n",
      "231/235 [============================>.] - ETA: 0s - loss: 0.0704\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00017: val_loss improved from 0.07054 to 0.07024, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00017: val_loss improved from 0.07054 to 0.07024, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00017: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00017: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 3ms/step - loss: 0.0704 - val_loss: 0.0702\n",
      "Epoch 18/20\n",
      "210/235 [=========================>....] - ETA: 0s - loss: 0.0702\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00018: val_loss improved from 0.07024 to 0.07004, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00018: val_loss improved from 0.07024 to 0.07004, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00018: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00018: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 2ms/step - loss: 0.0701 - val_loss: 0.0700\n",
      "Epoch 19/20\n",
      "227/235 [===========================>..] - ETA: 0s - loss: 0.0698\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00019: val_loss improved from 0.07004 to 0.06990, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00019: val_loss improved from 0.07004 to 0.06990, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00019: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00019: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 2ms/step - loss: 0.0698 - val_loss: 0.0699\n",
      "Epoch 20/20\n",
      "228/235 [============================>.] - ETA: 0s - loss: 0.0696\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00020: val_loss improved from 0.06990 to 0.06970, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00020: val_loss improved from 0.06990 to 0.06970, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00020: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00020: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "Epoch 00020: saving model to model/AE_model/model_2\\KERAS_check_model_epoch20.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 3ms/step - loss: 0.0696 - val_loss: 0.0697\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x216 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "network = Autoencoder(data_zoom, pruned=True)\n",
    "network.build_encoder()\n",
    "network.build_decoder()\n",
    "network.build_autoencoder()\n",
    "network.fit_data(epochs=20)\n",
    "network.plot_latent_dimension(\"compressed AE without classifier\")\n",
    "network.plot_reco()\n",
    "network.plot_score(use_latent_classifier=False,model_name=\"compressed AE without classifier\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Repeating the same procedure for the autoencoder with classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"functional_11\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "encoder_input (InputLayer)      [(None, 100)]        0                                            \n",
      "__________________________________________________________________________________________________\n",
      "dense_62 (Dense)                (None, 32)           3232        encoder_input[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dense_63 (Dense)                (None, 16)           528         dense_62[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "encoder_output (Dense)          (None, 2)            34          dense_63[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dense_64 (Dense)                (None, 16)           48          encoder_output[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "dense_66 (Dense)                (None, 16)           48          encoder_output[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "dense_65 (Dense)                (None, 32)           544         dense_64[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dense_67 (Dense)                (None, 32)           544         dense_66[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "ecoder_output (Dense)           (None, 100)          3300        dense_65[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "classifier_output (Dense)       (None, 10)           330         dense_67[0][0]                   \n",
      "==================================================================================================\n",
      "Total params: 8,608\n",
      "Trainable params: 8,608\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n",
      "WARNING:tensorflow:`epsilon` argument is deprecated and will be removed, use `min_delta` instead.\n",
      "WARNING:tensorflow:`period` argument is deprecated. Please use `save_freq` to specify the frequency in number of batches seen.\n",
      "Epoch 1/20\n",
      "  2/235 [..............................] - ETA: 2:13 - loss: 0.4387 - ecoder_output_loss: 0.2084 - classifier_output_loss: 2.3032 - ecoder_output_accuracy: 0.0254 - classifier_output_accuracy: 0.1094WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0000s vs `on_train_batch_end` time: 1.1449s). Check your callbacks.\n",
      "220/235 [===========================>..] - ETA: 0s - loss: 0.2914 - ecoder_output_loss: 0.1192 - classifier_output_loss: 1.7223 - ecoder_output_accuracy: 0.0238 - classifier_output_accuracy: 0.3421\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 0.21373, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 0.21373, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00001: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00001: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 2s 10ms/step - loss: 0.2867 - ecoder_output_loss: 0.1175 - classifier_output_loss: 1.6917 - ecoder_output_accuracy: 0.0233 - classifier_output_accuracy: 0.3541 - val_loss: 0.2137 - val_ecoder_output_loss: 0.0901 - val_classifier_output_loss: 1.2359 - val_ecoder_output_accuracy: 0.0142 - val_classifier_output_accuracy: 0.5250\n",
      "Epoch 2/20\n",
      "204/235 [=========================>....] - ETA: 0s - loss: 0.1997 - ecoder_output_loss: 0.0896 - classifier_output_loss: 1.1006 - ecoder_output_accuracy: 0.0119 - classifier_output_accuracy: 0.5852\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00002: val_loss improved from 0.21373 to 0.18426, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00002: val_loss improved from 0.21373 to 0.18426, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00002: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00002: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 2ms/step - loss: 0.1978 - ecoder_output_loss: 0.0895 - classifier_output_loss: 1.0835 - ecoder_output_accuracy: 0.0120 - classifier_output_accuracy: 0.5940 - val_loss: 0.1843 - val_ecoder_output_loss: 0.0871 - val_classifier_output_loss: 0.9719 - val_ecoder_output_accuracy: 0.0125 - val_classifier_output_accuracy: 0.6539\n",
      "Epoch 3/20\n",
      "215/235 [==========================>...] - ETA: 0s - loss: 0.1743 - ecoder_output_loss: 0.0869 - classifier_output_loss: 0.8737 - ecoder_output_accuracy: 0.0186 - classifier_output_accuracy: 0.7019\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00003: val_loss improved from 0.18426 to 0.16380, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00003: val_loss improved from 0.18426 to 0.16380, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00003: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00003: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 3ms/step - loss: 0.1736 - ecoder_output_loss: 0.0869 - classifier_output_loss: 0.8666 - ecoder_output_accuracy: 0.0188 - classifier_output_accuracy: 0.7052 - val_loss: 0.1638 - val_ecoder_output_loss: 0.0848 - val_classifier_output_loss: 0.7897 - val_ecoder_output_accuracy: 0.0196 - val_classifier_output_accuracy: 0.7499\n",
      "Epoch 4/20\n",
      "209/235 [=========================>....] - ETA: 0s - loss: 0.1579 - ecoder_output_loss: 0.0851 - classifier_output_loss: 0.7277 - ecoder_output_accuracy: 0.0235 - classifier_output_accuracy: 0.7708\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00004: val_loss improved from 0.16380 to 0.15227, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00004: val_loss improved from 0.16380 to 0.15227, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00004: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00004: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 3ms/step - loss: 0.1573 - ecoder_output_loss: 0.0851 - classifier_output_loss: 0.7219 - ecoder_output_accuracy: 0.0236 - classifier_output_accuracy: 0.7735 - val_loss: 0.1523 - val_ecoder_output_loss: 0.0838 - val_classifier_output_loss: 0.6843 - val_ecoder_output_accuracy: 0.0172 - val_classifier_output_accuracy: 0.7940\n",
      "Epoch 5/20\n",
      "210/235 [=========================>....] - ETA: 0s - loss: 0.1484 - ecoder_output_loss: 0.0842 - classifier_output_loss: 0.6412 - ecoder_output_accuracy: 0.0212 - classifier_output_accuracy: 0.8104\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00005: val_loss improved from 0.15227 to 0.14401, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00005: val_loss improved from 0.15227 to 0.14401, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00005: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00005: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 3ms/step - loss: 0.1477 - ecoder_output_loss: 0.0842 - classifier_output_loss: 0.6354 - ecoder_output_accuracy: 0.0214 - classifier_output_accuracy: 0.8120 - val_loss: 0.1440 - val_ecoder_output_loss: 0.0831 - val_classifier_output_loss: 0.6089 - val_ecoder_output_accuracy: 0.0140 - val_classifier_output_accuracy: 0.8279\n",
      "Epoch 6/20\n",
      "228/235 [============================>.] - ETA: 0s - loss: 0.1414 - ecoder_output_loss: 0.0836 - classifier_output_loss: 0.5776 - ecoder_output_accuracy: 0.0201 - classifier_output_accuracy: 0.8341\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00006: val_loss improved from 0.14401 to 0.13843, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00006: val_loss improved from 0.14401 to 0.13843, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00006: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00006: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 3ms/step - loss: 0.1413 - ecoder_output_loss: 0.0836 - classifier_output_loss: 0.5774 - ecoder_output_accuracy: 0.0205 - classifier_output_accuracy: 0.8342 - val_loss: 0.1384 - val_ecoder_output_loss: 0.0823 - val_classifier_output_loss: 0.5613 - val_ecoder_output_accuracy: 0.0189 - val_classifier_output_accuracy: 0.8425\n",
      "Epoch 7/20\n",
      "208/235 [=========================>....] - ETA: 0s - loss: 0.1365 - ecoder_output_loss: 0.0830 - classifier_output_loss: 0.5348 - ecoder_output_accuracy: 0.0187 - classifier_output_accuracy: 0.8490\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00007: val_loss improved from 0.13843 to 0.13564, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00007: val_loss improved from 0.13843 to 0.13564, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00007: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00007: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 2ms/step - loss: 0.1362 - ecoder_output_loss: 0.0830 - classifier_output_loss: 0.5318 - ecoder_output_accuracy: 0.0186 - classifier_output_accuracy: 0.8493 - val_loss: 0.1356 - val_ecoder_output_loss: 0.0821 - val_classifier_output_loss: 0.5358 - val_ecoder_output_accuracy: 0.0120 - val_classifier_output_accuracy: 0.8514\n",
      "Epoch 8/20\n",
      "233/235 [============================>.] - ETA: 0s - loss: 0.1321 - ecoder_output_loss: 0.0825 - classifier_output_loss: 0.4963 - ecoder_output_accuracy: 0.0196 - classifier_output_accuracy: 0.8595\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00008: val_loss improved from 0.13564 to 0.13082, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00008: val_loss improved from 0.13564 to 0.13082, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00008: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00008: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 3ms/step - loss: 0.1321 - ecoder_output_loss: 0.0825 - classifier_output_loss: 0.4960 - ecoder_output_accuracy: 0.0196 - classifier_output_accuracy: 0.8597 - val_loss: 0.1308 - val_ecoder_output_loss: 0.0816 - val_classifier_output_loss: 0.4925 - val_ecoder_output_accuracy: 0.0163 - val_classifier_output_accuracy: 0.8641\n",
      "Epoch 9/20\n",
      "223/235 [===========================>..] - ETA: 0s - loss: 0.1291 - ecoder_output_loss: 0.0822 - classifier_output_loss: 0.4694 - ecoder_output_accuracy: 0.0202 - classifier_output_accuracy: 0.8683\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00009: val_loss improved from 0.13082 to 0.12773, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00009: val_loss improved from 0.13082 to 0.12773, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00009: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00009: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 3ms/step - loss: 0.1289 - ecoder_output_loss: 0.0821 - classifier_output_loss: 0.4677 - ecoder_output_accuracy: 0.0202 - classifier_output_accuracy: 0.8686 - val_loss: 0.1277 - val_ecoder_output_loss: 0.0809 - val_classifier_output_loss: 0.4678 - val_ecoder_output_accuracy: 0.0144 - val_classifier_output_accuracy: 0.8677\n",
      "Epoch 10/20\n",
      "224/235 [===========================>..] - ETA: 0s - loss: 0.1256 - ecoder_output_loss: 0.0812 - classifier_output_loss: 0.4433 - ecoder_output_accuracy: 0.0190 - classifier_output_accuracy: 0.8765\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00010: val_loss improved from 0.12773 to 0.12428, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00010: val_loss improved from 0.12773 to 0.12428, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00010: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00010: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "Epoch 00010: saving model to model/AE_model/model_2\\KERAS_check_model_epoch10.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 3ms/step - loss: 0.1253 - ecoder_output_loss: 0.0812 - classifier_output_loss: 0.4414 - ecoder_output_accuracy: 0.0190 - classifier_output_accuracy: 0.8770 - val_loss: 0.1243 - val_ecoder_output_loss: 0.0800 - val_classifier_output_loss: 0.4426 - val_ecoder_output_accuracy: 0.0150 - val_classifier_output_accuracy: 0.8800\n",
      "Epoch 11/20\n",
      "203/235 [========================>.....] - ETA: 0s - loss: 0.1225 - ecoder_output_loss: 0.0804 - classifier_output_loss: 0.4211 - ecoder_output_accuracy: 0.0196 - classifier_output_accuracy: 0.8826\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00011: val_loss improved from 0.12428 to 0.12305, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00011: val_loss improved from 0.12428 to 0.12305, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00011: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00011: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 3ms/step - loss: 0.1224 - ecoder_output_loss: 0.0805 - classifier_output_loss: 0.4188 - ecoder_output_accuracy: 0.0198 - classifier_output_accuracy: 0.8830 - val_loss: 0.1231 - val_ecoder_output_loss: 0.0794 - val_classifier_output_loss: 0.4365 - val_ecoder_output_accuracy: 0.0170 - val_classifier_output_accuracy: 0.8819\n",
      "Epoch 12/20\n",
      "227/235 [===========================>..] - ETA: 0s - loss: 0.1202 - ecoder_output_loss: 0.0800 - classifier_output_loss: 0.4014 - ecoder_output_accuracy: 0.0205 - classifier_output_accuracy: 0.8884\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00012: val_loss improved from 0.12305 to 0.12023, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00012: val_loss improved from 0.12305 to 0.12023, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00012: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00012: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 2ms/step - loss: 0.1201 - ecoder_output_loss: 0.0800 - classifier_output_loss: 0.4005 - ecoder_output_accuracy: 0.0204 - classifier_output_accuracy: 0.8887 - val_loss: 0.1202 - val_ecoder_output_loss: 0.0792 - val_classifier_output_loss: 0.4102 - val_ecoder_output_accuracy: 0.0178 - val_classifier_output_accuracy: 0.8882\n",
      "Epoch 13/20\n",
      "227/235 [===========================>..] - ETA: 0s - loss: 0.1181 - ecoder_output_loss: 0.0797 - classifier_output_loss: 0.3843 - ecoder_output_accuracy: 0.0218 - classifier_output_accuracy: 0.8932\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00013: val_loss improved from 0.12023 to 0.11848, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00013: val_loss improved from 0.12023 to 0.11848, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00013: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00013: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 2ms/step - loss: 0.1180 - ecoder_output_loss: 0.0796 - classifier_output_loss: 0.3838 - ecoder_output_accuracy: 0.0219 - classifier_output_accuracy: 0.8935 - val_loss: 0.1185 - val_ecoder_output_loss: 0.0787 - val_classifier_output_loss: 0.3980 - val_ecoder_output_accuracy: 0.0180 - val_classifier_output_accuracy: 0.8928\n",
      "Epoch 14/20\n",
      "218/235 [==========================>...] - ETA: 0s - loss: 0.1163 - ecoder_output_loss: 0.0793 - classifier_output_loss: 0.3703 - ecoder_output_accuracy: 0.0235 - classifier_output_accuracy: 0.8978\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00014: val_loss improved from 0.11848 to 0.11636, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00014: val_loss improved from 0.11848 to 0.11636, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00014: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00014: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 3ms/step - loss: 0.1163 - ecoder_output_loss: 0.0792 - classifier_output_loss: 0.3701 - ecoder_output_accuracy: 0.0233 - classifier_output_accuracy: 0.8978 - val_loss: 0.1164 - val_ecoder_output_loss: 0.0783 - val_classifier_output_loss: 0.3802 - val_ecoder_output_accuracy: 0.0216 - val_classifier_output_accuracy: 0.8974\n",
      "Epoch 15/20\n",
      "218/235 [==========================>...] - ETA: 0s - loss: 0.1143 - ecoder_output_loss: 0.0788 - classifier_output_loss: 0.3550 - ecoder_output_accuracy: 0.0237 - classifier_output_accuracy: 0.9024\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00015: val_loss improved from 0.11636 to 0.11538, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00015: val_loss improved from 0.11636 to 0.11538, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00015: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00015: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 3ms/step - loss: 0.1145 - ecoder_output_loss: 0.0787 - classifier_output_loss: 0.3573 - ecoder_output_accuracy: 0.0238 - classifier_output_accuracy: 0.9017 - val_loss: 0.1154 - val_ecoder_output_loss: 0.0778 - val_classifier_output_loss: 0.3757 - val_ecoder_output_accuracy: 0.0255 - val_classifier_output_accuracy: 0.8988\n",
      "Epoch 16/20\n",
      "210/235 [=========================>....] - ETA: 0s - loss: 0.1128 - ecoder_output_loss: 0.0782 - classifier_output_loss: 0.3455 - ecoder_output_accuracy: 0.0252 - classifier_output_accuracy: 0.9060\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00016: val_loss improved from 0.11538 to 0.11394, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00016: val_loss improved from 0.11538 to 0.11394, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00016: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00016: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 3ms/step - loss: 0.1128 - ecoder_output_loss: 0.0782 - classifier_output_loss: 0.3453 - ecoder_output_accuracy: 0.0251 - classifier_output_accuracy: 0.9058 - val_loss: 0.1139 - val_ecoder_output_loss: 0.0773 - val_classifier_output_loss: 0.3659 - val_ecoder_output_accuracy: 0.0261 - val_classifier_output_accuracy: 0.9015\n",
      "Epoch 17/20\n",
      "206/235 [=========================>....] - ETA: 0s - loss: 0.1115 - ecoder_output_loss: 0.0777 - classifier_output_loss: 0.3375 - ecoder_output_accuracy: 0.0259 - classifier_output_accuracy: 0.9086\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00017: val_loss improved from 0.11394 to 0.11335, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00017: val_loss improved from 0.11394 to 0.11335, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00017: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00017: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 2ms/step - loss: 0.1116 - ecoder_output_loss: 0.0778 - classifier_output_loss: 0.3371 - ecoder_output_accuracy: 0.0256 - classifier_output_accuracy: 0.9084 - val_loss: 0.1133 - val_ecoder_output_loss: 0.0770 - val_classifier_output_loss: 0.3630 - val_ecoder_output_accuracy: 0.0209 - val_classifier_output_accuracy: 0.9021\n",
      "Epoch 18/20\n",
      "203/235 [========================>.....] - ETA: 0s - loss: 0.1101 - ecoder_output_loss: 0.0774 - classifier_output_loss: 0.3270 - ecoder_output_accuracy: 0.0250 - classifier_output_accuracy: 0.9112\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00018: val_loss improved from 0.11335 to 0.11204, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00018: val_loss improved from 0.11335 to 0.11204, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00018: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00018: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 2ms/step - loss: 0.1104 - ecoder_output_loss: 0.0774 - classifier_output_loss: 0.3292 - ecoder_output_accuracy: 0.0255 - classifier_output_accuracy: 0.9107 - val_loss: 0.1120 - val_ecoder_output_loss: 0.0768 - val_classifier_output_loss: 0.3528 - val_ecoder_output_accuracy: 0.0249 - val_classifier_output_accuracy: 0.9067\n",
      "Epoch 19/20\n",
      "211/235 [=========================>....] - ETA: 0s - loss: 0.1094 - ecoder_output_loss: 0.0772 - classifier_output_loss: 0.3222 - ecoder_output_accuracy: 0.0252 - classifier_output_accuracy: 0.9130\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00019: val_loss improved from 0.11204 to 0.11122, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00019: val_loss improved from 0.11204 to 0.11122, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00019: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00019: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 3ms/step - loss: 0.1094 - ecoder_output_loss: 0.0772 - classifier_output_loss: 0.3225 - ecoder_output_accuracy: 0.0253 - classifier_output_accuracy: 0.9126 - val_loss: 0.1112 - val_ecoder_output_loss: 0.0763 - val_classifier_output_loss: 0.3494 - val_ecoder_output_accuracy: 0.0294 - val_classifier_output_accuracy: 0.9082\n",
      "Epoch 20/20\n",
      "231/235 [============================>.] - ETA: 0s - loss: 0.1085 - ecoder_output_loss: 0.0769 - classifier_output_loss: 0.3161 - ecoder_output_accuracy: 0.0261 - classifier_output_accuracy: 0.9151\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00020: val_loss improved from 0.11122 to 0.11047, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00020: val_loss improved from 0.11122 to 0.11047, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00020: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00020: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "Epoch 00020: saving model to model/AE_model/model_2\\KERAS_check_model_epoch20.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 3ms/step - loss: 0.1085 - ecoder_output_loss: 0.0769 - classifier_output_loss: 0.3160 - ecoder_output_accuracy: 0.0260 - classifier_output_accuracy: 0.9151 - val_loss: 0.1105 - val_ecoder_output_loss: 0.0762 - val_classifier_output_loss: 0.3430 - val_ecoder_output_accuracy: 0.0212 - val_classifier_output_accuracy: 0.9080\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "network_with_classifier = Autoencoder(data_zoom, pruned=False)\n",
    "network_with_classifier.build_encoder()\n",
    "network_with_classifier.build_decoder()\n",
    "network_with_classifier.build_classifier()\n",
    "network_with_classifier.build_autoencoder(use_latent_classifier=True)\n",
    "network_with_classifier.fit_data( batch_size=256,epochs=20, use_latent_classifier=True)\n",
    "network_with_classifier.plot_latent_dimension(\"Autoencoder with classifier\")\n",
    "network_with_classifier.plot_score(use_latent_classifier=True,model_name=\"Autoencoder with classifier\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"functional_13\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "encoder_input (InputLayer)      [(None, 100)]        0                                            \n",
      "__________________________________________________________________________________________________\n",
      "dense_68 (Dense)                (None, 32)           3232        encoder_input[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "prune_low_magnitude_dense_69 (P (None, 16)           1042        dense_68[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "encoder_output (Dense)          (None, 2)            34          prune_low_magnitude_dense_69[0][0\n",
      "__________________________________________________________________________________________________\n",
      "prune_low_magnitude_dense_70 (P (None, 16)           82          encoder_output[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "prune_low_magnitude_dense_72 (P (None, 16)           82          encoder_output[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "dense_71 (Dense)                (None, 32)           544         prune_low_magnitude_dense_70[0][0\n",
      "__________________________________________________________________________________________________\n",
      "dense_73 (Dense)                (None, 32)           544         prune_low_magnitude_dense_72[0][0\n",
      "__________________________________________________________________________________________________\n",
      "ecoder_output (Dense)           (None, 100)          3300        dense_71[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "classifier_output (Dense)       (None, 10)           330         dense_73[0][0]                   \n",
      "==================================================================================================\n",
      "Total params: 9,190\n",
      "Trainable params: 8,608\n",
      "Non-trainable params: 582\n",
      "__________________________________________________________________________________________________\n",
      "WARNING:tensorflow:`epsilon` argument is deprecated and will be removed, use `min_delta` instead.\n",
      "WARNING:tensorflow:`period` argument is deprecated. Please use `save_freq` to specify the frequency in number of batches seen.\n",
      "Epoch 1/20\n",
      "WARNING:tensorflow:AutoGraph could not transform <function Pruning.conditional_mask_update.<locals>.maybe_update_masks at 0x000002C6CD582A60> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: module 'gast' has no attribute 'Index'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function Pruning.conditional_mask_update.<locals>.maybe_update_masks at 0x000002C6CD582A60> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: module 'gast' has no attribute 'Index'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING:tensorflow:AutoGraph could not transform <function Pruning.conditional_mask_update.<locals>.maybe_update_masks at 0x000002C6D0493DC0> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: module 'gast' has no attribute 'Index'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function Pruning.conditional_mask_update.<locals>.maybe_update_masks at 0x000002C6D0493DC0> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: module 'gast' has no attribute 'Index'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING:tensorflow:AutoGraph could not transform <function Pruning.conditional_mask_update.<locals>.maybe_update_masks at 0x000002C6D14A81F0> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: module 'gast' has no attribute 'Index'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function Pruning.conditional_mask_update.<locals>.maybe_update_masks at 0x000002C6D14A81F0> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: module 'gast' has no attribute 'Index'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING:tensorflow:AutoGraph could not transform <function Pruning.conditional_mask_update.<locals>.maybe_update_masks at 0x000002C6CD400B80> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: module 'gast' has no attribute 'Index'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function Pruning.conditional_mask_update.<locals>.maybe_update_masks at 0x000002C6CD400B80> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: module 'gast' has no attribute 'Index'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING:tensorflow:AutoGraph could not transform <function Pruning.conditional_mask_update.<locals>.maybe_update_masks at 0x000002C6CC4D1820> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: module 'gast' has no attribute 'Index'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function Pruning.conditional_mask_update.<locals>.maybe_update_masks at 0x000002C6CC4D1820> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: module 'gast' has no attribute 'Index'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING:tensorflow:AutoGraph could not transform <function Pruning.conditional_mask_update.<locals>.maybe_update_masks at 0x000002C6CD4000D0> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: module 'gast' has no attribute 'Index'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function Pruning.conditional_mask_update.<locals>.maybe_update_masks at 0x000002C6CD4000D0> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: module 'gast' has no attribute 'Index'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "  2/235 [..............................] - ETA: 2:44 - loss: 0.4378 - ecoder_output_loss: 0.2072 - classifier_output_loss: 2.3063 - ecoder_output_accuracy: 0.0059 - classifier_output_accuracy: 0.0957  WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0000s vs `on_train_batch_end` time: 1.4111s). Check your callbacks.\n",
      "228/235 [============================>.] - ETA: 0s - loss: 0.2995 - ecoder_output_loss: 0.1225 - classifier_output_loss: 1.7702 - ecoder_output_accuracy: 0.0108 - classifier_output_accuracy: 0.3263\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 0.23350, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 0.23350, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00001: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00001: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 2s 10ms/step - loss: 0.2978 - ecoder_output_loss: 0.1220 - classifier_output_loss: 1.7582 - ecoder_output_accuracy: 0.0119 - classifier_output_accuracy: 0.3305 - val_loss: 0.2335 - val_ecoder_output_loss: 0.1027 - val_classifier_output_loss: 1.3076 - val_ecoder_output_accuracy: 0.0060 - val_classifier_output_accuracy: 0.5076\n",
      "Epoch 2/20\n",
      "223/235 [===========================>..] - ETA: 0s - loss: 0.2150 - ecoder_output_loss: 0.0982 - classifier_output_loss: 1.1681 - ecoder_output_accuracy: 0.0502 - classifier_output_accuracy: 0.5657\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00002: val_loss improved from 0.23350 to 0.19652, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00002: val_loss improved from 0.23350 to 0.19652, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00002: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00002: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 3ms/step - loss: 0.2142 - ecoder_output_loss: 0.0980 - classifier_output_loss: 1.1627 - ecoder_output_accuracy: 0.0506 - classifier_output_accuracy: 0.5693 - val_loss: 0.1965 - val_ecoder_output_loss: 0.0925 - val_classifier_output_loss: 1.0403 - val_ecoder_output_accuracy: 0.0462 - val_classifier_output_accuracy: 0.6165\n",
      "Epoch 3/20\n",
      "231/235 [============================>.] - ETA: 0s - loss: 0.1864 - ecoder_output_loss: 0.0896 - classifier_output_loss: 0.9685 - ecoder_output_accuracy: 0.0590 - classifier_output_accuracy: 0.6513\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00003: val_loss improved from 0.19652 to 0.17763, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00003: val_loss improved from 0.19652 to 0.17763, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00003: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00003: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 2ms/step - loss: 0.1863 - ecoder_output_loss: 0.0896 - classifier_output_loss: 0.9676 - ecoder_output_accuracy: 0.0592 - classifier_output_accuracy: 0.6518 - val_loss: 0.1776 - val_ecoder_output_loss: 0.0865 - val_classifier_output_loss: 0.9117 - val_ecoder_output_accuracy: 0.0682 - val_classifier_output_accuracy: 0.6640\n",
      "Epoch 4/20\n",
      "231/235 [============================>.] - ETA: 0s - loss: 0.1720 - ecoder_output_loss: 0.0853 - classifier_output_loss: 0.8667 - ecoder_output_accuracy: 0.0549 - classifier_output_accuracy: 0.6937\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00004: val_loss improved from 0.17763 to 0.16781, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00004: val_loss improved from 0.17763 to 0.16781, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00004: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00004: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 2ms/step - loss: 0.1719 - ecoder_output_loss: 0.0853 - classifier_output_loss: 0.8659 - ecoder_output_accuracy: 0.0550 - classifier_output_accuracy: 0.6939 - val_loss: 0.1678 - val_ecoder_output_loss: 0.0831 - val_classifier_output_loss: 0.8466 - val_ecoder_output_accuracy: 0.0573 - val_classifier_output_accuracy: 0.6945\n",
      "Epoch 5/20\n",
      "217/235 [==========================>...] - ETA: 0s - loss: 0.1618 - ecoder_output_loss: 0.0819 - classifier_output_loss: 0.7990 - ecoder_output_accuracy: 0.0577 - classifier_output_accuracy: 0.7202\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00005: val_loss improved from 0.16781 to 0.16271, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00005: val_loss improved from 0.16781 to 0.16271, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00005: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00005: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 3ms/step - loss: 0.1617 - ecoder_output_loss: 0.0818 - classifier_output_loss: 0.7982 - ecoder_output_accuracy: 0.0579 - classifier_output_accuracy: 0.7205 - val_loss: 0.1627 - val_ecoder_output_loss: 0.0803 - val_classifier_output_loss: 0.8236 - val_ecoder_output_accuracy: 0.0504 - val_classifier_output_accuracy: 0.6868\n",
      "Epoch 6/20\n",
      "226/235 [===========================>..] - ETA: 0s - loss: 0.1548 - ecoder_output_loss: 0.0801 - classifier_output_loss: 0.7467 - ecoder_output_accuracy: 0.0539 - classifier_output_accuracy: 0.7394\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00006: val_loss improved from 0.16271 to 0.15228, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00006: val_loss improved from 0.16271 to 0.15228, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00006: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00006: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 3ms/step - loss: 0.1547 - ecoder_output_loss: 0.0801 - classifier_output_loss: 0.7460 - ecoder_output_accuracy: 0.0536 - classifier_output_accuracy: 0.7400 - val_loss: 0.1523 - val_ecoder_output_loss: 0.0790 - val_classifier_output_loss: 0.7327 - val_ecoder_output_accuracy: 0.0518 - val_classifier_output_accuracy: 0.7592\n",
      "Epoch 7/20\n",
      "213/235 [==========================>...] - ETA: 0s - loss: 0.1497 - ecoder_output_loss: 0.0792 - classifier_output_loss: 0.7045 - ecoder_output_accuracy: 0.0463 - classifier_output_accuracy: 0.7583\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00007: val_loss improved from 0.15228 to 0.14979, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00007: val_loss improved from 0.15228 to 0.14979, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00007: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00007: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 3ms/step - loss: 0.1496 - ecoder_output_loss: 0.0793 - classifier_output_loss: 0.7030 - ecoder_output_accuracy: 0.0462 - classifier_output_accuracy: 0.7590 - val_loss: 0.1498 - val_ecoder_output_loss: 0.0785 - val_classifier_output_loss: 0.7126 - val_ecoder_output_accuracy: 0.0359 - val_classifier_output_accuracy: 0.7654\n",
      "Epoch 8/20\n",
      "212/235 [==========================>...] - ETA: 0s - loss: 0.1456 - ecoder_output_loss: 0.0787 - classifier_output_loss: 0.6687 - ecoder_output_accuracy: 0.0430 - classifier_output_accuracy: 0.7745\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00008: val_loss improved from 0.14979 to 0.14401, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00008: val_loss improved from 0.14979 to 0.14401, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00008: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00008: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 3ms/step - loss: 0.1455 - ecoder_output_loss: 0.0788 - classifier_output_loss: 0.6675 - ecoder_output_accuracy: 0.0435 - classifier_output_accuracy: 0.7753 - val_loss: 0.1440 - val_ecoder_output_loss: 0.0780 - val_classifier_output_loss: 0.6600 - val_ecoder_output_accuracy: 0.0376 - val_classifier_output_accuracy: 0.7857\n",
      "Epoch 9/20\n",
      "234/235 [============================>.] - ETA: 0s - loss: 0.1415 - ecoder_output_loss: 0.0783 - classifier_output_loss: 0.6318 - ecoder_output_accuracy: 0.0411 - classifier_output_accuracy: 0.7937\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00009: val_loss improved from 0.14401 to 0.14162, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00009: val_loss improved from 0.14401 to 0.14162, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00009: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00009: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 2ms/step - loss: 0.1415 - ecoder_output_loss: 0.0783 - classifier_output_loss: 0.6319 - ecoder_output_accuracy: 0.0411 - classifier_output_accuracy: 0.7936 - val_loss: 0.1416 - val_ecoder_output_loss: 0.0777 - val_classifier_output_loss: 0.6397 - val_ecoder_output_accuracy: 0.0423 - val_classifier_output_accuracy: 0.8041\n",
      "Epoch 10/20\n",
      "204/235 [=========================>....] - ETA: 0s - loss: 0.1384 - ecoder_output_loss: 0.0779 - classifier_output_loss: 0.6047 - ecoder_output_accuracy: 0.0425 - classifier_output_accuracy: 0.8068\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00010: val_loss improved from 0.14162 to 0.13840, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00010: val_loss improved from 0.14162 to 0.13840, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00010: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00010: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "Epoch 00010: saving model to model/AE_model/model_2\\KERAS_check_model_epoch10.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 3ms/step - loss: 0.1382 - ecoder_output_loss: 0.0780 - classifier_output_loss: 0.6021 - ecoder_output_accuracy: 0.0415 - classifier_output_accuracy: 0.8088 - val_loss: 0.1384 - val_ecoder_output_loss: 0.0773 - val_classifier_output_loss: 0.6108 - val_ecoder_output_accuracy: 0.0332 - val_classifier_output_accuracy: 0.8152\n",
      "Epoch 11/20\n",
      "234/235 [============================>.] - ETA: 0s - loss: 0.1350 - ecoder_output_loss: 0.0777 - classifier_output_loss: 0.5726 - ecoder_output_accuracy: 0.0412 - classifier_output_accuracy: 0.8227\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00011: val_loss improved from 0.13840 to 0.13517, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00011: val_loss improved from 0.13840 to 0.13517, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00011: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00011: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 3ms/step - loss: 0.1350 - ecoder_output_loss: 0.0777 - classifier_output_loss: 0.5726 - ecoder_output_accuracy: 0.0412 - classifier_output_accuracy: 0.8227 - val_loss: 0.1352 - val_ecoder_output_loss: 0.0770 - val_classifier_output_loss: 0.5813 - val_ecoder_output_accuracy: 0.0338 - val_classifier_output_accuracy: 0.8299\n",
      "Epoch 12/20\n",
      "233/235 [============================>.] - ETA: 0s - loss: 0.1321 - ecoder_output_loss: 0.0775 - classifier_output_loss: 0.5466 - ecoder_output_accuracy: 0.0397 - classifier_output_accuracy: 0.8370\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00012: val_loss improved from 0.13517 to 0.13371, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00012: val_loss improved from 0.13517 to 0.13371, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00012: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00012: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 3ms/step - loss: 0.1321 - ecoder_output_loss: 0.0775 - classifier_output_loss: 0.5463 - ecoder_output_accuracy: 0.0397 - classifier_output_accuracy: 0.8372 - val_loss: 0.1337 - val_ecoder_output_loss: 0.0769 - val_classifier_output_loss: 0.5685 - val_ecoder_output_accuracy: 0.0278 - val_classifier_output_accuracy: 0.8348\n",
      "Epoch 13/20\n",
      "213/235 [==========================>...] - ETA: 0s - loss: 0.1292 - ecoder_output_loss: 0.0773 - classifier_output_loss: 0.5194 - ecoder_output_accuracy: 0.0404 - classifier_output_accuracy: 0.8478\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00013: val_loss improved from 0.13371 to 0.12968, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00013: val_loss improved from 0.13371 to 0.12968, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00013: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00013: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 3ms/step - loss: 0.1293 - ecoder_output_loss: 0.0773 - classifier_output_loss: 0.5203 - ecoder_output_accuracy: 0.0406 - classifier_output_accuracy: 0.8473 - val_loss: 0.1297 - val_ecoder_output_loss: 0.0767 - val_classifier_output_loss: 0.5296 - val_ecoder_output_accuracy: 0.0380 - val_classifier_output_accuracy: 0.8501\n",
      "Epoch 14/20\n",
      "228/235 [============================>.] - ETA: 0s - loss: 0.1269 - ecoder_output_loss: 0.0771 - classifier_output_loss: 0.4978 - ecoder_output_accuracy: 0.0394 - classifier_output_accuracy: 0.8559\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00014: val_loss improved from 0.12968 to 0.12801, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00014: val_loss improved from 0.12968 to 0.12801, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00014: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00014: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 4ms/step - loss: 0.1269 - ecoder_output_loss: 0.0771 - classifier_output_loss: 0.4978 - ecoder_output_accuracy: 0.0394 - classifier_output_accuracy: 0.8559 - val_loss: 0.1280 - val_ecoder_output_loss: 0.0765 - val_classifier_output_loss: 0.5155 - val_ecoder_output_accuracy: 0.0337 - val_classifier_output_accuracy: 0.8513\n",
      "Epoch 15/20\n",
      "219/235 [==========================>...] - ETA: 0s - loss: 0.1248 - ecoder_output_loss: 0.0769 - classifier_output_loss: 0.4784 - ecoder_output_accuracy: 0.0383 - classifier_output_accuracy: 0.8634\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00015: val_loss improved from 0.12801 to 0.12563, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00015: val_loss improved from 0.12801 to 0.12563, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00015: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00015: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 3ms/step - loss: 0.1245 - ecoder_output_loss: 0.0769 - classifier_output_loss: 0.4757 - ecoder_output_accuracy: 0.0386 - classifier_output_accuracy: 0.8645 - val_loss: 0.1256 - val_ecoder_output_loss: 0.0763 - val_classifier_output_loss: 0.4930 - val_ecoder_output_accuracy: 0.0337 - val_classifier_output_accuracy: 0.8611\n",
      "Epoch 16/20\n",
      "229/235 [============================>.] - ETA: 0s - loss: 0.1226 - ecoder_output_loss: 0.0768 - classifier_output_loss: 0.4585 - ecoder_output_accuracy: 0.0368 - classifier_output_accuracy: 0.8717\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00016: val_loss improved from 0.12563 to 0.12293, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00016: val_loss improved from 0.12563 to 0.12293, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00016: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00016: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 4ms/step - loss: 0.1226 - ecoder_output_loss: 0.0768 - classifier_output_loss: 0.4581 - ecoder_output_accuracy: 0.0368 - classifier_output_accuracy: 0.8720 - val_loss: 0.1229 - val_ecoder_output_loss: 0.0760 - val_classifier_output_loss: 0.4690 - val_ecoder_output_accuracy: 0.0339 - val_classifier_output_accuracy: 0.8714\n",
      "Epoch 17/20\n",
      "228/235 [============================>.] - ETA: 0s - loss: 0.1205 - ecoder_output_loss: 0.0766 - classifier_output_loss: 0.4392 - ecoder_output_accuracy: 0.0370 - classifier_output_accuracy: 0.8774\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00017: val_loss improved from 0.12293 to 0.12167, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00017: val_loss improved from 0.12293 to 0.12167, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00017: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00017: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 3ms/step - loss: 0.1206 - ecoder_output_loss: 0.0766 - classifier_output_loss: 0.4402 - ecoder_output_accuracy: 0.0371 - classifier_output_accuracy: 0.8772 - val_loss: 0.1217 - val_ecoder_output_loss: 0.0760 - val_classifier_output_loss: 0.4566 - val_ecoder_output_accuracy: 0.0350 - val_classifier_output_accuracy: 0.8755\n",
      "Epoch 18/20\n",
      "221/235 [===========================>..] - ETA: 0s - loss: 0.1193 - ecoder_output_loss: 0.0765 - classifier_output_loss: 0.4280 - ecoder_output_accuracy: 0.0346 - classifier_output_accuracy: 0.8818\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00018: val_loss improved from 0.12167 to 0.11989, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00018: val_loss improved from 0.12167 to 0.11989, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00018: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00018: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 4ms/step - loss: 0.1191 - ecoder_output_loss: 0.0764 - classifier_output_loss: 0.4264 - ecoder_output_accuracy: 0.0352 - classifier_output_accuracy: 0.8826 - val_loss: 0.1199 - val_ecoder_output_loss: 0.0758 - val_classifier_output_loss: 0.4413 - val_ecoder_output_accuracy: 0.0360 - val_classifier_output_accuracy: 0.8793\n",
      "Epoch 19/20\n",
      "226/235 [===========================>..] - ETA: 0s - loss: 0.1177 - ecoder_output_loss: 0.0763 - classifier_output_loss: 0.4139 - ecoder_output_accuracy: 0.0341 - classifier_output_accuracy: 0.8860\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00019: val_loss improved from 0.11989 to 0.11919, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00019: val_loss improved from 0.11989 to 0.11919, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00019: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00019: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 4ms/step - loss: 0.1176 - ecoder_output_loss: 0.0763 - classifier_output_loss: 0.4130 - ecoder_output_accuracy: 0.0343 - classifier_output_accuracy: 0.8863 - val_loss: 0.1192 - val_ecoder_output_loss: 0.0757 - val_classifier_output_loss: 0.4353 - val_ecoder_output_accuracy: 0.0315 - val_classifier_output_accuracy: 0.8820\n",
      "Epoch 20/20\n",
      "232/235 [============================>.] - ETA: 0s - loss: 0.1162 - ecoder_output_loss: 0.0761 - classifier_output_loss: 0.4014 - ecoder_output_accuracy: 0.0342 - classifier_output_accuracy: 0.8899\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00020: val_loss did not improve from 0.11919\n",
      "\n",
      "Epoch 00020: val_loss did not improve from 0.11919\n",
      "\n",
      "Epoch 00020: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00020: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "Epoch 00020: saving model to model/AE_model/model_2\\KERAS_check_model_epoch20.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 4ms/step - loss: 0.1162 - ecoder_output_loss: 0.0761 - classifier_output_loss: 0.4012 - ecoder_output_accuracy: 0.0341 - classifier_output_accuracy: 0.8899 - val_loss: 0.1206 - val_ecoder_output_loss: 0.0758 - val_classifier_output_loss: 0.4475 - val_ecoder_output_accuracy: 0.0287 - val_classifier_output_accuracy: 0.8772\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "network_with_classifier = Autoencoder(data_zoom, pruned=True)\n",
    "network_with_classifier.build_encoder()\n",
    "network_with_classifier.build_decoder()\n",
    "network_with_classifier.build_classifier()\n",
    "network_with_classifier.build_autoencoder(use_latent_classifier=True)\n",
    "network_with_classifier.fit_data( batch_size=256,epochs=20, use_latent_classifier=True)\n",
    "network_with_classifier.plot_latent_dimension(\"Autoencoder with classifier\")\n",
    "network_with_classifier.plot_score(use_latent_classifier=True,model_name=\"Autoencoder with classifier\")"
   ]
  },
  {
   "cell_type": "code",
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