AE.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "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 modifiable resolution is shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 64)\n"
     ]
    }
   ],
   "source": [
    "import MNIST_dataset as mnist\n",
    "size_final = 8\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": 3,
   "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",
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     "locked": true,
     "schema_version": 3,
     "solution": false,
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    }
   },
   "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": 9,
   "metadata": {
    "deletable": false,
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    "nbgrader": {
     "cell_type": "code",
     "checksum": "8f093c3123887a615df510d5cb9d3bee",
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     "grade_id": "cell-b3452809a3ed4ee3",
     "locked": true,
     "schema_version": 3,
     "solution": false,
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    },
    "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",
    "\n",
    "        if self.pruned:\n",
    "            self.decoder = prune_low_magnitude(Dense(16,  activation='relu'), **self.pruning_params)(self.encoder)\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",
    "        \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=20, 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": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"autoencoder\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "encoder_input (InputLayer)   [(None, 64)]              0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 32)                2080      \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 16)                528       \n",
      "_________________________________________________________________\n",
      "encoder_output (Dense)       (None, 2)                 34        \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 16)                48        \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 32)                544       \n",
      "_________________________________________________________________\n",
      "ecoder_output (Dense)        (None, 64)                2112      \n",
      "=================================================================\n",
      "Total params: 5,346\n",
      "Trainable params: 5,346\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",
      "  1/235 [..............................] - ETA: 0s - loss: 0.2071WARNING:tensorflow:From C:\\Users\\loren\\miniconda3\\envs\\MLinPhysics\\lib\\site-packages\\tensorflow\\python\\ops\\summary_ops_v2.py:1277: stop (from tensorflow.python.eager.profiler) is deprecated and will be removed after 2020-07-01.\n",
      "Instructions for updating:\n",
      "use `tf.profiler.experimental.stop` instead.\n",
      "  2/235 [..............................] - ETA: 7s - loss: 0.2082WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0154s vs `on_train_batch_end` time: 0.0448s). Check your callbacks.\n",
      "227/235 [===========================>..] - ETA: 0s - loss: 0.1162\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 0.08790, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 0.08790, 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 [==============================] - 1s 5ms/step - loss: 0.1154 - val_loss: 0.0879\n",
      "Epoch 2/20\n",
      "230/235 [============================>.] - ETA: 0s - loss: 0.0834\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00002: val_loss improved from 0.08790 to 0.07969, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00002: val_loss improved from 0.08790 to 0.07969, 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: 0.0833 - val_loss: 0.0797\n",
      "Epoch 3/20\n",
      "219/235 [==========================>...] - ETA: 0s - loss: 0.0781\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00003: val_loss improved from 0.07969 to 0.07639, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00003: val_loss improved from 0.07969 to 0.07639, 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: 0.0780 - val_loss: 0.0764\n",
      "Epoch 4/20\n",
      "213/235 [==========================>...] - ETA: 0s - loss: 0.0759\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00004: val_loss improved from 0.07639 to 0.07473, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00004: val_loss improved from 0.07639 to 0.07473, 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: 0.0758 - val_loss: 0.0747\n",
      "Epoch 5/20\n",
      "223/235 [===========================>..] - ETA: 0s - loss: 0.0743\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00005: val_loss improved from 0.07473 to 0.07342, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00005: val_loss improved from 0.07473 to 0.07342, 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: 0.0743 - val_loss: 0.0734\n",
      "Epoch 6/20\n",
      "227/235 [===========================>..] - ETA: 0s - loss: 0.0732\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00006: val_loss improved from 0.07342 to 0.07233, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00006: val_loss improved from 0.07342 to 0.07233, 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: 0.0732 - val_loss: 0.0723\n",
      "Epoch 7/20\n",
      "217/235 [==========================>...] - ETA: 0s - loss: 0.0723\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00007: val_loss improved from 0.07233 to 0.07142, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00007: val_loss improved from 0.07233 to 0.07142, 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: 0.0722 - val_loss: 0.0714\n",
      "Epoch 8/20\n",
      "224/235 [===========================>..] - ETA: 0s - loss: 0.0714\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00008: val_loss improved from 0.07142 to 0.07080, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00008: val_loss improved from 0.07142 to 0.07080, 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: 0.0714 - val_loss: 0.0708\n",
      "Epoch 9/20\n",
      "228/235 [============================>.] - ETA: 0s - loss: 0.0707\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00009: val_loss improved from 0.07080 to 0.07013, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00009: val_loss improved from 0.07080 to 0.07013, 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: 0.0707 - val_loss: 0.0701\n",
      "Epoch 10/20\n",
      "225/235 [===========================>..] - ETA: 0s - loss: 0.0700\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00010: val_loss improved from 0.07013 to 0.06949, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00010: val_loss improved from 0.07013 to 0.06949, 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: 0.0700 - val_loss: 0.0695\n",
      "Epoch 11/20\n",
      "217/235 [==========================>...] - ETA: 0s - loss: 0.0694\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00011: val_loss improved from 0.06949 to 0.06887, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00011: val_loss improved from 0.06949 to 0.06887, 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 4ms/step - loss: 0.0694 - val_loss: 0.0689\n",
      "Epoch 12/20\n",
      "227/235 [===========================>..] - ETA: 0s - loss: 0.0688\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00012: val_loss improved from 0.06887 to 0.06848, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00012: val_loss improved from 0.06887 to 0.06848, 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 4ms/step - loss: 0.0688 - val_loss: 0.0685\n",
      "Epoch 13/20\n",
      "219/235 [==========================>...] - ETA: 0s - loss: 0.0683\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00013: val_loss improved from 0.06848 to 0.06793, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00013: val_loss improved from 0.06848 to 0.06793, 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 4ms/step - loss: 0.0683 - val_loss: 0.0679\n",
      "Epoch 14/20\n",
      "232/235 [============================>.] - ETA: 0s - loss: 0.0678\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00014: val_loss improved from 0.06793 to 0.06744, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00014: val_loss improved from 0.06793 to 0.06744, 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: 0.0678 - val_loss: 0.0674\n",
      "Epoch 15/20\n",
      "231/235 [============================>.] - ETA: 0s - loss: 0.0674\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00015: val_loss improved from 0.06744 to 0.06717, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00015: val_loss improved from 0.06744 to 0.06717, 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: 0.0673 - val_loss: 0.0672\n",
      "Epoch 16/20\n",
      "225/235 [===========================>..] - ETA: 0s - loss: 0.0670\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00016: val_loss improved from 0.06717 to 0.06676, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00016: val_loss improved from 0.06717 to 0.06676, 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: 0.0670 - val_loss: 0.0668\n",
      "Epoch 17/20\n",
      "232/235 [============================>.] - ETA: 0s - loss: 0.0666\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00017: val_loss improved from 0.06676 to 0.06644, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00017: val_loss improved from 0.06676 to 0.06644, 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 4ms/step - loss: 0.0666 - val_loss: 0.0664\n",
      "Epoch 18/20\n",
      "219/235 [==========================>...] - ETA: 0s - loss: 0.0663\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00018: val_loss improved from 0.06644 to 0.06621, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00018: val_loss improved from 0.06644 to 0.06621, 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: 0.0663 - val_loss: 0.0662\n",
      "Epoch 19/20\n",
      "216/235 [==========================>...] - ETA: 0s - loss: 0.0661\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00019: val_loss improved from 0.06621 to 0.06597, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00019: val_loss improved from 0.06621 to 0.06597, 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: 0.0661 - val_loss: 0.0660\n",
      "Epoch 20/20\n",
      "231/235 [============================>.] - ETA: 0s - loss: 0.0658\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00020: val_loss improved from 0.06597 to 0.06565, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00020: val_loss improved from 0.06597 to 0.06565, 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: 0.0658 - val_loss: 0.0657\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": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"autoencoder\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "encoder_input (InputLayer)   [(None, 64)]              0         \n",
      "_________________________________________________________________\n",
      "dense_20 (Dense)             (None, 32)                2080      \n",
      "_________________________________________________________________\n",
      "prune_low_magnitude_dense_21 (None, 16)                1042      \n",
      "_________________________________________________________________\n",
      "encoder_output (Dense)       (None, 2)                 34        \n",
      "_________________________________________________________________\n",
      "prune_low_magnitude_dense_22 (None, 16)                82        \n",
      "_________________________________________________________________\n",
      "dense_23 (Dense)             (None, 32)                544       \n",
      "_________________________________________________________________\n",
      "ecoder_output (Dense)        (None, 64)                2112      \n",
      "=================================================================\n",
      "Total params: 5,894\n",
      "Trainable params: 5,346\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 0x00000159474EE3A0> 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 0x00000159474EE3A0> 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 0x00000159476898B0> 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 0x00000159476898B0> 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 0x0000015947B13790> 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 0x0000015947B13790> 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 0x000001594A735550> 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 0x000001594A735550> 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: 1:53 - loss: 0.2089WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0060s vs `on_train_batch_end` time: 0.9724s). Check your callbacks.\n",
      "233/235 [============================>.] - ETA: 0s - loss: 0.1171\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 0.09450, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 0.09450, 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 7ms/step - loss: 0.1170 - val_loss: 0.0945\n",
      "Epoch 2/20\n",
      "196/235 [========================>.....] - ETA: 0s - loss: 0.0892\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00002: val_loss improved from 0.09450 to 0.08510, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00002: val_loss improved from 0.09450 to 0.08510, 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 [==============================] - 0s 2ms/step - loss: 0.0886 - val_loss: 0.0851\n",
      "Epoch 3/20\n",
      "207/235 [=========================>....] - ETA: 0s - loss: 0.0834\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00003: val_loss improved from 0.08510 to 0.08100, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00003: val_loss improved from 0.08510 to 0.08100, 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: 0.0831 - val_loss: 0.0810\n",
      "Epoch 4/20\n",
      "194/235 [=======================>......] - ETA: 0s - loss: 0.0803\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00004: val_loss improved from 0.08100 to 0.07872, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00004: val_loss improved from 0.08100 to 0.07872, 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 [==============================] - 0s 2ms/step - loss: 0.0800 - val_loss: 0.0787\n",
      "Epoch 5/20\n",
      "195/235 [=======================>......] - ETA: 0s - loss: 0.0784\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00005: val_loss improved from 0.07872 to 0.07717, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00005: val_loss improved from 0.07872 to 0.07717, 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 [==============================] - 0s 2ms/step - loss: 0.0782 - val_loss: 0.0772\n",
      "Epoch 6/20\n",
      "233/235 [============================>.] - ETA: 0s - loss: 0.0766\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00006: val_loss improved from 0.07717 to 0.07559, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00006: val_loss improved from 0.07717 to 0.07559, 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 [==============================] - 0s 2ms/step - loss: 0.0766 - val_loss: 0.0756\n",
      "Epoch 7/20\n",
      "199/235 [========================>.....] - ETA: 0s - loss: 0.0755\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00007: val_loss improved from 0.07559 to 0.07447, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00007: val_loss improved from 0.07559 to 0.07447, 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: 0.0753 - val_loss: 0.0745\n",
      "Epoch 8/20\n",
      "223/235 [===========================>..] - ETA: 0s - loss: 0.0743\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00008: val_loss improved from 0.07447 to 0.07323, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00008: val_loss improved from 0.07447 to 0.07323, 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: 0.0743 - val_loss: 0.0732\n",
      "Epoch 9/20\n",
      "229/235 [============================>.] - ETA: 0s - loss: 0.0749\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00009: val_loss improved from 0.07323 to 0.07277, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00009: val_loss improved from 0.07323 to 0.07277, 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 [==============================] - 0s 2ms/step - loss: 0.0749 - val_loss: 0.0728\n",
      "Epoch 10/20\n",
      "198/235 [========================>.....] - ETA: 0s - loss: 0.0726\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00010: val_loss improved from 0.07277 to 0.07133, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00010: val_loss improved from 0.07277 to 0.07133, 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 [==============================] - 0s 2ms/step - loss: 0.0725 - val_loss: 0.0713\n",
      "Epoch 11/20\n",
      "198/235 [========================>.....] - ETA: 0s - loss: 0.0714\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00011: val_loss improved from 0.07133 to 0.07048, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00011: val_loss improved from 0.07133 to 0.07048, 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 [==============================] - 0s 2ms/step - loss: 0.0714 - val_loss: 0.0705\n",
      "Epoch 12/20\n",
      "233/235 [============================>.] - ETA: 0s - loss: 0.0706\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00012: val_loss improved from 0.07048 to 0.06995, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00012: val_loss improved from 0.07048 to 0.06995, 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 [==============================] - 0s 2ms/step - loss: 0.0706 - val_loss: 0.0700\n",
      "Epoch 13/20\n",
      "228/235 [============================>.] - ETA: 0s - loss: 0.0700\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00013: val_loss improved from 0.06995 to 0.06939, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00013: val_loss improved from 0.06995 to 0.06939, 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: 0.0700 - val_loss: 0.0694\n",
      "Epoch 14/20\n",
      "195/235 [=======================>......] - ETA: 0s - loss: 0.0695\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00014: val_loss improved from 0.06939 to 0.06900, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00014: val_loss improved from 0.06939 to 0.06900, 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 [==============================] - 0s 2ms/step - loss: 0.0695 - val_loss: 0.0690\n",
      "Epoch 15/20\n",
      "229/235 [============================>.] - ETA: 0s - loss: 0.0690\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00015: val_loss improved from 0.06900 to 0.06850, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00015: val_loss improved from 0.06900 to 0.06850, 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 [==============================] - 0s 2ms/step - loss: 0.0690 - val_loss: 0.0685\n",
      "Epoch 16/20\n",
      "196/235 [========================>.....] - ETA: 0s - loss: 0.0686\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00016: val_loss improved from 0.06850 to 0.06810, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00016: val_loss improved from 0.06850 to 0.06810, 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 [==============================] - 0s 2ms/step - loss: 0.0685 - val_loss: 0.0681\n",
      "Epoch 17/20\n",
      "232/235 [============================>.] - ETA: 0s - loss: 0.0680\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00017: val_loss improved from 0.06810 to 0.06753, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00017: val_loss improved from 0.06810 to 0.06753, 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 [==============================] - 0s 2ms/step - loss: 0.0680 - val_loss: 0.0675\n",
      "Epoch 18/20\n",
      "235/235 [==============================] - ETA: 0s - loss: 0.0675\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00018: val_loss improved from 0.06753 to 0.06712, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00018: val_loss improved from 0.06753 to 0.06712, 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 [==============================] - 0s 2ms/step - loss: 0.0675 - val_loss: 0.0671\n",
      "Epoch 19/20\n",
      "197/235 [========================>.....] - ETA: 0s - loss: 0.0671\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00019: val_loss improved from 0.06712 to 0.06662, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00019: val_loss improved from 0.06712 to 0.06662, 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 [==============================] - 0s 2ms/step - loss: 0.0671 - val_loss: 0.0666\n",
      "Epoch 20/20\n",
      "220/235 [===========================>..] - ETA: 0s - loss: 0.0667\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00020: val_loss improved from 0.06662 to 0.06636, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00020: val_loss improved from 0.06662 to 0.06636, 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 2ms/step - loss: 0.0667 - val_loss: 0.0664\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": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"functional_1\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "encoder_input (InputLayer)      [(None, 64)]         0                                            \n",
      "__________________________________________________________________________________________________\n",
      "dense_8 (Dense)                 (None, 32)           2080        encoder_input[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dense_9 (Dense)                 (None, 16)           528         dense_8[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "encoder_output (Dense)          (None, 2)            34          dense_9[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_10 (Dense)                (None, 16)           48          encoder_output[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "dense_12 (Dense)                (None, 16)           48          encoder_output[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "dense_11 (Dense)                (None, 32)           544         dense_10[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dense_13 (Dense)                (None, 32)           544         dense_12[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "ecoder_output (Dense)           (None, 64)           2112        dense_11[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "classifier_output (Dense)       (None, 10)           330         dense_13[0][0]                   \n",
      "==================================================================================================\n",
      "Total params: 6,268\n",
      "Trainable params: 6,268\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: 3:59 - loss: 0.4406 - ecoder_output_loss: 0.2103 - classifier_output_loss: 2.3024 - ecoder_output_accuracy: 0.0059 - classifier_output_accuracy: 0.0879  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: 2.0552s). Check your callbacks.\n",
      "217/235 [==========================>...] - ETA: 0s - loss: 0.3162 - ecoder_output_loss: 0.1241 - classifier_output_loss: 1.9214 - ecoder_output_accuracy: 0.0744 - classifier_output_accuracy: 0.2501\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 0.24790, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 0.24790, 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 [==============================] - 4s 15ms/step - loss: 0.3112 - ecoder_output_loss: 0.1221 - classifier_output_loss: 1.8910 - ecoder_output_accuracy: 0.0734 - classifier_output_accuracy: 0.2661 - val_loss: 0.2479 - val_ecoder_output_loss: 0.0979 - val_classifier_output_loss: 1.5000 - val_ecoder_output_accuracy: 0.0473 - val_classifier_output_accuracy: 0.4734\n",
      "Epoch 2/20\n",
      "226/235 [===========================>..] - ETA: 0s - loss: 0.2172 - ecoder_output_loss: 0.0940 - classifier_output_loss: 1.2326 - ecoder_output_accuracy: 0.0634 - classifier_output_accuracy: 0.6076\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00002: val_loss improved from 0.24790 to 0.19235, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00002: val_loss improved from 0.24790 to 0.19235, 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 4ms/step - loss: 0.2161 - ecoder_output_loss: 0.0938 - classifier_output_loss: 1.2233 - ecoder_output_accuracy: 0.0634 - classifier_output_accuracy: 0.6106 - val_loss: 0.1924 - val_ecoder_output_loss: 0.0897 - val_classifier_output_loss: 1.0266 - val_ecoder_output_accuracy: 0.0639 - val_classifier_output_accuracy: 0.6784\n",
      "Epoch 3/20\n",
      "232/235 [============================>.] - ETA: 0s - loss: 0.1775 - ecoder_output_loss: 0.0868 - classifier_output_loss: 0.9066 - ecoder_output_accuracy: 0.0718 - classifier_output_accuracy: 0.7119\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00003: val_loss improved from 0.19235 to 0.16938, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00003: val_loss improved from 0.19235 to 0.16938, 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: 0.1774 - ecoder_output_loss: 0.0868 - classifier_output_loss: 0.9060 - ecoder_output_accuracy: 0.0719 - classifier_output_accuracy: 0.7123 - val_loss: 0.1694 - val_ecoder_output_loss: 0.0849 - val_classifier_output_loss: 0.8448 - val_ecoder_output_accuracy: 0.0811 - val_classifier_output_accuracy: 0.7235\n",
      "Epoch 4/20\n",
      "227/235 [===========================>..] - ETA: 0s - loss: 0.1627 - ecoder_output_loss: 0.0838 - classifier_output_loss: 0.7896 - ecoder_output_accuracy: 0.0755 - classifier_output_accuracy: 0.7440\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00004: val_loss improved from 0.16938 to 0.15889, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00004: val_loss improved from 0.16938 to 0.15889, 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 4ms/step - loss: 0.1626 - ecoder_output_loss: 0.0838 - classifier_output_loss: 0.7885 - ecoder_output_accuracy: 0.0754 - classifier_output_accuracy: 0.7443 - val_loss: 0.1589 - val_ecoder_output_loss: 0.0826 - val_classifier_output_loss: 0.7632 - val_ecoder_output_accuracy: 0.0725 - val_classifier_output_accuracy: 0.7568\n",
      "Epoch 5/20\n",
      "217/235 [==========================>...] - ETA: 0s - loss: 0.1545 - ecoder_output_loss: 0.0821 - classifier_output_loss: 0.7235 - ecoder_output_accuracy: 0.0748 - classifier_output_accuracy: 0.7656\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00005: val_loss improved from 0.15889 to 0.15303, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00005: val_loss improved from 0.15889 to 0.15303, 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 4ms/step - loss: 0.1542 - ecoder_output_loss: 0.0820 - classifier_output_loss: 0.7215 - ecoder_output_accuracy: 0.0751 - classifier_output_accuracy: 0.7667 - val_loss: 0.1530 - val_ecoder_output_loss: 0.0814 - val_classifier_output_loss: 0.7158 - val_ecoder_output_accuracy: 0.0708 - val_classifier_output_accuracy: 0.7671\n",
      "Epoch 6/20\n",
      "216/235 [==========================>...] - ETA: 0s - loss: 0.1496 - ecoder_output_loss: 0.0816 - classifier_output_loss: 0.6797 - ecoder_output_accuracy: 0.0711 - classifier_output_accuracy: 0.7829\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00006: val_loss improved from 0.15303 to 0.14840, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00006: val_loss improved from 0.15303 to 0.14840, 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 4ms/step - loss: 0.1491 - ecoder_output_loss: 0.0815 - classifier_output_loss: 0.6759 - ecoder_output_accuracy: 0.0712 - classifier_output_accuracy: 0.7843 - val_loss: 0.1484 - val_ecoder_output_loss: 0.0809 - val_classifier_output_loss: 0.6749 - val_ecoder_output_accuracy: 0.0566 - val_classifier_output_accuracy: 0.7902\n",
      "Epoch 7/20\n",
      "220/235 [===========================>..] - ETA: 0s - loss: 0.1448 - ecoder_output_loss: 0.0811 - classifier_output_loss: 0.6369 - ecoder_output_accuracy: 0.0661 - classifier_output_accuracy: 0.7995\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00007: val_loss improved from 0.14840 to 0.14447, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00007: val_loss improved from 0.14840 to 0.14447, 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 4ms/step - loss: 0.1448 - ecoder_output_loss: 0.0811 - classifier_output_loss: 0.6367 - ecoder_output_accuracy: 0.0660 - classifier_output_accuracy: 0.7997 - val_loss: 0.1445 - val_ecoder_output_loss: 0.0806 - val_classifier_output_loss: 0.6390 - val_ecoder_output_accuracy: 0.0564 - val_classifier_output_accuracy: 0.8072\n",
      "Epoch 8/20\n",
      "230/235 [============================>.] - ETA: 0s - loss: 0.1408 - ecoder_output_loss: 0.0805 - classifier_output_loss: 0.6033 - ecoder_output_accuracy: 0.0631 - classifier_output_accuracy: 0.8133\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00008: val_loss improved from 0.14447 to 0.14027, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00008: val_loss improved from 0.14447 to 0.14027, 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 5ms/step - loss: 0.1408 - ecoder_output_loss: 0.0805 - classifier_output_loss: 0.6034 - ecoder_output_accuracy: 0.0632 - classifier_output_accuracy: 0.8131 - val_loss: 0.1403 - val_ecoder_output_loss: 0.0797 - val_classifier_output_loss: 0.6053 - val_ecoder_output_accuracy: 0.0644 - val_classifier_output_accuracy: 0.8201\n",
      "Epoch 9/20\n",
      "220/235 [===========================>..] - ETA: 0s - loss: 0.1372 - ecoder_output_loss: 0.0800 - classifier_output_loss: 0.5718 - ecoder_output_accuracy: 0.0589 - classifier_output_accuracy: 0.8293\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00009: val_loss improved from 0.14027 to 0.13633, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00009: val_loss improved from 0.14027 to 0.13633, 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: 0.1371 - ecoder_output_loss: 0.0801 - classifier_output_loss: 0.5699 - ecoder_output_accuracy: 0.0592 - classifier_output_accuracy: 0.8301 - val_loss: 0.1363 - val_ecoder_output_loss: 0.0793 - val_classifier_output_loss: 0.5708 - val_ecoder_output_accuracy: 0.0477 - val_classifier_output_accuracy: 0.8373\n",
      "Epoch 10/20\n",
      "215/235 [==========================>...] - ETA: 0s - loss: 0.1333 - ecoder_output_loss: 0.0794 - classifier_output_loss: 0.5392 - ecoder_output_accuracy: 0.0585 - classifier_output_accuracy: 0.8427\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00010: val_loss improved from 0.13633 to 0.13236, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00010: val_loss improved from 0.13633 to 0.13236, 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 5ms/step - loss: 0.1333 - ecoder_output_loss: 0.0793 - classifier_output_loss: 0.5394 - ecoder_output_accuracy: 0.0587 - classifier_output_accuracy: 0.8432 - val_loss: 0.1324 - val_ecoder_output_loss: 0.0780 - val_classifier_output_loss: 0.5436 - val_ecoder_output_accuracy: 0.0526 - val_classifier_output_accuracy: 0.8487\n",
      "Epoch 11/20\n",
      "218/235 [==========================>...] - ETA: 0s - loss: 0.1294 - ecoder_output_loss: 0.0780 - classifier_output_loss: 0.5139 - ecoder_output_accuracy: 0.0574 - classifier_output_accuracy: 0.8541\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00011: val_loss improved from 0.13236 to 0.12883, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00011: val_loss improved from 0.13236 to 0.12883, 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 4ms/step - loss: 0.1295 - ecoder_output_loss: 0.0780 - classifier_output_loss: 0.5150 - ecoder_output_accuracy: 0.0574 - classifier_output_accuracy: 0.8539 - val_loss: 0.1288 - val_ecoder_output_loss: 0.0769 - val_classifier_output_loss: 0.5192 - val_ecoder_output_accuracy: 0.0591 - val_classifier_output_accuracy: 0.8608\n",
      "Epoch 12/20\n",
      "222/235 [===========================>..] - ETA: 0s - loss: 0.1269 - ecoder_output_loss: 0.0774 - classifier_output_loss: 0.4941 - ecoder_output_accuracy: 0.0584 - classifier_output_accuracy: 0.8607\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00012: val_loss improved from 0.12883 to 0.12783, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00012: val_loss improved from 0.12883 to 0.12783, 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 4ms/step - loss: 0.1268 - ecoder_output_loss: 0.0774 - classifier_output_loss: 0.4934 - ecoder_output_accuracy: 0.0587 - classifier_output_accuracy: 0.8610 - val_loss: 0.1278 - val_ecoder_output_loss: 0.0767 - val_classifier_output_loss: 0.5110 - val_ecoder_output_accuracy: 0.0547 - val_classifier_output_accuracy: 0.8601\n",
      "Epoch 13/20\n",
      "218/235 [==========================>...] - ETA: 0s - loss: 0.1249 - ecoder_output_loss: 0.0772 - classifier_output_loss: 0.4772 - ecoder_output_accuracy: 0.0571 - classifier_output_accuracy: 0.8665\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00013: val_loss improved from 0.12783 to 0.12550, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00013: val_loss improved from 0.12783 to 0.12550, 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 4ms/step - loss: 0.1247 - ecoder_output_loss: 0.0771 - classifier_output_loss: 0.4754 - ecoder_output_accuracy: 0.0573 - classifier_output_accuracy: 0.8670 - val_loss: 0.1255 - val_ecoder_output_loss: 0.0763 - val_classifier_output_loss: 0.4920 - val_ecoder_output_accuracy: 0.0515 - val_classifier_output_accuracy: 0.8699\n",
      "Epoch 14/20\n",
      "219/235 [==========================>...] - ETA: 0s - loss: 0.1232 - ecoder_output_loss: 0.0769 - classifier_output_loss: 0.4630 - ecoder_output_accuracy: 0.0555 - classifier_output_accuracy: 0.8713\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00014: val_loss improved from 0.12550 to 0.12347, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00014: val_loss improved from 0.12550 to 0.12347, 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: 0.1229 - ecoder_output_loss: 0.0769 - classifier_output_loss: 0.4602 - ecoder_output_accuracy: 0.0561 - classifier_output_accuracy: 0.8720 - val_loss: 0.1235 - val_ecoder_output_loss: 0.0761 - val_classifier_output_loss: 0.4742 - val_ecoder_output_accuracy: 0.0702 - val_classifier_output_accuracy: 0.8722\n",
      "Epoch 15/20\n",
      "225/235 [===========================>..] - ETA: 0s - loss: 0.1216 - ecoder_output_loss: 0.0767 - classifier_output_loss: 0.4489 - ecoder_output_accuracy: 0.0562 - classifier_output_accuracy: 0.8741\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00015: val_loss improved from 0.12347 to 0.12188, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00015: val_loss improved from 0.12347 to 0.12188, 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: 0.1214 - ecoder_output_loss: 0.0767 - classifier_output_loss: 0.4472 - ecoder_output_accuracy: 0.0561 - classifier_output_accuracy: 0.8746 - val_loss: 0.1219 - val_ecoder_output_loss: 0.0760 - val_classifier_output_loss: 0.4589 - val_ecoder_output_accuracy: 0.0582 - val_classifier_output_accuracy: 0.8761\n",
      "Epoch 16/20\n",
      "219/235 [==========================>...] - ETA: 0s - loss: 0.1195 - ecoder_output_loss: 0.0763 - classifier_output_loss: 0.4319 - ecoder_output_accuracy: 0.0530 - classifier_output_accuracy: 0.8794\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00016: val_loss improved from 0.12188 to 0.12097, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00016: val_loss improved from 0.12188 to 0.12097, 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: 0.1196 - ecoder_output_loss: 0.0763 - classifier_output_loss: 0.4333 - ecoder_output_accuracy: 0.0530 - classifier_output_accuracy: 0.8792 - val_loss: 0.1210 - val_ecoder_output_loss: 0.0756 - val_classifier_output_loss: 0.4539 - val_ecoder_output_accuracy: 0.0508 - val_classifier_output_accuracy: 0.8806\n",
      "Epoch 17/20\n",
      "224/235 [===========================>..] - ETA: 0s - loss: 0.1180 - ecoder_output_loss: 0.0759 - classifier_output_loss: 0.4205 - ecoder_output_accuracy: 0.0534 - classifier_output_accuracy: 0.8827\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00017: val_loss improved from 0.12097 to 0.11881, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00017: val_loss improved from 0.12097 to 0.11881, 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 4ms/step - loss: 0.1180 - ecoder_output_loss: 0.0759 - classifier_output_loss: 0.4212 - ecoder_output_accuracy: 0.0534 - classifier_output_accuracy: 0.8823 - val_loss: 0.1188 - val_ecoder_output_loss: 0.0752 - val_classifier_output_loss: 0.4365 - val_ecoder_output_accuracy: 0.0589 - val_classifier_output_accuracy: 0.8859\n",
      "Epoch 18/20\n",
      "222/235 [===========================>..] - ETA: 0s - loss: 0.1167 - ecoder_output_loss: 0.0756 - classifier_output_loss: 0.4110 - ecoder_output_accuracy: 0.0538 - classifier_output_accuracy: 0.8861\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00018: val_loss improved from 0.11881 to 0.11782, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00018: val_loss improved from 0.11881 to 0.11782, 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: 0.1166 - ecoder_output_loss: 0.0756 - classifier_output_loss: 0.4100 - ecoder_output_accuracy: 0.0535 - classifier_output_accuracy: 0.8866 - val_loss: 0.1178 - val_ecoder_output_loss: 0.0748 - val_classifier_output_loss: 0.4299 - val_ecoder_output_accuracy: 0.0579 - val_classifier_output_accuracy: 0.8847\n",
      "Epoch 19/20\n",
      "220/235 [===========================>..] - ETA: 0s - loss: 0.1153 - ecoder_output_loss: 0.0753 - classifier_output_loss: 0.4003 - ecoder_output_accuracy: 0.0530 - classifier_output_accuracy: 0.8883\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00019: val_loss improved from 0.11782 to 0.11626, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00019: val_loss improved from 0.11782 to 0.11626, 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: 0.1154 - ecoder_output_loss: 0.0753 - classifier_output_loss: 0.4011 - ecoder_output_accuracy: 0.0532 - classifier_output_accuracy: 0.8882 - val_loss: 0.1163 - val_ecoder_output_loss: 0.0743 - val_classifier_output_loss: 0.4194 - val_ecoder_output_accuracy: 0.0503 - val_classifier_output_accuracy: 0.8886\n",
      "Epoch 20/20\n",
      "223/235 [===========================>..] - ETA: 0s - loss: 0.1142 - ecoder_output_loss: 0.0749 - classifier_output_loss: 0.3929 - ecoder_output_accuracy: 0.0526 - classifier_output_accuracy: 0.8913\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00020: val_loss improved from 0.11626 to 0.11528, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00020: val_loss improved from 0.11626 to 0.11528, 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 5ms/step - loss: 0.1143 - ecoder_output_loss: 0.0749 - classifier_output_loss: 0.3941 - ecoder_output_accuracy: 0.0523 - classifier_output_accuracy: 0.8910 - val_loss: 0.1153 - val_ecoder_output_loss: 0.0740 - val_classifier_output_loss: 0.4124 - val_ecoder_output_accuracy: 0.0538 - val_classifier_output_accuracy: 0.8907\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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c9CTy0hLJTE8kNy2J7NQE4j19nEOjSqIIfVf0Dl1RlwgGk6985St4PB4A6uvrufLKK9m4cSMiQmdnZ8B1zj33XJKSkkhKSmL48OHs3LmTwkK74NoMYt4uaK2FVqeO3NdcTUdjNR0N1XibqvG17EZaaohrrcHTWk1yew0Jvt6nQCcCLZrGbs2kmmFU6QhqOITm+Gw6kvPwpuTRlVZAZ+pI4jLySE9JITM5nozkBEalJHCoO5yZ4jynJ8XjievHs+yi+Iy9qEsEB/LPPVzS0tL2DN92222ccsopPPvss5SXl3PyyScHXCcp6bP7pHs8Hrq6usIdpolVPh90tjj/zjubneeOFuhowtfeRFNTA02N9bQ01tPW0khXawNxbXUkdtSS3FFHclc96d560rVpr5eNA5LdR4smUUs6tZpBraZTTTHVOo2WBOfg7kvNJy5jOAmZBaRkjSB3WDp56YnkpSdRkpFETlpi3//QTb+JukQwWNXX1zN6tHM93RNPPBHZYExsaK2DytWwYxVUfgS7PkbbG9D2JuhoJq6rtc9V46DXWSzNmkQ96dRLJlVxmTR7xtOaMoz2hGw6krLoSspGU3LQlFzi0nLxpOeSkpZBepKHtMR4cpLjmZTuHNwT7OA+qFgiGCA/+tGPuPLKK7nrrrs499xzIx2OiTZNu9Adq2jb8j4dFR+QsGsNqc2fXdhfE5fLBi1iZ1chLZpEM8m0kESLJtFCMpKY7pylkpZJanom6RmZZGZmk5U1jOysHPKys8jPTGFUgodREXybJjyG3D2Lp0+frj1vTPPxxx8zefLkCEU08GLt/RpHR5ePnfWtVG37lI6t7xO3czWZtWsZ3vIJOd6aPctt9g1njRaz1lfCJ3El1GZMJiVnJCOHJTNiWDLDM5IYnuk8F2Qmk5eeRGK8/UOPdiKyUlUDnqtuJQJjwqWzFSpWwJZlsHmpUz2jPhAPxMVDnMd5+I37xEOHT2j3CW1d0OoVWjuhuQu8XR1MkgrGiFMn71WhXApZlXQE1VmH0pJ7GHEjDyc/fzhjslI4NiuF3LRE65bE7JMlAmP6S2sdbH3XOehvXgrbPwBfJyBQMBUOPQ/ik+jq7KSprZ2m1jaa2zpobWuntbmdto4OOjs7icNHPD7i8JHs8ZIWDzlJQlJaEruzZlMz4ghSio4id/xRjE/NYHyk37cZ8iwRGHOgGis/O+hvWQY71wIKcQkw6ij0uO9QnTudlb6JrNiprN5WT3lNMzsb9r4KNSctkbG5qZTkpjE2N43ivFTnOTeVrNRoPGvdDDaWCIwJhSrsLv3soL95KdSWOfMS0mDM52iZ9WM2JB3GktaxrNzexqp369nd3AGUkhgfx5SRmZwwIZ+SPQf6NIpyUxmWYhcMmsiyRGBMIJ2tTtXO1ndh63LnuaXamZeSg3fMcWyfcCnvM5nX60fwQUUTW9a1ACCylYnD0znt0OFMG5PFkWOyOGREhp0yaQYtSwTGANRXuAf995xH5Ufgcy7m05zxNBedyvr4Q3m7cyILdw3j4zXNeH3OGXejhjUwbUwWlx5bxLTCLA4vHEZ6kv20zNBh39Z+cDDdUAMsWrSIxMREjj/++LDHaoCuDudA73/gb9zuzItPgdHH0HTMd/hIJrGgvoiFm71s+9C5+CojOZ5phclce1IB0wqdf/vDM5Mj+GaMOXiWCPrBvrqh3pdFixaRnp5uiSBcfD6oeA82zIct7zpVPl63wXZYEYydSdPwY/hAJ7GgJo+lpfWUbmgGICu1nZnjcrn2pHEcNy6X8fnpxPVn/zXGDAKWCMJk5cqVfP/736epqYm8vDyeeOIJRo4cyX333cdDDz1EfHw8U6ZM4e677+ahhx7C4/Hw97//nfvvv58TTzwx0uEPfaqw7X1Y+wysfQ4aKtyzeY6EGd+iueAYVnon8MZ2D8s+rWH9ikagg/SkXRxbksOlxxYxc3wuk0dk2oHfRL3oSwQv3eL0r9KfRhwOZ98d8uKqyvXXX89//vMf8vPzefLJJ/npT3/KY489xt13301ZWRlJSUnU1dWRlZXFtddeu9+lCBOAqlPls+YZWPss1G12Dv4TTqPzlJ/xbuKxLN7SzrKNNaxZVI9Pt5KcEMfninM4f9oojh+fy+Gjh1knZybmRF8iGATa29tZs2YNZ5xxBgBer5eRI0cCcMQRR3DZZZdx0UUXcdFFF0Uwyiiyc53zz3/NM7D7U+dK3XEn03XCD1maeCzPrW/h1ed20tj+MQke4aiibK4/dSLHj8/lyKIskuI9kX4HxkRU9CWC/fjnHi6qytSpU1m2bFmveS+++CJvvfUWzz//PL/85S9ZvbqfSy+xouoTt9rnWahaDxIHxSfSddz1vJM0k+c+aeeVFyppaNtIZnI8sw8bwTmHj+S4cbmkJNqB3xh/0ZcIBoGkpCSqqqpYtmwZM2fOpLOzk08++YTJkyezdetWTjnlFE444QTmzp1LU1MTGRkZNDT0713VotLu0s+qfXauAQTGHo/37N/yTtIJPLexkwXzK2lo+5SM5HjOmjqCcw8fyawJedapmjFBWCIIg7i4OJ5++mluuOEG6uvr6erq4qabbmLSpElcfvnl1NfXo6rccMMNZGVlcf755/PlL3+Z//znP9ZY7K9xJ5QvhrK3nOfdpc70whl4z/wV76WeyLObfCx4eSf1rWVkJMVzxtQCzjvCOfhblY8xobFuqIegqH2/zTXOAb98MZQthuoNzvSkTBg7C2/xiaxMmcUzpXG8vLaSupZO0pPiOXNKAeccPpITJ9nB35i+WDfUZnBqrXX67Clz//XvWutMT0iDsTPhyEvZmTeDNxtG8vandSxeWEVtyzbSEj2cMaWAc48YxYkT80hOsIO/MQfDEoEZOO2NsHkZlL/lHPh3fAQoxCdD0XFw2G00jJjJktYxLC6tZ+myaspraoFa8jOSOPmQ4cw+bAQnTcq3g78x/SisiUBEZgP3Ah7gEVW9u8f8IuAvQJa7zC2qOv9AtqWqMXEDjqFWlUfLblj/gtPAW/aW03+PJxEKZ8DJt9A+ZhbLO0t4u6yJJauqWfNSHap1pCfFc9y4HK48vphZE/KYODw9Jj5fYyIhbIlARDzAg8AZQAWwXETmqeo6v8V+Bjylqn8UkSnAfKB4f7eVnJxMTU0Nubm5UX2wUFVqampITh7kfdu01sL6F52Df+ki5+CfXQIzr8NbcgprPYeyuLyJJRurWbGwlo6uVXvO7//e6ZOYNSGXIwqzrLdOYwZIOEsEM4BNqloKICJzgQsB/0SgQKY7PAzYfiAbKiwspKKigqqqqoMId2hITk6msLAw0mH01loL6+fDuufg0zecO3NljYWZ11Fbch6v7B7O6xuqWLakhoa2lQBMHpnJlTPHcvyEPGYU55BmPXYaExHh/OWNBrb6jVcAx/ZY5g7gFRG5HkgDTg/0QiJyDXANQFFRUa/5CQkJlJSUHHzEZv+01TsH/7XPwqevOwf/YUXocd+mYtRZPF9VwMKPd/HB67tQ3cWoYcmcfdhIZk3M4/jxueSlJ0X6HRhjiHxj8SXAE6r6PyIyE/ibiBymqj7/hVT1YeBhcE4fjUCcpltbA2x4yT34vwbeDsgsxDfj/7Eu5zSe21nAwlW7KH+9HqjniMJhfO/0SZw+uYDJIzOiuurOmKEqnIlgGzDGb7zQnebvamA2gKouE5FkIA/YFca4zP5SdQ76yx+DTQudLpwzR9Nx9NWsTDuJf1WO4PX3qqhraSXRs4WZ43P55onjOG3ycEYOS4l09MaYfQhnIlgOTBSREpwEMAe4tMcyW4DTgCdEZDKQDER/Rf9Q0dUBa/4NS+93zvFPL6D5iCtYnHgic7cXsHRpLR1eH1mpVZx66HDOmFzAiZPy7e5cxgwxYfvFqmqXiFwHLMA5NfQxVV0rIncCK1R1HvAD4M8i8j2chuOrdMidHxmF2uph5RPwzkPQuJ3O3ENZfOgvuH/XND5Y5tyXd2xuK1fMHMsZUwo4Zmy2dd1szBAWFV1MmH5Svw3e/SOseAI6GtmR/Tke8Z3HozsnAMK0MVmcNbWAM6cUMD7fzus3ZiixLiZMcJVrYOn96JqnUZ+Pd1I+z90dp/PRjnEcOiKDH541igumjWJMTmqkIzXGhIElglilCqWL6Hr7PuLLXqdNknmy63T+3HU28WljOf/kUfzPtFFMLMiIdKTGmDCzRBBrvJ10rn6G1kX3kFm3jlodxuNdF7Mw9VxOmj6J/502isNHD7NqH2NiiCWCWKCKt6aU8qVPk7P6UbI7d7LZN4p7PNfiO+IrnH1kCTcX59hN2o2JUZYIoo0q1G2B7R/A9g/wbnsfb8X7JHY1MR5YqZP5sOgmxs/6Ij+ZONz68zHGWCIY0lShYfueg/6eR+tuALwSz3ot4sOuY6nPnsrkGacz87gTOMa6cDbG+LFEMJQ07ep90G/a6cwTDwyfQs2YM3i1bhRzt+WyQYs49bAxXH1CCUcXZUc2dmPMoGWJYCho2Q0v/wQ+mutOEMg/BMafCqOOpmvENBbWDufPy3aw8qNaMpLiuWRWEQ8eX8zoLOviwRgTnCWCwW7dPHjxB051z6wbYdJsGHEEJKXT2NbJk8u38vj/lbOtbh1FOan8/PwpfGX6GOvmwRgTMjtaDFZNVfDSD51ePkccAV97BkYcDsDW3S08/so6nlqxlab2LmYU53D7+VM4fXIBHjvzxxiznywRDDaqTkdv838IHU1w6m1OScCTwMrNu3lkcRkL1lYSJ8K5R4zk6hNKOKIwK9JRG2OGMEsEg0nDDqcaaMOLMPoYuPBBGD6Z7XWt3P6fVSz8eCfDUhL4fyeN54qZY62LZ2NMv7BEMBiowof/hAU/ga52OOO/YOZ38RLH35aU8dsFG/Ap3HL2oVwxcyypifaxGWP6jx1RIq1uK7xwk3PDl6KZcMEDkDeBj3c0cMszq1m1tY6TJuVz10WHWadvxpiwsEQQKT4fvP8EvHI7qA/O/i187pu0din3vrSePy8uJSslgXvnHMkF00ZZ3z/GmLCxRBAJu8tg3vVQvhhKPg8X3A/ZxSzeWMVPn13Dlt0tXDy9kFvPmUxWamKkozXGRDlLBAPJ54P3HobXfuFcCXzePXDMVdQ0d/DLJz/kmQ+2UZKXxj+/dSzHj8+LdLTGmBhhiWCg1G6GZ66Bre/AhDPg/HvQzNH8+/1t/PLFdTS1d3HDqRP4zikTSLa+gIwxA2ifiUBErgf+rqq1AxBPdNqxCv7xFehsg4v+CNMuobymhVsfeZeln9ZwzNhsfvXFw5lkN4ExxkRAKCWCAmC5iLwPPAYssBvM74dPX4cnvwbJWXD1PDpzJ/Hwok+577WNJHriuOuiw7h0RpHdC8AYEzH7TASq+jMRuQ04E/g68ICIPAU8qqqfhjvAIW3VXPjPdyHvELj8ad6vS+HW+99mfWUjZx82gjsumEpBZnKkozTGxLiQ2ghUVUWkEqgEuoBs4GkReVVVfxTOAIckVVhyDyy8A4pPhDn/4KWNLXznn0spyEjm4a8dw5lTR0Q6SmOMAUJrI7gRuAKoBh4BfqiqnSISB2wELBH483nh5Vucs4MO+xJc9Ee6JIHfLPiAQwoy+Ne1M8lIToh0lMYYs0coJYIc4Iuqutl/oqr6ROS8YCuKyGzgXsADPKKqd/eY/wfgFHc0FRiuqlkhxj74dLbCM9+Cj5+Hmdc5XUXExTHv/QrKqpt56PJjLAkYYwadUBLBS8Du7hERyQQmq+q7qvpxXyuJiAd4EDgDqMBpcJ6nquu6l1HV7/ktfz1w1P6/hUGiZTfMvRS2LIOz/htmfheALq+PB17fxKEjMjhzSkGEgzTGmN5CuXP5H4Emv/Emd9q+zAA2qWqpqnYAc4ELgyx/CfB/Ibzu4FO3FR6bDdtWwpcf25MEAJ7/aDul1c3cdPpEOzPIGDMohZIIxP90UVX1EVpJYjSw1W+8wp3WewMiY4ES4PU+5l8jIitEZEVVVVUImx5Alavh0TOgsRIuf8ZpF3B5fcr9r3WXBqxx2BgzOIWSCEpF5AYRSXAfNwKl/RzHHOBpVfUGmqmqD6vqdFWdnp+f38+bPgilb8JjZwMC33gJSk7ca/bzq5zSwI2nWWnAGDN4hZIIrgWOB7bh/Ks/FrgmhPW2AWP8xgvdaYHMYahVC61+Gv7+JRhWCN98FQqm7jXb61Pue30jh47I4Cw7VdQYM4iFckHZLpwD9f5aDkwUkRKcBDAHuLTnQiJyKM51CcsOYBsDTxWW3g+v3gZjZ8Gcf0BKdq/FXvhoO6VVzfzvZUdbacAYM6iFch1BMnA1MBXYcxmsqn4j2Hqq2iUi1wELcE4ffUxV14rIncAKVZ3nLjoHmDskuq3w+WDBrfDuH2HKRfCFP0FC7yuDvT7l3tc2ckhBBrOtNGCMGeRCafT9G7AeOAu4E7gM6PO0UX+qOh+Y32Pa7T3G7wjltSKuq93pPXTdc3Dst51TROMC16x1lwYevNRKA8aYwS+UNoIJqnob0KyqfwHOxWkniC3LH3GSwBn/BbN/1WcS8PqU+17byKSCdM4+zEoDxpjBL5RE0Ok+14nIYcAwYHj4QhqkShdB3iSYdQMEuW3ki6t38GlVMzeeNslKA8aYISGURPCwiGQDPwPmAeuAX4c1qsHG54Ut7ziNw0FYacAYMxQFbSNwO5ZrcG9K8xYwbkCiGmwqP4L2Big+Iehi81fvYNOuJh649CgrDRhjhoygJQL3KmLrXbT8bec5SCLwuaWBicPTOeewkQMUmDHGHLxQqoYWisjNIjJGRHK6H2GPbDApXwI54yGj7+qe+Wt2sHFXEzfYVcTGmCEmlNNHv+o+f9dvmhIr1UQ+L2xZClP67i+vuzQwYXg65xxupQFjzNASypXFJQMRyKC1cy201Tt3GuvDS2sq+WRnE/ddchQeKw0YY4aYUK4sviLQdFX9a/+HMwh1tw/0ccaQz6fc+9onTBiezrlWGjDGDEGhVA19zm84GTgNeB+IjUSweQlkF8OwgD1o8/JapzRw75wjrTRgjBmSQqkaut5/XESycG4yE/18PicRHHJuH7OVexduZHx+GucdMWqAgzPGmP4RyllDPTXj3EQm+lV9DK21fZ42umBtJRt2NnLDaROtNGCMGbJCaSN4HucsIXASxxTgqXAGNWjsuX6gd/uAz+1hdJyVBowxQ1wobQS/8xvuAjarakWY4hlcyt+GYUWQVdRr1oK1layvbOSer1rbgDFmaAslEWwBdqhqG4CIpIhIsaqWhzWySFOFzUth4hm9Zu0pDeSlcf40Kw0YY4a2UNoI/gX4/Ma97rToVrUBWqoDtg+8ss4pDVjbgDEmGoSSCOJVtaN7xB1ODF9Ig0T5Yue5x/UDTmlgk5UGjDFRI5REUCUiF3SPiMiFQHX4QhokNi+BzNHONQR+Xlm3k493NHD9aROsNGCMiQqhtBFcC/xDRB5wxyuAgFcbRw1Vp6O5cSfvdRMaVadPoZK8NM63M4WMMVEilAvKPgWOE5F0d7wp7FFFWs0maN7Vq33glXU7Wbejgd9fPI14z4FcgmGMMYPPPo9mIvLfIpKlqk2q2iQi2SJy10AEFzHd7QN+iUDVuYq4ODeVC6xtwBgTRUL5W3u2qtZ1j7h3KzsnbBENBuVLIH0E5HzW0/YbG3axbkcD15860UoDxpioEsoRzSMiSd0jIpICJAVZfg8RmS0iG0Rkk4jc0scyF4vIOhFZKyL/DC3sMFJ1GoqLZ+3VPvBeWS0JHuGCI600YIyJLqE0Fv8DeE1EHnfHv04IPY+KiAd4EDgDp4F5uYjMU9V1fstMBH4CzFLVWhEZvr9voN/tLoXGHb3aB8qrmynKSSXBSgPGmCgTSmPxr0VkFXC6O+m/VHVBCK89A9ikqqUAIjIXuBBY57fMt4AH3eomVHXX/gQfFnvuP7B3IiirbqYkLz0CARljTHiF9PdWVV9W1ZuBnwPDReTFEFYbDWz1G69wp/mbBEwSkSUi8o6IzA70QiJyjYisEJEVVVVVoYR84DYvgbThkDdxzySfTymvaWZcflp4t22MMREQyllDiSLyBRH5F7ADOBV4qJ+2Hw9MBE4GLgH+7N7vYC+q+rCqTlfV6fn5+f206QC6rx8Ye/xe7QM7Gtpo7/JRnGuJwBgTffpMBCJyptsuUAZ8CaddYLeqfl1Vnw/htbcBY/zGC91p/iqAearaqaplwCc4iSEy6jZDQ0Wv9oGyqmYASvIsERhjok+wEsHLwDjgBFW93D34+4Is39NyYKKIlIhIIjAHmNdjmedwSgOISB5OVVHpfmyjf+25/0DP9gHnGjqrGjLGRKNgieBoYBmwUEReFZGrAU+oL6yqXcB1wALgY+ApVV0rInf69V20AKgRkXXAG8APVbXmQN5IvyhfAqm5kH/oXpPLqltITfQwPCOks2aNMWZI6fOsIVX9EPgQuEVEjsepw08QkZeAZ1X14X29uKrOB+b3mHa737AC33cfkbf57V7tA+CUCIpz0xCxTuaMMdEn1LOGlro3sS8E/gAcF9aoIqFui/MoPrHXLOfUUasWMsZEp/26OkpVfar6iqp+I1wBRUz5Eue5x/0HOr0+tta2WiIwxkQtu0y22+a3ISUbhk/Za/LW3S14fWqJwBgTtSwRdCtfAkXHQ9zeu6S8xjl1tNgSgTEmSgVNBCLiEZH1AxVMxNRvg9qygPcnLnWvIRhnicAYE6WCJgJV9QIbRKRogOKJjM1u+0DxrF6zyqqbyUpNIDst+m/TbIyJTaH0PpoNrBWR94Dm7omqekHfqwwx5W9D0jAoOKz3rJpm61rCGBPVQkkEt4U9ikjbvATGzoS43tfLlVU1c9y43AgEZYwxA2OfjcWq+iZQDiS4w8uB98Mc18BprHTuURygfaC1w8v2+jZrKDbGRLVQeh/9FvA08Cd30micPoKiw577D/RuH9i82zqbM8ZEv1BOH/0uMAtoAFDVjUDk7yTWXzYvgcQMGHFEr1nW66gxJhaEkgjaVbWje0RE4gENX0gDrHwJFB0Hnt7NJWV2DYExJgaEkgjeFJFbgRQROQP4FxDK/QgGv6YqqN4QsH0AnBLB8Iwk0pNCaVM3xpihKZREcAtQBawG/h9Ob6I/C2dQA2Zz4PsPdLPO5owxsSCUm9f7gD+7j+hSvgQS0mDktMCza5o5fXLBAAdljDEDq89EICJPqerFIrKaAG0Cqtq7dXWo2bwEio4FT0KvWfWtnVQ3dViJwBgT9YKVCG5yn88bgDgGXnMN7FoHh3854OzyajtjyBgTG4Ilghdwbld5l6p+bYDiGTjd/QuNDdw+0N3rqCUCY0y0C5YIEkXkUuB4Efliz5mq+kz4whoAm5dAfAqMOirg7NKqZkSgKDd1gAMzxpiBFSwRXAtcBmQB5/eYp8DQTgTlS2DMDIgP3KtoWXUzo7NSSIrv3f+QMcZEk2A3r38beFtEVqjqowMYU/i11sLONXDKT/tcpLzGTh01xsSGYGcNnaqqrwO1UVc1tHkZoAHvPwCgqpRVNfPFo0cPbFzGGBMBwaqGTgJep3e1EAz1qqHytyE+GUYfE3B2TXMHje1d1rWEMSYmBKsa+rn7/PUDfXERmQ3cC3iAR1T17h7zrwJ+C2xzJz2gqo8c6PZCtvltKPwcxCcFnF1mp44aY2JIKN1Q3ygimeJ4RETeF5EzQ1jPAzwInA1MAS4RkSkBFn1SVY90H+FPAm31ULm6z24l4LNeR8flpYc9HGOMibRQ+hr6hqo2AGcCucDXgLuDrwLADGCTqpa6vZfOBS484Ej7y5Z3QH0B7z/QraymmQSPMCoreQADM8aYyAglEYj7fA7wV1Vd6zctmNHAVr/xCndaT18SkY9E5GkRGRMwAJFrRGSFiKyoqqoKYdNBlC8GTyIUTu9zkbKqZopyUon3hLJ7jDFmaAvlSLdSRF7BSQQLRCQD8PXT9p8Hit1+i14F/hJoIVV9WFWnq+r0/Pz8g9ti+RIYPR0SUvpcxHodNcbEklASwdU4XVF/TlVbgAQglAbkbYD/P/xCPmsUBkBVa1S13R19BAh8Gk9/aW+EHauCtg/4fGrXEBhjYkooiWAmsEFV60Tkcpx7EdSHsN5yYKKIlIhIIjAHmOe/gIiM9Bu9APg4tLAP0JZ3Qb19Xj8AsKOhjfYuHyXWUGyMiRGhJII/Ai0iMg34AfAp8Nd9raSqXcB1wAKcA/xTqrpWRO4UkQvcxW4QkbUisgq4AbjqAN5D6MoXQ1wCFM7oe5Hq7ttTWh9DxpjYEMo9GLtUVUXkQpzz/B8VkatDeXFVnY9zRzP/abf7Df8E+Mn+BHxQNi+B0UdDYt8H+dJqO3XUGBNbQikRNIrIT4DLgRdFJA6nnWBo6WiG7R8EbR8A54yhlAQPBZmBLzYzxphoE0oi+CrQDlytqpU4jb6/DWtU4bD1XfB1Bb1+AJzO5orz0hAJ5QxZY4wZ+kK5Z3El8Hu/8S2E0EYw6Gx9D8QDY44NulhZdTNTRmYOUFDGGBN5oXQxcZyILBeRJhHpEBGviIRy1tDg8vkfwfUrIKnvuv9Or48tu1usodgYE1NCqRp6ALgE2AikAN8E/jecQYVFXBzkjAu6SEVtK16f2qmjxpiYElIfCqq6CfCoqldVHwdmhzesyCirbgKs11FjTGwJ5fTRFveCsA9F5DfADkJMIENNaZV1P22MiT2hHNC/hnM/geuAZpxuI74UzqAipbymmWEpCWSnDr2zY40x5kCFctbQZnewFfhFeMOJrO7O5uzUUWNMLAl2z+LVOLekDMjtMTSqlFe3MKMkJ9JhGGPMgApWIjhvwKIYBNo6vWyra7X2AWNMzAmWCBKAAlVd4j9RRGYBlWGNKgLKa7o7m7NEYIyJLcEai+8BGgJMb3DnRZXyPZ3NWSIwxsSWYImgQFVX95zoTisOW0QRUlptJQJjTGwKlgiygszr+z6PQ1RZVTP5GUmkJ4VyaYUxxkSPYIlghYh8q+dEEfkmsDJ8IUWG3Z7SGBOrgv39vQl4VkQu47MD/3QgEfhCmOMacGXVzZw+uSDSYRhjzIDrMxGo6k7geBE5BTjMnfyiqr4+IJENoIa2TqqbOqx9wBgTk0K5svgN4I0BiCVius8YsqohY0wsisrO4/ZXmZ06aoyJYZYIcBKBCIzJsRvSGGNijyUCnEQwOiuF5ARPpEMxxpgBF9ZEICKzRWSDiGwSkVuCLPclEVERmR7OePrS3euoMcbEorAlAhHxAA8CZwNTgEtEZEqA5TKAG4F3wxVLMKpqicAYE9PCWSKYAWxS1VJV7QDmAhcGWO6/gF8DbWGMpU81zR00tnVZIjDGxKxwJoLRwFa/8Qp32h4icjQwRlVfDGMcQZVZH0PGmBgXscZiEYkDfg/8IIRlrxGRFSKyoqqqql/jsFNHjTGxLpyJYBvO/Y27FbrTumXgXLG8SETKgeOAeYEajFX1YVWdrqrT8/Pz+zXIsupmEjzC6Kyo60fPGGNCEs5EsByYKCIlIpIIzAHmdc9U1XpVzVPVYlUtBt4BLlDVFWGMqZfy6mbG5KQS77EzaY0xsSlsRz9V7QKuAxYAHwNPqepaEblTRC4I13b3V1l1s1ULGWNiWlg731fV+cD8HtNu72PZk8MZSyA+n3Pq6AkT8gZ608YYM2jEdH1IZUMb7V0+SvKtRGCMiV0xnQjKrNdRY4yJ7URQaonAGGNiOxGUVzeTkuChICM50qEYY0zExHQiKKtupjgvjbg4iXQoxhgTMTGfCEry7B4ExpjYFrOJoNPrY+vuFmsfMMbEvJhNBBW1rXT5lOJcSwTGmNgWs4mg+4b14+waAmNMjIvZRPDZqaPpEY7EGGMiK2YTQVl1E5nJ8WSnJkQ6FGOMiaiYTQTl1S2U5KcjYqeOGmNiW8wmAut11BhjHDGZCNo6vWyra7UzhowxhhhNBJtrWgCs11FjjCFGE0FZdRMAJVYiMMaYWE0ETomg2LqXMMaYWE0ETeRnJJGRbKeOGmNMjCaCZqsWMsYYV4wmAutszhhjusVcImho66S6qd3OGDLGGFfMJYLuzubsGgJjjHHEXCIos15HjTFmL2FNBCIyW0Q2iMgmEbklwPxrRWS1iHwoIm+LyJRwxgNOIhCBohw7ddQYYyCMiUBEPMCDwNnAFOCSAAf6f6rq4ap6JPAb4PfhiqdbWXUzo4alkJzgCfemjDFmSAhniWAGsElVS1W1A5gLXOi/gKo2+I2mARrGeACnjcCqhYwx5jPhTASjga1+4xXutL2IyHdF5FOcEsENgV5IRK4RkRUisqKqquqAA1JVSqubraHYGGP8RLyxWFUfVNXxwI+Bn/WxzMOqOl1Vp+fn5x/wtnY3d9DY1mXXEBhjjJ9wJoJtwBi/8UJ3Wl/mAheFMZ49ZwzZNQTGGPOZcCaC5cBEESkRkURgDjDPfwERmeg3ei6wMYzxfHafYqsaMsaYPeLD9cKq2iUi1wELAA/wmKquFZE7gRWqOg+4TkROBzqBWuDKcMUDTkNxfJxQmJ0Szs0YY8yQErZEAKCq84H5Pabd7jd8Yzi331NZdTNFuanEeyLeNGKMMYNGTB0RrddRY4zpLWYSgc+nlNc02xlDxhjTQ8wkgsqGNto6fRRbIjDGmL3ETCLY09mcJQJjjNlLzCUCu4bAGGP2FjOJYHhGEmdOKaAgIznSoRhjzKAS1tNHB5Mzp47gzKkjIh2GMcYMOjFTIjDGGBOYJQJjjIlxlgiMMSbGWSIwxpgYZ4nAGGNinCUCY4yJcZYIjDEmxlkiMMaYGCeqGukY9ouIVAGbD3D1PKC6H8PpbxbfwbH4Dt5gj9HiO3BjVTXgTd+HXCI4GCKyQlWnRzqOvlh8B8fiO3iDPUaLLzysasgYY2KcJQJjjIlxsZYIHo50APtg8R0ci+/gDfYYLb4wiKk2AmOMMb3FWonAGGNMD5YIjDEmxkVlIhCR2SKyQUQ2icgtAeYniciT7vx3RaR4AGMbIyJviMg6EVkrIjcGWOZkEakXkQ/dx+0DFZ+7/XIRWe1ue0WA+SIi97n77yMROXoAYzvEb798KCINInJTj2UGfP+JyGMisktE1vhNyxGRV0Vko/uc3ce6V7rLbBSRKwcott+KyHr383tWRLL6WDfodyHMMd4hItv8Psdz+lg36O89jPE96RdbuYh82Me6A7IPD4qqRtUD8ACfAuOARGAVMKXHMt8BHnKH5wBPDmB8I4Gj3eEM4JMA8Z0MvBDBfVgO5AWZfw7wEiDAccC7EfysK3EulIno/gM+DxwNrPGb9hvgFnf4FuDXAdbLAUrd52x3OHsAYjsTiHeHfx0otlC+C2GO8Q7g5hC+A0F/7+GKr8f8/wFuj+Q+PJhHNJYIZgCbVLVUVTuAucCFPZa5EPiLO/w0cJqIyEAEp6o7VPV9d7gR+BgYPRDb7kcXAn9VxztAloiMjEAcpwGfquqBXmneb1T1LWB3j8n+37O/ABcFWPUs4FVV3a2qtcCrwOxwx6aqr6hqlzv6DlDYn9vcX33sv1CE8ns/aMHic48dFwP/19/bHSjRmAhGA1v9xivofaDds4z7Y6gHcgckOj9uldRRwLsBZs8UkVUi8pKITB3YyFDgFRFZKSLXBJgfyj4eCHPo+8cXyf3XrUBVd7jDlUBBgGUGw778Bk4JL5B9fRfC7Tq3+uqxPqrWBsP+OxHYqaob+5gf6X24T9GYCIYEEUkH/g3cpKoNPWa/j1PdMQ24H3hugMM7QVWPBs4Gvisinx/g7e+TiCQCFwD/CjA70vuvF3XqCAbdudoi8lOgC/hHH4tE8rvwR2A8cCSwA6f6ZTC6hOClgUH/e4rGRLANGOM3XuhOC7iMiMQDw4CaAYnO2WYCThL4h6o+03O+qjaoapM7PB9IEJG8gYpPVbe5z7uAZ3GK3/5C2cfhdjbwvqru7Dkj0vvPz87uKjP3eVeAZSK2L0XkKuA84DI3UfUSwnchbFR1p6p6VdUH/LmPbUf0u+geP74IPNnXMpHch6GKxkSwHJgoIiXuv8Y5wLwey8wDus/O+DLwel8/hP7m1ic+Cnysqr/vY5kR3W0WIjID53MakEQlImkiktE9jNOouKbHYvOAK9yzh44D6v2qQAZKn//CIrn/evD/nl0J/CfAMguAM0Uk2636ONOdFlYiMhv4EXCBqrb0sUwo34Vwxujf7vSFPrYdyu89nE4H1qtqRaCZkd6HIYt0a3U4HjhntXyCczbBT91pd+J86QGScaoUNgHvAeMGMLYTcKoIPgI+dB/nANcC17rLXAesxTkD4h3g+AGMb5y73VVuDN37zz8+AR509+9qYPoAf75pOAf2YX7TIrr/cJLSDqATp576apx2p9eAjcBCIMdddjrwiN+633C/i5uArw9QbJtw6ta7v4PdZ9GNAuYH+y4M4P77m/v9+gjn4D6yZ4zueK/f+0DE505/ovt757dsRPbhwTysiwljjIlx0Vg1ZIwxZj9YIjDGmBhnicAYY2KcJQJjjIlxlgiMMSbGWSIwpgcR8crePZz2W4+WIlLs34OlMYNBfKQDMGYQalXVIyMdhDEDxUoExoTI7Vf+N27f8u+JyAR3erGIvO52jvaaiBS50wvcvv5XuY/j3ZfyiMifxbkfxSsikhKxN2UMlgiMCSSlR9XQV/3m1avq4cADwD3utPuBv6jqETidt93nTr8PeFOdzu+OxrmyFGAi8KCqTgXqgC+F9d0Ysw92ZbExPYhIk6qmB5heDpyqqqVux4GVqporItU43R90utN3qGqeiFQBhara7vcaxTj3H5jojv8YSFDVuwbgrRkTkJUIjNk/2sfw/mj3G/ZibXUmwiwRGLN/vur3vMwdXorT6yXAZcBid/g14NsAIuIRkWEDFaQx+8P+iRjTW0qPG5G/rKrdp5Bmi8hHOP/qL3GnXQ88LiI/BKqAr7vTbwQeFpGrcf75fxunB0tjBhVrIzAmRG4bwXRVrY50LMb0J6saMsaYGGclAmOMiXFWIjDGmBhnicAYY2KcJQJjjIlxlgiMMSbGWSIwxpgY9/8BTKIz8x4btKwAAAAASUVORK5CYII=",
      "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": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"functional_3\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "encoder_input (InputLayer)      [(None, 64)]         0                                            \n",
      "__________________________________________________________________________________________________\n",
      "dense_14 (Dense)                (None, 32)           2080        encoder_input[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "prune_low_magnitude_dense_15 (P (None, 16)           1042        dense_14[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "encoder_output (Dense)          (None, 2)            34          prune_low_magnitude_dense_15[0][0\n",
      "__________________________________________________________________________________________________\n",
      "prune_low_magnitude_dense_16 (P (None, 16)           82          encoder_output[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "prune_low_magnitude_dense_18 (P (None, 16)           82          encoder_output[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "dense_17 (Dense)                (None, 32)           544         prune_low_magnitude_dense_16[0][0\n",
      "__________________________________________________________________________________________________\n",
      "dense_19 (Dense)                (None, 32)           544         prune_low_magnitude_dense_18[0][0\n",
      "__________________________________________________________________________________________________\n",
      "ecoder_output (Dense)           (None, 64)           2112        dense_17[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "classifier_output (Dense)       (None, 10)           330         dense_19[0][0]                   \n",
      "==================================================================================================\n",
      "Total params: 6,850\n",
      "Trainable params: 6,268\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 0x000001594AD64670> 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 0x000001594AD64670> 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 0x000001594C4FF670> 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 0x000001594C4FF670> 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 0x000001594AD64160> 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 0x000001594AD64160> 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 0x0000015948392790> 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 0x0000015948392790> 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 0x0000015948543820> 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 0x0000015948543820> 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 0x0000015948392700> 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 0x0000015948392700> 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: 6:03 - loss: 0.4386 - ecoder_output_loss: 0.2082 - classifier_output_loss: 2.3037 - ecoder_output_accuracy: 0.0059 - classifier_output_accuracy: 0.0625WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0000s vs `on_train_batch_end` time: 3.1086s). Check your callbacks.\n",
      "223/235 [===========================>..] - ETA: 0s - loss: 0.3162 - ecoder_output_loss: 0.1189 - classifier_output_loss: 1.9729 - ecoder_output_accuracy: 0.0298 - classifier_output_accuracy: 0.2629\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 0.23696, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 0.23696, 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 [==============================] - 5s 20ms/step - loss: 0.3124 - ecoder_output_loss: 0.1175 - classifier_output_loss: 1.9487 - ecoder_output_accuracy: 0.0298 - classifier_output_accuracy: 0.2729 - val_loss: 0.2370 - val_ecoder_output_loss: 0.0905 - val_classifier_output_loss: 1.4647 - val_ecoder_output_accuracy: 0.0411 - val_classifier_output_accuracy: 0.4671\n",
      "Epoch 2/20\n",
      "231/235 [============================>.] - ETA: 0s - loss: 0.2127 - ecoder_output_loss: 0.0875 - classifier_output_loss: 1.2522 - ecoder_output_accuracy: 0.0262 - classifier_output_accuracy: 0.5347\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00002: val_loss improved from 0.23696 to 0.19147, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00002: val_loss improved from 0.23696 to 0.19147, 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 5ms/step - loss: 0.2124 - ecoder_output_loss: 0.0875 - classifier_output_loss: 1.2493 - ecoder_output_accuracy: 0.0262 - classifier_output_accuracy: 0.5354 - val_loss: 0.1915 - val_ecoder_output_loss: 0.0844 - val_classifier_output_loss: 1.0705 - val_ecoder_output_accuracy: 0.0210 - val_classifier_output_accuracy: 0.5984\n",
      "Epoch 3/20\n",
      "229/235 [============================>.] - ETA: 0s - loss: 0.1813 - ecoder_output_loss: 0.0828 - classifier_output_loss: 0.9853 - ecoder_output_accuracy: 0.0535 - classifier_output_accuracy: 0.6316\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00003: val_loss improved from 0.19147 to 0.17160, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00003: val_loss improved from 0.19147 to 0.17160, 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 5ms/step - loss: 0.1811 - ecoder_output_loss: 0.0827 - classifier_output_loss: 0.9833 - ecoder_output_accuracy: 0.0536 - classifier_output_accuracy: 0.6325 - val_loss: 0.1716 - val_ecoder_output_loss: 0.0807 - val_classifier_output_loss: 0.9093 - val_ecoder_output_accuracy: 0.0623 - val_classifier_output_accuracy: 0.6831\n",
      "Epoch 4/20\n",
      "228/235 [============================>.] - ETA: 0s - loss: 0.1663 - ecoder_output_loss: 0.0806 - classifier_output_loss: 0.8570 - ecoder_output_accuracy: 0.0621 - classifier_output_accuracy: 0.7085\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00004: val_loss improved from 0.17160 to 0.15929, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00004: val_loss improved from 0.17160 to 0.15929, 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 4ms/step - loss: 0.1661 - ecoder_output_loss: 0.0806 - classifier_output_loss: 0.8557 - ecoder_output_accuracy: 0.0620 - classifier_output_accuracy: 0.7093 - val_loss: 0.1593 - val_ecoder_output_loss: 0.0793 - val_classifier_output_loss: 0.8000 - val_ecoder_output_accuracy: 0.0442 - val_classifier_output_accuracy: 0.7340\n",
      "Epoch 5/20\n",
      "221/235 [===========================>..] - ETA: 0s - loss: 0.1552 - ecoder_output_loss: 0.0796 - classifier_output_loss: 0.7554 - ecoder_output_accuracy: 0.0598 - classifier_output_accuracy: 0.7580\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00005: val_loss improved from 0.15929 to 0.14978, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00005: val_loss improved from 0.15929 to 0.14978, 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 5ms/step - loss: 0.1551 - ecoder_output_loss: 0.0796 - classifier_output_loss: 0.7547 - ecoder_output_accuracy: 0.0595 - classifier_output_accuracy: 0.7587 - val_loss: 0.1498 - val_ecoder_output_loss: 0.0786 - val_classifier_output_loss: 0.7121 - val_ecoder_output_accuracy: 0.0534 - val_classifier_output_accuracy: 0.7776\n",
      "Epoch 6/20\n",
      "216/235 [==========================>...] - ETA: 0s - loss: 0.1478 - ecoder_output_loss: 0.0787 - classifier_output_loss: 0.6901 - ecoder_output_accuracy: 0.0563 - classifier_output_accuracy: 0.7854\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00006: val_loss improved from 0.14978 to 0.14480, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00006: val_loss improved from 0.14978 to 0.14480, 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 5ms/step - loss: 0.1476 - ecoder_output_loss: 0.0787 - classifier_output_loss: 0.6881 - ecoder_output_accuracy: 0.0564 - classifier_output_accuracy: 0.7859 - val_loss: 0.1448 - val_ecoder_output_loss: 0.0779 - val_classifier_output_loss: 0.6694 - val_ecoder_output_accuracy: 0.0487 - val_classifier_output_accuracy: 0.7994\n",
      "Epoch 7/20\n",
      "233/235 [============================>.] - ETA: 0s - loss: 0.1419 - ecoder_output_loss: 0.0783 - classifier_output_loss: 0.6364 - ecoder_output_accuracy: 0.0540 - classifier_output_accuracy: 0.8056\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00007: val_loss improved from 0.14480 to 0.13908, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00007: val_loss improved from 0.14480 to 0.13908, 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 5ms/step - loss: 0.1419 - ecoder_output_loss: 0.0783 - classifier_output_loss: 0.6363 - ecoder_output_accuracy: 0.0539 - classifier_output_accuracy: 0.8056 - val_loss: 0.1391 - val_ecoder_output_loss: 0.0775 - val_classifier_output_loss: 0.6161 - val_ecoder_output_accuracy: 0.0452 - val_classifier_output_accuracy: 0.8139\n",
      "Epoch 8/20\n",
      "234/235 [============================>.] - ETA: 0s - loss: 0.1376 - ecoder_output_loss: 0.0780 - classifier_output_loss: 0.5957 - ecoder_output_accuracy: 0.0537 - classifier_output_accuracy: 0.8220\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00008: val_loss improved from 0.13908 to 0.13583, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00008: val_loss improved from 0.13908 to 0.13583, 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 5ms/step - loss: 0.1375 - ecoder_output_loss: 0.0780 - classifier_output_loss: 0.5954 - ecoder_output_accuracy: 0.0538 - classifier_output_accuracy: 0.8222 - val_loss: 0.1358 - val_ecoder_output_loss: 0.0775 - val_classifier_output_loss: 0.5834 - val_ecoder_output_accuracy: 0.0446 - val_classifier_output_accuracy: 0.8252\n",
      "Epoch 9/20\n",
      "227/235 [===========================>..] - ETA: 0s - loss: 0.1676 - ecoder_output_loss: 0.0795 - classifier_output_loss: 0.8813 - ecoder_output_accuracy: 0.0568 - classifier_output_accuracy: 0.7557\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00009: val_loss did not improve from 0.13583\n",
      "\n",
      "Epoch 00009: val_loss did not improve from 0.13583\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: 0.1670 - ecoder_output_loss: 0.0794 - classifier_output_loss: 0.8761 - ecoder_output_accuracy: 0.0572 - classifier_output_accuracy: 0.7568 - val_loss: 0.1513 - val_ecoder_output_loss: 0.0778 - val_classifier_output_loss: 0.7351 - val_ecoder_output_accuracy: 0.0512 - val_classifier_output_accuracy: 0.7999\n",
      "Epoch 10/20\n",
      "233/235 [============================>.] - ETA: 0s - loss: 0.1451 - ecoder_output_loss: 0.0778 - classifier_output_loss: 0.6725 - ecoder_output_accuracy: 0.0571 - classifier_output_accuracy: 0.8069\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00010: val_loss did not improve from 0.13583\n",
      "\n",
      "Epoch 00010: val_loss did not improve from 0.13583\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: 0.1450 - ecoder_output_loss: 0.0778 - classifier_output_loss: 0.6719 - ecoder_output_accuracy: 0.0572 - classifier_output_accuracy: 0.8071 - val_loss: 0.1395 - val_ecoder_output_loss: 0.0768 - val_classifier_output_loss: 0.6268 - val_ecoder_output_accuracy: 0.0537 - val_classifier_output_accuracy: 0.8229\n",
      "Epoch 11/20\n",
      "216/235 [==========================>...] - ETA: 0s - loss: 0.1382 - ecoder_output_loss: 0.0772 - classifier_output_loss: 0.6094 - ecoder_output_accuracy: 0.0586 - classifier_output_accuracy: 0.8250\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00011: val_loss improved from 0.13583 to 0.13490, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00011: val_loss improved from 0.13583 to 0.13490, 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 4ms/step - loss: 0.1380 - ecoder_output_loss: 0.0773 - classifier_output_loss: 0.6070 - ecoder_output_accuracy: 0.0586 - classifier_output_accuracy: 0.8258 - val_loss: 0.1349 - val_ecoder_output_loss: 0.0764 - val_classifier_output_loss: 0.5849 - val_ecoder_output_accuracy: 0.0527 - val_classifier_output_accuracy: 0.8312\n",
      "Epoch 12/20\n",
      "234/235 [============================>.] - ETA: 0s - loss: 0.1340 - ecoder_output_loss: 0.0768 - classifier_output_loss: 0.5716 - ecoder_output_accuracy: 0.0588 - classifier_output_accuracy: 0.8355\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00012: val_loss improved from 0.13490 to 0.13065, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00012: val_loss improved from 0.13490 to 0.13065, 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 5ms/step - loss: 0.1340 - ecoder_output_loss: 0.0768 - classifier_output_loss: 0.5714 - ecoder_output_accuracy: 0.0588 - classifier_output_accuracy: 0.8356 - val_loss: 0.1307 - val_ecoder_output_loss: 0.0759 - val_classifier_output_loss: 0.5480 - val_ecoder_output_accuracy: 0.0533 - val_classifier_output_accuracy: 0.8482\n",
      "Epoch 13/20\n",
      "224/235 [===========================>..] - ETA: 0s - loss: 0.1311 - ecoder_output_loss: 0.0765 - classifier_output_loss: 0.5469 - ecoder_output_accuracy: 0.0610 - classifier_output_accuracy: 0.8426\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00013: val_loss improved from 0.13065 to 0.12872, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00013: val_loss improved from 0.13065 to 0.12872, 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 5ms/step - loss: 0.1310 - ecoder_output_loss: 0.0764 - classifier_output_loss: 0.5456 - ecoder_output_accuracy: 0.0613 - classifier_output_accuracy: 0.8429 - val_loss: 0.1287 - val_ecoder_output_loss: 0.0756 - val_classifier_output_loss: 0.5314 - val_ecoder_output_accuracy: 0.0589 - val_classifier_output_accuracy: 0.8513\n",
      "Epoch 14/20\n",
      "219/235 [==========================>...] - ETA: 0s - loss: 0.1286 - ecoder_output_loss: 0.0760 - classifier_output_loss: 0.5253 - ecoder_output_accuracy: 0.0627 - classifier_output_accuracy: 0.8499\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00014: val_loss improved from 0.12872 to 0.12676, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00014: val_loss improved from 0.12872 to 0.12676, 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 5ms/step - loss: 0.1286 - ecoder_output_loss: 0.0760 - classifier_output_loss: 0.5259 - ecoder_output_accuracy: 0.0625 - classifier_output_accuracy: 0.8496 - val_loss: 0.1268 - val_ecoder_output_loss: 0.0752 - val_classifier_output_loss: 0.5157 - val_ecoder_output_accuracy: 0.0581 - val_classifier_output_accuracy: 0.8527\n",
      "Epoch 15/20\n",
      "224/235 [===========================>..] - ETA: 0s - loss: 0.1267 - ecoder_output_loss: 0.0757 - classifier_output_loss: 0.5100 - ecoder_output_accuracy: 0.0628 - classifier_output_accuracy: 0.8539\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00015: val_loss improved from 0.12676 to 0.12489, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00015: val_loss improved from 0.12676 to 0.12489, 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 5ms/step - loss: 0.1266 - ecoder_output_loss: 0.0757 - classifier_output_loss: 0.5088 - ecoder_output_accuracy: 0.0627 - classifier_output_accuracy: 0.8542 - val_loss: 0.1249 - val_ecoder_output_loss: 0.0748 - val_classifier_output_loss: 0.5007 - val_ecoder_output_accuracy: 0.0586 - val_classifier_output_accuracy: 0.8607\n",
      "Epoch 16/20\n",
      "229/235 [============================>.] - ETA: 0s - loss: 0.1249 - ecoder_output_loss: 0.0754 - classifier_output_loss: 0.4946 - ecoder_output_accuracy: 0.0628 - classifier_output_accuracy: 0.8595\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00016: val_loss improved from 0.12489 to 0.12328, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00016: val_loss improved from 0.12489 to 0.12328, 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 5ms/step - loss: 0.1249 - ecoder_output_loss: 0.0754 - classifier_output_loss: 0.4946 - ecoder_output_accuracy: 0.0632 - classifier_output_accuracy: 0.8594 - val_loss: 0.1233 - val_ecoder_output_loss: 0.0745 - val_classifier_output_loss: 0.4878 - val_ecoder_output_accuracy: 0.0562 - val_classifier_output_accuracy: 0.8646\n",
      "Epoch 17/20\n",
      "224/235 [===========================>..] - ETA: 0s - loss: 0.1235 - ecoder_output_loss: 0.0751 - classifier_output_loss: 0.4839 - ecoder_output_accuracy: 0.0635 - classifier_output_accuracy: 0.8626\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00017: val_loss improved from 0.12328 to 0.12189, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00017: val_loss improved from 0.12328 to 0.12189, 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 5ms/step - loss: 0.1235 - ecoder_output_loss: 0.0751 - classifier_output_loss: 0.4832 - ecoder_output_accuracy: 0.0632 - classifier_output_accuracy: 0.8628 - val_loss: 0.1219 - val_ecoder_output_loss: 0.0743 - val_classifier_output_loss: 0.4759 - val_ecoder_output_accuracy: 0.0553 - val_classifier_output_accuracy: 0.8642\n",
      "Epoch 18/20\n",
      "219/235 [==========================>...] - ETA: 0s - loss: 0.1222 - ecoder_output_loss: 0.0748 - classifier_output_loss: 0.4739 - ecoder_output_accuracy: 0.0617 - classifier_output_accuracy: 0.8648\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00018: val_loss improved from 0.12189 to 0.12101, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00018: val_loss improved from 0.12189 to 0.12101, 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 5ms/step - loss: 0.1221 - ecoder_output_loss: 0.0749 - classifier_output_loss: 0.4728 - ecoder_output_accuracy: 0.0617 - classifier_output_accuracy: 0.8655 - val_loss: 0.1210 - val_ecoder_output_loss: 0.0742 - val_classifier_output_loss: 0.4683 - val_ecoder_output_accuracy: 0.0607 - val_classifier_output_accuracy: 0.8676\n",
      "Epoch 19/20\n",
      "235/235 [==============================] - ETA: 0s - loss: 0.1209 - ecoder_output_loss: 0.0746 - classifier_output_loss: 0.4623 - ecoder_output_accuracy: 0.0615 - classifier_output_accuracy: 0.8683\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00019: val_loss improved from 0.12101 to 0.11957, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00019: val_loss improved from 0.12101 to 0.11957, 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 5ms/step - loss: 0.1209 - ecoder_output_loss: 0.0746 - classifier_output_loss: 0.4623 - ecoder_output_accuracy: 0.0615 - classifier_output_accuracy: 0.8683 - val_loss: 0.1196 - val_ecoder_output_loss: 0.0738 - val_classifier_output_loss: 0.4576 - val_ecoder_output_accuracy: 0.0603 - val_classifier_output_accuracy: 0.8704\n",
      "Epoch 20/20\n",
      "226/235 [===========================>..] - ETA: 0s - loss: 0.1199 - ecoder_output_loss: 0.0744 - classifier_output_loss: 0.4548 - ecoder_output_accuracy: 0.0625 - classifier_output_accuracy: 0.8709\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00020: val_loss improved from 0.11957 to 0.11913, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00020: val_loss improved from 0.11957 to 0.11913, 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 5ms/step - loss: 0.1199 - ecoder_output_loss: 0.0744 - classifier_output_loss: 0.4543 - ecoder_output_accuracy: 0.0623 - classifier_output_accuracy: 0.8709 - val_loss: 0.1191 - val_ecoder_output_loss: 0.0737 - val_classifier_output_loss: 0.4543 - val_ecoder_output_accuracy: 0.0585 - val_classifier_output_accuracy: 0.8721\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\")"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "789093bb736187c46b4a361b2c83e244a48ff9bb7ad40e66e5ffb40455c8edff"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}