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 l1, 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": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import MNIST_dataset as mnist\n",
    "size_final = 28\n",
    "\n",
    "data = mnist.MNISTData(size_initial=28, size_final=size_final, color_depth=8)\n",
    "train= data.x_train\n",
    "ax = plt.subplot(1, 1 , 1)\n",
    "\n",
    "plt.imshow(train[0].reshape(size_final,size_final), cmap='gray_r')\n",
    "ax.get_xaxis().set_visible(False)\n",
    "ax.get_yaxis().set_visible(False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import MNIST_dataset as mnist\n",
    "\n",
    "#Choose the final size of your image dataset\n",
    "size_final = 8\n",
    "\n",
    "data_zoom = mnist.MNISTData(size_initial=20, size_final=size_final, color_depth=5, flat=True)\n",
    "train= data_zoom.x_train\n",
    "ax = plt.subplot(1, 1 , 1)\n",
    "\n",
    "plt.imshow(train[0].reshape(size_final,size_final), cmap='gray_r')\n",
    "ax.get_xaxis().set_visible(False)\n",
    "ax.get_yaxis().set_visible(False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "markdown",
     "checksum": "cec58d97b4c05e29e9afdeb102569f20",
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     "grade_id": "cell-6ee6a27aac2a7e5b",
     "locked": true,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "source": [
    "# Autoencoder using Keras\n",
    "The aim of this class is to implement a simple autoencoder for the MNIST data and then build an autoencoder that is\n",
    "able to classify MNIST data in its latent dimension.\n",
    "\n",
    "Code partially adapted from [Keras Documentation](https://blog.keras.io/building-autoencoders-in-keras.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "8f093c3123887a615df510d5cb9d3bee",
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     "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.decoder_model = None\n",
    "        self.latent_classifier = None\n",
    "        self.classifier = None\n",
    "        self.autoencoder = None\n",
    "\n",
    "        self.history = None\n",
    "\n",
    "        self.pruning_params = {\"pruning_schedule\" : pruning_schedule.ConstantSparsity(0.85, begin_step=2000, end_step=10000, 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, name='fc1', activation='relu')(self.input)\n",
    "        if self.pruned:\n",
    "            self.encoder = prune_low_magnitude(Dense(16, name='fc2_prun',  activation='relu'), **self.pruning_params)(self.encoder) \n",
    "        \n",
    "        if not self.pruned:\n",
    "            self.encoder = Dense(16, name='fc2',  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,  name='fc3_pruned', activation='relu'), **self.pruning_params)(self.encoder)\n",
    "        \n",
    "        if not self.pruned:\n",
    "            self.decoder = Dense(16, name='fc3', activation='relu')(self.encoder)\n",
    "        self.decoder = Dense(32, name='fc4', 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, name='fc3_prunclass', activation='relu'), **self.pruning_params)(self.encoder)\n",
    "\n",
    "        if not self.pruned:\n",
    "            self.latent_classifier = Dense(16, name='fc3_class', activation='relu')(self.encoder)\n",
    "        \n",
    "        self.latent_classifier = Dense(32, name='fc4_class', activation='relu')(self.latent_classifier)\n",
    "        self.latent_classifier = Dense(self.num_classes, activation='softmax', name='classifier_output')(self.latent_classifier)\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=30, 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",
    "            self.autoencoder = strip_pruning(self.autoencoder)\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, model_name = None):\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",
    "            \n",
    "            plt.savefig('./images/AE/reconstructed images{model_name}.png'.format(model_name=model_name))\n",
    "            plt.show()\n",
    "            \n",
    "    def plot_latent(self):\n",
    "        n = 6\n",
    "        plt.figure(figsize=(10, 3))\n",
    "        reco_imgs = self.encoder_model.predict(self.x_test)\n",
    "        img_size = int(np.sqrt(self.input_shape[0]))\n",
    "\n",
    "        ax = plt.subplot(1, 1, 1)\n",
    "        plt.imshow(reco_imgs[1].reshape(2,1), cmap='gray_r')\n",
    "        ax.get_xaxis().set_visible(False)\n",
    "        ax.get_yaxis().set_visible(False)\n",
    "\n",
    "        plt.show()\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": 4,
   "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 1/30\n",
      "  1/235 [..............................] - ETA: 0s - loss: 0.2094WARNING:tensorflow:From C:\\Users\\loren\\miniconda3\\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",
      "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: 0.0417s). Check your callbacks.\n",
      "206/235 [=========================>....] - ETA: 0s - loss: 0.1209\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 0.09399, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 0.09399, 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 4ms/step - loss: 0.1178 - val_loss: 0.0940\n",
      "Epoch 2/30\n",
      "230/235 [============================>.] - ETA: 0s - loss: 0.0876\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00002: val_loss improved from 0.09399 to 0.08205, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00002: val_loss improved from 0.09399 to 0.08205, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00002: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00002: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 2ms/step - loss: 0.0876 - val_loss: 0.0820\n",
      "Epoch 3/30\n",
      "200/235 [========================>.....] - ETA: 0s - loss: 0.0797\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00003: val_loss improved from 0.08205 to 0.07617, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00003: val_loss improved from 0.08205 to 0.07617, 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: 0.0793 - val_loss: 0.0762\n",
      "Epoch 4/30\n",
      "231/235 [============================>.] - ETA: 0s - loss: 0.0747\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00004: val_loss improved from 0.07617 to 0.07311, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00004: val_loss improved from 0.07617 to 0.07311, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00004: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00004: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 2ms/step - loss: 0.0747 - val_loss: 0.0731\n",
      "Epoch 5/30\n",
      "228/235 [============================>.] - ETA: 0s - loss: 0.0725\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00005: val_loss improved from 0.07311 to 0.07165, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00005: val_loss improved from 0.07311 to 0.07165, 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 2ms/step - loss: 0.0725 - val_loss: 0.0716\n",
      "Epoch 6/30\n",
      "225/235 [===========================>..] - ETA: 0s - loss: 0.0713\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00006: val_loss improved from 0.07165 to 0.07074, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00006: val_loss improved from 0.07165 to 0.07074, 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 2ms/step - loss: 0.0712 - val_loss: 0.0707\n",
      "Epoch 7/30\n",
      "227/235 [===========================>..] - ETA: 0s - loss: 0.0703\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00007: val_loss improved from 0.07074 to 0.06991, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00007: val_loss improved from 0.07074 to 0.06991, 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: 0.0703 - val_loss: 0.0699\n",
      "Epoch 8/30\n",
      "197/235 [========================>.....] - ETA: 0s - loss: 0.0696\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00008: val_loss improved from 0.06991 to 0.06931, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00008: val_loss improved from 0.06991 to 0.06931, 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 2ms/step - loss: 0.0696 - val_loss: 0.0693\n",
      "Epoch 9/30\n",
      "199/235 [========================>.....] - ETA: 0s - loss: 0.0691\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00009: val_loss improved from 0.06931 to 0.06875, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00009: val_loss improved from 0.06931 to 0.06875, 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: 0.0690 - val_loss: 0.0687\n",
      "Epoch 10/30\n",
      "198/235 [========================>.....] - ETA: 0s - loss: 0.0684\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00010: val_loss improved from 0.06875 to 0.06832, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00010: val_loss improved from 0.06875 to 0.06832, 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 2ms/step - loss: 0.0684 - val_loss: 0.0683\n",
      "Epoch 11/30\n",
      "198/235 [========================>.....] - ETA: 0s - loss: 0.0680\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00011: val_loss improved from 0.06832 to 0.06777, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00011: val_loss improved from 0.06832 to 0.06777, 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 2ms/step - loss: 0.0680 - val_loss: 0.0678\n",
      "Epoch 12/30\n",
      "199/235 [========================>.....] - ETA: 0s - loss: 0.0676\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00012: val_loss improved from 0.06777 to 0.06741, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00012: val_loss improved from 0.06777 to 0.06741, 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: 0.0675 - val_loss: 0.0674\n",
      "Epoch 13/30\n",
      "224/235 [===========================>..] - ETA: 0s - loss: 0.0671\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00013: val_loss improved from 0.06741 to 0.06703, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00013: val_loss improved from 0.06741 to 0.06703, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00013: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00013: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 2ms/step - loss: 0.0671 - val_loss: 0.0670\n",
      "Epoch 14/30\n",
      "218/235 [==========================>...] - ETA: 0s - loss: 0.0668\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00014: val_loss improved from 0.06703 to 0.06673, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00014: val_loss improved from 0.06703 to 0.06673, 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 2ms/step - loss: 0.0667 - val_loss: 0.0667\n",
      "Epoch 15/30\n",
      "199/235 [========================>.....] - ETA: 0s - loss: 0.0664\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00015: val_loss improved from 0.06673 to 0.06635, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00015: val_loss improved from 0.06673 to 0.06635, 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: 0.0664 - val_loss: 0.0664\n",
      "Epoch 16/30\n",
      "197/235 [========================>.....] - ETA: 0s - loss: 0.0660\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00016: val_loss improved from 0.06635 to 0.06610, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00016: val_loss improved from 0.06635 to 0.06610, 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: 0.0661 - val_loss: 0.0661\n",
      "Epoch 17/30\n",
      "197/235 [========================>.....] - ETA: 0s - loss: 0.0658\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00017: val_loss improved from 0.06610 to 0.06578, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00017: val_loss improved from 0.06610 to 0.06578, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00017: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00017: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 2ms/step - loss: 0.0658 - val_loss: 0.0658\n",
      "Epoch 18/30\n",
      "197/235 [========================>.....] - ETA: 0s - loss: 0.0655\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00018: val_loss improved from 0.06578 to 0.06560, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00018: val_loss improved from 0.06578 to 0.06560, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00018: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00018: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 2ms/step - loss: 0.0655 - val_loss: 0.0656\n",
      "Epoch 19/30\n",
      "235/235 [==============================] - ETA: 0s - loss: 0.0653\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00019: val_loss improved from 0.06560 to 0.06530, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00019: val_loss improved from 0.06560 to 0.06530, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00019: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00019: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 2ms/step - loss: 0.0653 - val_loss: 0.0653\n",
      "Epoch 20/30\n",
      "224/235 [===========================>..] - ETA: 0s - loss: 0.0650\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00020: val_loss improved from 0.06530 to 0.06511, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00020: val_loss improved from 0.06530 to 0.06511, 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: 0.0650 - val_loss: 0.0651\n",
      "Epoch 21/30\n",
      "200/235 [========================>.....] - ETA: 0s - loss: 0.0648\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00021: val_loss improved from 0.06511 to 0.06499, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00021: val_loss improved from 0.06511 to 0.06499, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00021: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00021: 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: 0.0648 - val_loss: 0.0650\n",
      "Epoch 22/30\n",
      "233/235 [============================>.] - ETA: 0s - loss: 0.0646\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00022: val_loss improved from 0.06499 to 0.06489, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00022: val_loss improved from 0.06499 to 0.06489, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00022: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00022: 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: 0.0646 - val_loss: 0.0649\n",
      "Epoch 23/30\n",
      "235/235 [==============================] - ETA: 0s - loss: 0.0644\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00023: val_loss improved from 0.06489 to 0.06457, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00023: val_loss improved from 0.06489 to 0.06457, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00023: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00023: 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: 0.0644 - val_loss: 0.0646\n",
      "Epoch 24/30\n",
      "234/235 [============================>.] - ETA: 0s - loss: 0.0642\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00024: val_loss improved from 0.06457 to 0.06452, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00024: val_loss improved from 0.06457 to 0.06452, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00024: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00024: 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: 0.0642 - val_loss: 0.0645\n",
      "Epoch 25/30\n",
      "227/235 [===========================>..] - ETA: 0s - loss: 0.0641\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00025: val_loss improved from 0.06452 to 0.06431, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00025: val_loss improved from 0.06452 to 0.06431, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00025: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00025: 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: 0.0641 - val_loss: 0.0643\n",
      "Epoch 26/30\n",
      "223/235 [===========================>..] - ETA: 0s - loss: 0.0639\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00026: val_loss improved from 0.06431 to 0.06410, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00026: val_loss improved from 0.06431 to 0.06410, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00026: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00026: 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: 0.0639 - val_loss: 0.0641\n",
      "Epoch 27/30\n",
      "234/235 [============================>.] - ETA: 0s - loss: 0.0637\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00027: val_loss improved from 0.06410 to 0.06402, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00027: val_loss improved from 0.06410 to 0.06402, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00027: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00027: 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: 0.0637 - val_loss: 0.0640\n",
      "Epoch 28/30\n",
      "194/235 [=======================>......] - ETA: 0s - loss: 0.0635\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00028: val_loss improved from 0.06402 to 0.06380, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00028: val_loss improved from 0.06402 to 0.06380, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00028: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00028: 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: 0.0635 - val_loss: 0.0638\n",
      "Epoch 29/30\n",
      "233/235 [============================>.] - ETA: 0s - loss: 0.0634\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00029: val_loss improved from 0.06380 to 0.06360, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00029: val_loss improved from 0.06380 to 0.06360, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00029: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00029: 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: 0.0634 - val_loss: 0.0636\n",
      "Epoch 30/30\n",
      "201/235 [========================>.....] - ETA: 0s - loss: 0.0632\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00030: val_loss improved from 0.06360 to 0.06358, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00030: val_loss improved from 0.06360 to 0.06358, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00030: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00030: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "Epoch 00030: saving model to model/AE_model/model_2\\KERAS_check_model_epoch30.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 2ms/step - loss: 0.0632 - val_loss: 0.0636\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjoAAAC0CAYAAACdbYZBAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAAANAklEQVR4nO3cW4iVVRsH8Heb5ilRayKl0whFaRlWZkoF6U1pRIQQpEalN1Fd2QGqS5MuLOrCiCgNCowiukolErpIysAMsjx0cqLDmJ8nNDxNur/LiO9r1jP57pnZj7/f7f6z1nL5zvbvHvbTaDabFQBARkMG+gAAAK2i6AAAaSk6AEBaig4AkJaiAwCkNbQv4Y6OjmZnZ2eLjpJHV1dXtXfv3kada9Z591u3bi1mTpw4UcteVVVV119/fW1rlbTi7quqvZ/9HTt2FDNXXnllLXtluf+enp5iJnKvVVVVU6dOPd3jhA32957Msjz77aq3++9T0ens7Kw2b95cz6kSmz59eu1r1nn3kyZNKma6urpq2auqqn59Zlpx91XV3s/+rFmzipnPPvuslr2y3P/u3buLmci9VlX7P//t/Oz3pyzPfrvq7f796goASEvRAQDSUnQAgLQUHQAgLUUHAEhL0QEA0urT18sjXnjhhVDu2muvLWbmzJlzusc5o2zfvj2Ui3wtdteuXcXM5MmTQ/sdPHiwmBk3blxoLf4Sudeqqqrly5e39iBtJvLV8YkTJ9a2X+TnMvqz1O4iYwyiX9uvy4IFC0K5NWvWtPgk7SPy70NVVdV1111XzBw4cOB0j1PkEx0AIC1FBwBIS9EBANJSdACAtBQdACAtRQcASEvRAQDSUnQAgLQUHQAgrdonI0enGR87dqyYmTdvXi3rVFVsEvPq1atDa/XHJMd/IzpdNTLhMzLNdceOHaH9TD1ujegE2ejE7DNFXVOP586dG8pF3hO7u7tP9zhtIXIXR48e7YeT/CU65Ze/TJkyJZRbtWpVi08S4xMdACAtRQcASEvRAQDSUnQAgLQUHQAgLUUHAEhL0QEA0lJ0AIC0ah8YGBnMF7Vu3bra1lqyZEkxs2XLltr2a3eRgVD9PdiLv4sObKTvms1mbWtFBtI1Go3QWnWeq07PPvtsKLdt27YWn+TvIu/769ev74eT5BId1LtgwYIWnyTGJzoAQFqKDgCQlqIDAKSl6AAAaSk6AEBaig4AkJaiAwCkpegAAGnVPjBwsFq9enUxs2rVqn44ycB79NFHi5m5c+cWMyNGjKjjOPwfkYFcy5Yt64eTtI/3338/lFu6dGmLT3LmiQxFrKqqmjRpUotP8nfe9/suMqi33d77faIDAKSl6AAAaSk6AEBaig4AkJaiAwCkpegAAGkpOgBAWooOAJCWogMApNX2k5HnzJkTynV3d7f4JO3j5ZdfLmaazWY/nIR/snjx4mImMvX1TLJ27dpQbuTIkbXsF5kgW1VVdccddxQzkb/vwSw6XXjJkiXFTOS9etGiRaH9IiJTyKuq/aYB/1vz588vZrZt29YPJ6mPT3QAgLQUHQAgLUUHAEhL0QEA0lJ0AIC0FB0AIC1FBwBIS9EBANIa1AMDd+/eXcwcOHAgtNaECRNO9zhnlEajUdtaM2fOLGY2bdpU234ZvP3228XMmjVr+uEk7ePxxx8P5aZMmVLMRAfgRUTee+rcbzDr7z/nwoULi5kzZRBgVGSA4qRJk/rhJPXxiQ4AkJaiAwCkpegAAGkpOgBAWooOAJCWogMApKXoAABpKToAQFqKDgCQ1qCejDxx4sRiptls9sNJcsl8Z9OnTx/oI9Ri8eLFA32EtjN58uRQLjJNffz48cXM2rVrQ/vNmzcvlKN+d99990AfgUHAJzoAQFqKDgCQlqIDAKSl6AAAaSk6AEBaig4AkJaiAwCkpegAAGk1+jI8rtFo/Keqqp9ad5w0Lm02m+fXuaC7D6v97qvK/feB+x9Y3nsGjmd/YP3j/fep6AAAtBO/ugIA0lJ0AIC0FB0AIC1FBwBIS9EBANJSdACAtBQdACAtRQcASEvRAQDSUnQAgLQUHQAgLUUHAEhL0QEA0hral3BHR0ezs7OzRUfJo6urq9q7d2+jzjXrvPuenp5i5o8//ihmDh06FNpvyJBynz7vvPNCa40aNarX11tx91XV3s/+0aNHi5mRI0fWslc73P+pU6eKmWPHjhUz0ed/xIgRxczo0aNDaw0bNqzX1wf7e09m7fDs97fDhw8XM2PGjKllr97uv09Fp7Ozs9q8eXMth8ps+vTpta9Z593/+uuvxczGjRuLmQ0bNoT2O+ecc4qZ+++/P7TWtGnTen29FXdfVe397G/durWYmTp1ai17DeT9nzx5MrTW8ePHi5lvv/22mFm/fn1ov6uuuqqYueGGG0JrXXDBBb2+PmPGjNA6fdHOz35/8t7zvz7++ONiZvbs2bXs1dv9+9UVAJCWogMApKXoAABpKToAQFqKDgCQlqIDAKTVp6+X12nbtm3FTGT+RNSOHTuKmdtuuy201llnnXW6x2mJgwcPhnKRr+Gef/75xczMmTND+40bN66YiXwFN4vIHJdGozyO44EHHgjtN378+GLmpZdeCq01mEW/Xt7V1VXMPPbYY8XMli1bQvtF5hjddNNNobXeeOONXl8/ceJEaJ267d27N5Rbt25dMXPhhRcWM5Fnuqqq6siRI8VM9Kv9w4cPD+UGsz///LOYicw9W758eWi/L7/8spip6+vlvfGJDgCQlqIDAKSl6AAAaSk6AEBaig4AkJaiAwCkpegAAGkpOgBAWooOAJBW7ZORH3rooVBu48aNxcy+ffuKmbFjx4b227lzZzHzyCOPhNZauXJlKNffIhOIq6qqRo8eXcx89NFHxUx0UuhFF11UzAzWadOtEJk8Gplg+sEHH4T2++KLL0K5djd0aOztbMWKFcXM9u3bi5mnnnoqtF9kWvG7774bWivy7NSt2WwW/wxPPvlkaK3LL7+8mJk1a1Yx8/vvv4f2e+2110K5iBtvvLHX15vNZm17tUrkZyTyvL711luh/V555ZVQrtV8ogMApKXoAABpKToAQFqKDgCQlqIDAKSl6AAAaSk6AEBaig4AkFbtAwMXLVoUyj344IPFzIwZM4qZXbt2hfa76667ipnZs2eH1mp3kaFR+/fvL2YOHDgQ2i8yMHAgBqENZpGhXZG/o6qqqosvvvh0jzMolAaybd68ObTOhx9+WMw8/fTTxcytt94a2u+3334rZr7++uvQWqXBmo1GI7ROX/T09FR79uzpNRN9H3744YeLmchQwV9++SW0X3d3dzETPfvNN9/c6+utuPuBcOzYsWLmu+++C611yy23nO5xauFfFwAgLUUHAEhL0QEA0lJ0AIC0FB0AIC1FBwBIS9EBANJSdACAtGofGHjNNdeEcmeffXYxc/z48WImMoyrqqpq1KhRxcz8+fNDa7W7I0eOFDPff/99MXP48OHQfpEBYPzdp59+WsxEhm5WVXnIXDtoNptVT09Pr5nnn38+tNa9995bzCxevLiYiQx1rKqq2r17dzHzzjvvhNZatWpVr6+34u96yJAhxffPzs7O0Fpr1qwpZjZu3FjMnDx5MrRfZBDjtGnTQmudKd57771iJvreE/l3vj/4RAcASEvRAQDSUnQAgLQUHQAgLUUHAEhL0QEA0lJ0AIC0FB0AIC1FBwBIq/bJyGPGjKltrWazWczcfvvtobV+/vnn0z3OgCvdx759+0LrzJo1q5jp6OgoZlauXBnaLzINdenSpaG1Mkz5jXjmmWeKmQ0bNvTDSQaHEydOFH+Gv/rqq9Ba99xzTzFz6NChYuann34K7bdw4cJi5s477wytNXLkyF5fHzKk/v+7Dh06tDr33HN7zTz33HOhtX788cfQfiVHjx4N7dfd3V3MXHrppaG1zhQrVqwoZj7//PN+OEl9fKIDAKSl6AAAaSk6AEBaig4AkJaiAwCkpegAAGkpOgBAWooOAJBW7QMDG41GbWu9/vrrxczVV18dWmv8+PGne5xBLzpMLzJUbNOmTcXMkiVLQvtdccUVxUxkqGBVVdV9990Xyg1mkUGYe/bsKWZKw+OyOXXqVK+vHzx4MLTOE088UczMmDGjmIkObBw9enQx8+qrr4bWasVAwDpMmDChtlxkGOD+/ftD+11yySXFTJ1Dbge7yN2OHTu2mGm3957B+VMDAFADRQcASEvRAQDSUnQAgLQUHQAgLUUHAEhL0QEA0lJ0AIC0FB0AIK3aJyPXadmyZcVMdKJoBqWp09Hpzzt37qzjOPxLhw4dKmYiE3CHDh3UP761Gj58eHXZZZf1mnnzzTdDa7344ovFzOHDh4uZTz75JLTf5MmTi5k6J8q3u8jU3eg04xEjRhQz33zzTWit6BT+weyHH34oZjo6OoqZYcOG1XGcfuMTHQAgLUUHAEhL0QEA0lJ0AIC0FB0AIC1FBwBIS9EBANJSdACAtBrNZjMebjT+U1XVT607ThqXNpvN8+tc0N2H1X73VeX++8D9DyzvPQPHsz+w/vH++1R0AADaiV9dAQBpKToAQFqKDgCQlqIDAKSl6AAAaSk6AEBaig4AkJaiAwCkpegAAGn9F4QJL96uI9XVAAAAAElFTkSuQmCC",
      "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"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x216 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=30)\n",
    "network.plot_latent_dimension(\"Autoencoder without classifier\")\n",
    "network.plot_reco(model_name=\"complete\")\n",
    "network.plot_score(use_latent_classifier=False, model_name=\"Autoencoder without classifier\")\n",
    "network.plot_latent()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\loren\\AppData\\Roaming\\Python\\Python38\\site-packages\\tensorflow_model_optimization\\python\\core\\sparsity\\keras\\pruning_wrapper.py:212: Layer.add_variable (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `layer.add_weight` method instead.\n",
      "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",
      "prune_low_magnitude_dense_1  (None, 16)                1042      \n",
      "_________________________________________________________________\n",
      "encoder_output (Dense)       (None, 2)                 34        \n",
      "_________________________________________________________________\n",
      "prune_low_magnitude_dense_2  (None, 16)                82        \n",
      "_________________________________________________________________\n",
      "dense_3 (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 1/30\n",
      "  1/235 [..............................] - ETA: 0s - loss: 0.2080WARNING:tensorflow:From C:\\Users\\loren\\miniconda3\\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",
      "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: 0.0269s). Check your callbacks.\n",
      "196/235 [========================>.....] - ETA: 0s - loss: 0.1178\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 0.09057, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 0.09057, 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 3ms/step - loss: 0.1135 - val_loss: 0.0906\n",
      "Epoch 2/30\n",
      "232/235 [============================>.] - ETA: 0s - loss: 0.0855\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00002: val_loss improved from 0.09057 to 0.08169, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00002: val_loss improved from 0.09057 to 0.08169, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00002: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00002: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 2ms/step - loss: 0.0855 - val_loss: 0.0817\n",
      "Epoch 3/30\n",
      "210/235 [=========================>....] - ETA: 0s - loss: 0.0799\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00003: val_loss improved from 0.08169 to 0.07809, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00003: val_loss improved from 0.08169 to 0.07809, 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: 0.0797 - val_loss: 0.0781\n",
      "Epoch 4/30\n",
      "214/235 [==========================>...] - ETA: 0s - loss: 0.0768\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00004: val_loss improved from 0.07809 to 0.07494, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00004: val_loss improved from 0.07809 to 0.07494, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00004: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00004: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 2ms/step - loss: 0.0766 - val_loss: 0.0749\n",
      "Epoch 5/30\n",
      "216/235 [==========================>...] - ETA: 0s - loss: 0.0739\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00005: val_loss improved from 0.07494 to 0.07295, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00005: val_loss improved from 0.07494 to 0.07295, 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 2ms/step - loss: 0.0739 - val_loss: 0.0730\n",
      "Epoch 6/30\n",
      "216/235 [==========================>...] - ETA: 0s - loss: 0.0725\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00006: val_loss improved from 0.07295 to 0.07197, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00006: val_loss improved from 0.07295 to 0.07197, 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 2ms/step - loss: 0.0725 - val_loss: 0.0720\n",
      "Epoch 7/30\n",
      "217/235 [==========================>...] - ETA: 0s - loss: 0.0716\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00007: val_loss improved from 0.07197 to 0.07112, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00007: val_loss improved from 0.07197 to 0.07112, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00007: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00007: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 2ms/step - loss: 0.0716 - val_loss: 0.0711\n",
      "Epoch 8/30\n",
      "207/235 [=========================>....] - ETA: 0s - loss: 0.0709\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00008: val_loss improved from 0.07112 to 0.07036, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00008: val_loss improved from 0.07112 to 0.07036, 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 2ms/step - loss: 0.0709 - val_loss: 0.0704\n",
      "Epoch 9/30\n",
      "215/235 [==========================>...] - ETA: 0s - loss: 0.0762\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00009: val_loss did not improve from 0.07036\n",
      "\n",
      "Epoch 00009: val_loss did not improve from 0.07036\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: 0.0763 - val_loss: 0.0767\n",
      "Epoch 10/30\n",
      "215/235 [==========================>...] - ETA: 0s - loss: 0.0747\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00010: val_loss did not improve from 0.07036\n",
      "\n",
      "Epoch 00010: val_loss did not improve from 0.07036\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 2ms/step - loss: 0.0745 - val_loss: 0.0726\n",
      "Epoch 11/30\n",
      "214/235 [==========================>...] - ETA: 0s - loss: 0.0720\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00011: val_loss did not improve from 0.07036\n",
      "\n",
      "Epoch 00011: val_loss did not improve from 0.07036\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: 0.0720 - val_loss: 0.0712\n",
      "Epoch 12/30\n",
      "231/235 [============================>.] - ETA: 0s - loss: 0.0708\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00012: val_loss did not improve from 0.07036\n",
      "\n",
      "Epoch 00012: val_loss did not improve from 0.07036\n",
      "\n",
      "Epoch 00012: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00012: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 2ms/step - loss: 0.0708 - val_loss: 0.0705\n",
      "Epoch 13/30\n",
      "212/235 [==========================>...] - 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 0.07036 to 0.06978, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00013: val_loss improved from 0.07036 to 0.06978, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00013: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00013: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 2ms/step - loss: 0.0701 - val_loss: 0.0698\n",
      "Epoch 14/30\n",
      "216/235 [==========================>...] - 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 0.06978 to 0.06931, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00014: val_loss improved from 0.06978 to 0.06931, 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 2ms/step - loss: 0.0695 - val_loss: 0.0693\n",
      "Epoch 15/30\n",
      "221/235 [===========================>..] - 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 0.06931 to 0.06899, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00015: val_loss improved from 0.06931 to 0.06899, 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 2ms/step - loss: 0.0690 - val_loss: 0.0690\n",
      "Epoch 16/30\n",
      "226/235 [===========================>..] - 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 0.06899 to 0.06862, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00016: val_loss improved from 0.06899 to 0.06862, 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 2ms/step - loss: 0.0686 - val_loss: 0.0686\n",
      "Epoch 17/30\n",
      "214/235 [==========================>...] - ETA: 0s - loss: 0.0683\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00017: val_loss improved from 0.06862 to 0.06826, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00017: val_loss improved from 0.06862 to 0.06826, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00017: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00017: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 2ms/step - loss: 0.0682 - val_loss: 0.0683\n",
      "Epoch 18/30\n",
      "212/235 [==========================>...] - ETA: 0s - loss: 0.0679\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00018: val_loss improved from 0.06826 to 0.06806, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00018: val_loss improved from 0.06826 to 0.06806, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00018: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00018: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 2ms/step - loss: 0.0679 - val_loss: 0.0681\n",
      "Epoch 19/30\n",
      "225/235 [===========================>..] - ETA: 0s - loss: 0.0676\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00019: val_loss improved from 0.06806 to 0.06765, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00019: val_loss improved from 0.06806 to 0.06765, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00019: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00019: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 2ms/step - loss: 0.0676 - val_loss: 0.0676\n",
      "Epoch 20/30\n",
      "212/235 [==========================>...] - ETA: 0s - loss: 0.0674\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00020: val_loss improved from 0.06765 to 0.06746, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00020: val_loss improved from 0.06765 to 0.06746, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00020: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00020: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "Epoch 00020: saving model to model/AE_model/model_2\\KERAS_check_model_epoch20.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 3ms/step - loss: 0.0673 - val_loss: 0.0675\n",
      "Epoch 21/30\n",
      "203/235 [========================>.....] - ETA: 0s - loss: 0.0671\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00021: val_loss improved from 0.06746 to 0.06731, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00021: val_loss improved from 0.06746 to 0.06731, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00021: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00021: 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: 0.0670 - val_loss: 0.0673\n",
      "Epoch 22/30\n",
      "212/235 [==========================>...] - ETA: 0s - loss: 0.0668\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00022: val_loss improved from 0.06731 to 0.06685, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00022: val_loss improved from 0.06731 to 0.06685, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00022: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00022: 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: 0.0668 - val_loss: 0.0669\n",
      "Epoch 23/30\n",
      "209/235 [=========================>....] - ETA: 0s - loss: 0.0666\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00023: val_loss improved from 0.06685 to 0.06675, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00023: val_loss improved from 0.06685 to 0.06675, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00023: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00023: 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: 0.0666 - val_loss: 0.0667\n",
      "Epoch 24/30\n",
      "222/235 [===========================>..] - ETA: 0s - loss: 0.0664\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00024: val_loss improved from 0.06675 to 0.06668, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00024: val_loss improved from 0.06675 to 0.06668, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00024: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00024: 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: 0.0664 - val_loss: 0.0667\n",
      "Epoch 25/30\n",
      "222/235 [===========================>..] - ETA: 0s - loss: 0.0661\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00025: val_loss improved from 0.06668 to 0.06639, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00025: val_loss improved from 0.06668 to 0.06639, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00025: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00025: 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: 0.0662 - val_loss: 0.0664\n",
      "Epoch 26/30\n",
      "218/235 [==========================>...] - ETA: 0s - loss: 0.0660\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00026: val_loss improved from 0.06639 to 0.06634, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00026: val_loss improved from 0.06639 to 0.06634, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00026: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00026: 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: 0.0660 - val_loss: 0.0663\n",
      "Epoch 27/30\n",
      "213/235 [==========================>...] - ETA: 0s - loss: 0.0658\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00027: val_loss improved from 0.06634 to 0.06610, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00027: val_loss improved from 0.06634 to 0.06610, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00027: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00027: 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: 0.0658 - val_loss: 0.0661\n",
      "Epoch 28/30\n",
      "225/235 [===========================>..] - ETA: 0s - loss: 0.0657\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00028: val_loss improved from 0.06610 to 0.06594, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00028: val_loss improved from 0.06610 to 0.06594, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00028: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00028: 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: 0.0657 - val_loss: 0.0659\n",
      "Epoch 29/30\n",
      "211/235 [=========================>....] - ETA: 0s - loss: 0.0656\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00029: val_loss improved from 0.06594 to 0.06586, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00029: val_loss improved from 0.06594 to 0.06586, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00029: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00029: 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: 0.0655 - val_loss: 0.0659\n",
      "Epoch 30/30\n",
      "214/235 [==========================>...] - ETA: 0s - loss: 0.0654\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00030: val_loss improved from 0.06586 to 0.06576, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00030: val_loss improved from 0.06586 to 0.06576, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00030: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00030: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "Epoch 00030: saving model to model/AE_model/model_2\\KERAS_check_model_epoch30.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 3ms/step - loss: 0.0654 - val_loss: 0.0658\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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AAAAASUVORK5CYII=",
      "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()\n",
    "network.plot_latent_dimension(\"Compressed AE without classifier\")\n",
    "network.plot_reco(model_name=\" compressed\")\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": 4,
   "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",
      "fc1 (Dense)                     (None, 32)           2080        encoder_input[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "fc2 (Dense)                     (None, 16)           528         fc1[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "encoder_output (Dense)          (None, 2)            34          fc2[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "fc3 (Dense)                     (None, 16)           48          encoder_output[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "fc3_class (Dense)               (None, 16)           48          encoder_output[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "fc4 (Dense)                     (None, 32)           544         fc3[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "fc4_class (Dense)               (None, 32)           544         fc3_class[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "ecoder_output (Dense)           (None, 64)           2112        fc4[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "classifier_output (Dense)       (None, 10)           330         fc4_class[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 1/30\n",
      "  1/235 [..............................] - ETA: 0s - loss: 0.4398 - ecoder_output_loss: 0.2107 - classifier_output_loss: 2.2910 - ecoder_output_accuracy: 0.0312 - classifier_output_accuracy: 0.0977WARNING:tensorflow:From C:\\Users\\loren\\miniconda3\\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",
      "WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0020s vs `on_train_batch_end` time: 0.0372s). Check your callbacks.\n",
      "213/235 [==========================>...] - ETA: 0s - loss: 0.3152 - ecoder_output_loss: 0.1228 - classifier_output_loss: 1.9239 - ecoder_output_accuracy: 0.0776 - classifier_output_accuracy: 0.2800\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 0.22890, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 0.22890, 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 3ms/step - loss: 0.3081 - ecoder_output_loss: 0.1206 - classifier_output_loss: 1.8746 - ecoder_output_accuracy: 0.0791 - classifier_output_accuracy: 0.3042 - val_loss: 0.2289 - val_ecoder_output_loss: 0.0967 - val_classifier_output_loss: 1.3220 - val_ecoder_output_accuracy: 0.0853 - val_classifier_output_accuracy: 0.6000\n",
      "Epoch 2/30\n",
      "218/235 [==========================>...] - ETA: 0s - loss: 0.2030 - ecoder_output_loss: 0.0933 - classifier_output_loss: 1.0968 - ecoder_output_accuracy: 0.0779 - classifier_output_accuracy: 0.6481\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00002: val_loss improved from 0.22890 to 0.18311, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00002: val_loss improved from 0.22890 to 0.18311, 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: 0.2018 - ecoder_output_loss: 0.0931 - classifier_output_loss: 1.0863 - ecoder_output_accuracy: 0.0775 - classifier_output_accuracy: 0.6505 - val_loss: 0.1831 - val_ecoder_output_loss: 0.0896 - val_classifier_output_loss: 0.9349 - val_ecoder_output_accuracy: 0.0621 - val_classifier_output_accuracy: 0.7013\n",
      "Epoch 3/30\n",
      "218/235 [==========================>...] - ETA: 0s - loss: 0.1749 - ecoder_output_loss: 0.0878 - classifier_output_loss: 0.8709 - ecoder_output_accuracy: 0.0588 - classifier_output_accuracy: 0.7029\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00003: val_loss improved from 0.18311 to 0.16782, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00003: val_loss improved from 0.18311 to 0.16782, 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 [==============================] - 0s 2ms/step - loss: 0.1746 - ecoder_output_loss: 0.0877 - classifier_output_loss: 0.8687 - ecoder_output_accuracy: 0.0585 - classifier_output_accuracy: 0.7031 - val_loss: 0.1678 - val_ecoder_output_loss: 0.0860 - val_classifier_output_loss: 0.8185 - val_ecoder_output_accuracy: 0.0509 - val_classifier_output_accuracy: 0.7304\n",
      "Epoch 4/30\n",
      "213/235 [==========================>...] - ETA: 0s - loss: 0.1632 - ecoder_output_loss: 0.0857 - classifier_output_loss: 0.7749 - ecoder_output_accuracy: 0.0529 - classifier_output_accuracy: 0.7358\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00004: val_loss improved from 0.16782 to 0.15902, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00004: val_loss improved from 0.16782 to 0.15902, 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: 0.1629 - ecoder_output_loss: 0.0856 - classifier_output_loss: 0.7721 - ecoder_output_accuracy: 0.0537 - classifier_output_accuracy: 0.7368 - val_loss: 0.1590 - val_ecoder_output_loss: 0.0850 - val_classifier_output_loss: 0.7402 - val_ecoder_output_accuracy: 0.0499 - val_classifier_output_accuracy: 0.7566\n",
      "Epoch 5/30\n",
      "211/235 [=========================>....] - ETA: 0s - loss: 0.1551 - ecoder_output_loss: 0.0845 - classifier_output_loss: 0.7067 - ecoder_output_accuracy: 0.0549 - classifier_output_accuracy: 0.7650\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00005: val_loss improved from 0.15902 to 0.15067, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00005: val_loss improved from 0.15902 to 0.15067, 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: 0.1549 - ecoder_output_loss: 0.0844 - classifier_output_loss: 0.7054 - ecoder_output_accuracy: 0.0550 - classifier_output_accuracy: 0.7661 - val_loss: 0.1507 - val_ecoder_output_loss: 0.0827 - val_classifier_output_loss: 0.6793 - val_ecoder_output_accuracy: 0.0558 - val_classifier_output_accuracy: 0.7866\n",
      "Epoch 6/30\n",
      "207/235 [=========================>....] - ETA: 0s - loss: 0.1476 - ecoder_output_loss: 0.0825 - classifier_output_loss: 0.6508 - ecoder_output_accuracy: 0.0563 - classifier_output_accuracy: 0.7900\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00006: val_loss improved from 0.15067 to 0.14500, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00006: val_loss improved from 0.15067 to 0.14500, 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: 0.1477 - ecoder_output_loss: 0.0825 - classifier_output_loss: 0.6515 - ecoder_output_accuracy: 0.0576 - classifier_output_accuracy: 0.7901 - val_loss: 0.1450 - val_ecoder_output_loss: 0.0819 - val_classifier_output_loss: 0.6311 - val_ecoder_output_accuracy: 0.0708 - val_classifier_output_accuracy: 0.8025\n",
      "Epoch 7/30\n",
      "215/235 [==========================>...] - ETA: 0s - loss: 0.1424 - ecoder_output_loss: 0.0814 - classifier_output_loss: 0.6099 - ecoder_output_accuracy: 0.0708 - classifier_output_accuracy: 0.8072\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00007: val_loss improved from 0.14500 to 0.13990, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00007: val_loss improved from 0.14500 to 0.13990, 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: 0.1422 - ecoder_output_loss: 0.0814 - classifier_output_loss: 0.6088 - ecoder_output_accuracy: 0.0709 - classifier_output_accuracy: 0.8071 - val_loss: 0.1399 - val_ecoder_output_loss: 0.0802 - val_classifier_output_loss: 0.5969 - val_ecoder_output_accuracy: 0.0702 - val_classifier_output_accuracy: 0.8190\n",
      "Epoch 8/30\n",
      "217/235 [==========================>...] - ETA: 0s - loss: 0.1372 - ecoder_output_loss: 0.0798 - classifier_output_loss: 0.5746 - ecoder_output_accuracy: 0.0744 - classifier_output_accuracy: 0.8235\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00008: val_loss improved from 0.13990 to 0.13632, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00008: val_loss improved from 0.13990 to 0.13632, 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: 0.1373 - ecoder_output_loss: 0.0798 - classifier_output_loss: 0.5747 - ecoder_output_accuracy: 0.0744 - classifier_output_accuracy: 0.8238 - val_loss: 0.1363 - val_ecoder_output_loss: 0.0790 - val_classifier_output_loss: 0.5733 - val_ecoder_output_accuracy: 0.0658 - val_classifier_output_accuracy: 0.8290\n",
      "Epoch 9/30\n",
      "214/235 [==========================>...] - ETA: 0s - loss: 0.1334 - ecoder_output_loss: 0.0790 - classifier_output_loss: 0.5443 - ecoder_output_accuracy: 0.0720 - classifier_output_accuracy: 0.8382\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00009: val_loss improved from 0.13632 to 0.13302, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00009: val_loss improved from 0.13632 to 0.13302, 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: 0.1331 - ecoder_output_loss: 0.0789 - classifier_output_loss: 0.5419 - ecoder_output_accuracy: 0.0715 - classifier_output_accuracy: 0.8390 - val_loss: 0.1330 - val_ecoder_output_loss: 0.0784 - val_classifier_output_loss: 0.5464 - val_ecoder_output_accuracy: 0.0714 - val_classifier_output_accuracy: 0.8393\n",
      "Epoch 10/30\n",
      "217/235 [==========================>...] - ETA: 0s - loss: 0.1300 - ecoder_output_loss: 0.0783 - classifier_output_loss: 0.5168 - ecoder_output_accuracy: 0.0708 - classifier_output_accuracy: 0.8473\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00010: val_loss improved from 0.13302 to 0.13034, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00010: val_loss improved from 0.13302 to 0.13034, 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: 0.1300 - ecoder_output_loss: 0.0783 - classifier_output_loss: 0.5165 - ecoder_output_accuracy: 0.0705 - classifier_output_accuracy: 0.8476 - val_loss: 0.1303 - val_ecoder_output_loss: 0.0778 - val_classifier_output_loss: 0.5258 - val_ecoder_output_accuracy: 0.0677 - val_classifier_output_accuracy: 0.8498\n",
      "Epoch 11/30\n",
      "216/235 [==========================>...] - ETA: 0s - loss: 0.1278 - ecoder_output_loss: 0.0781 - classifier_output_loss: 0.4976 - ecoder_output_accuracy: 0.0688 - classifier_output_accuracy: 0.8564\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00011: val_loss improved from 0.13034 to 0.12754, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00011: val_loss improved from 0.13034 to 0.12754, 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: 0.1276 - ecoder_output_loss: 0.0781 - classifier_output_loss: 0.4957 - ecoder_output_accuracy: 0.0694 - classifier_output_accuracy: 0.8566 - val_loss: 0.1275 - val_ecoder_output_loss: 0.0775 - val_classifier_output_loss: 0.5006 - val_ecoder_output_accuracy: 0.0700 - val_classifier_output_accuracy: 0.8587\n",
      "Epoch 12/30\n",
      "215/235 [==========================>...] - ETA: 0s - loss: 0.1255 - ecoder_output_loss: 0.0778 - classifier_output_loss: 0.4770 - ecoder_output_accuracy: 0.0661 - classifier_output_accuracy: 0.8636\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00012: val_loss improved from 0.12754 to 0.12579, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00012: val_loss improved from 0.12754 to 0.12579, 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: 0.1255 - ecoder_output_loss: 0.0778 - classifier_output_loss: 0.4771 - ecoder_output_accuracy: 0.0657 - classifier_output_accuracy: 0.8634 - val_loss: 0.1258 - val_ecoder_output_loss: 0.0774 - val_classifier_output_loss: 0.4842 - val_ecoder_output_accuracy: 0.0626 - val_classifier_output_accuracy: 0.8671\n",
      "Epoch 13/30\n",
      "214/235 [==========================>...] - ETA: 0s - loss: 0.1235 - ecoder_output_loss: 0.0776 - classifier_output_loss: 0.4590 - ecoder_output_accuracy: 0.0633 - classifier_output_accuracy: 0.8702\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00013: val_loss improved from 0.12579 to 0.12505, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00013: val_loss improved from 0.12579 to 0.12505, 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: 0.1238 - ecoder_output_loss: 0.0776 - classifier_output_loss: 0.4620 - ecoder_output_accuracy: 0.0638 - classifier_output_accuracy: 0.8688 - val_loss: 0.1251 - val_ecoder_output_loss: 0.0771 - val_classifier_output_loss: 0.4797 - val_ecoder_output_accuracy: 0.0518 - val_classifier_output_accuracy: 0.8653\n",
      "Epoch 14/30\n",
      "216/235 [==========================>...] - ETA: 0s - loss: 0.1223 - ecoder_output_loss: 0.0774 - classifier_output_loss: 0.4497 - ecoder_output_accuracy: 0.0606 - classifier_output_accuracy: 0.8728\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00014: val_loss improved from 0.12505 to 0.12324, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00014: val_loss improved from 0.12505 to 0.12324, 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: 0.1223 - ecoder_output_loss: 0.0774 - classifier_output_loss: 0.4500 - ecoder_output_accuracy: 0.0606 - classifier_output_accuracy: 0.8727 - val_loss: 0.1232 - val_ecoder_output_loss: 0.0768 - val_classifier_output_loss: 0.4644 - val_ecoder_output_accuracy: 0.0685 - val_classifier_output_accuracy: 0.8715\n",
      "Epoch 15/30\n",
      "215/235 [==========================>...] - ETA: 0s - loss: 0.1208 - ecoder_output_loss: 0.0770 - classifier_output_loss: 0.4381 - ecoder_output_accuracy: 0.0594 - classifier_output_accuracy: 0.8768\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00015: val_loss improved from 0.12324 to 0.12190, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00015: val_loss improved from 0.12324 to 0.12190, 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: 0.1207 - ecoder_output_loss: 0.0771 - classifier_output_loss: 0.4369 - ecoder_output_accuracy: 0.0593 - classifier_output_accuracy: 0.8771 - val_loss: 0.1219 - val_ecoder_output_loss: 0.0766 - val_classifier_output_loss: 0.4530 - val_ecoder_output_accuracy: 0.0526 - val_classifier_output_accuracy: 0.8746\n",
      "Epoch 16/30\n",
      "209/235 [=========================>....] - ETA: 0s - loss: 0.1194 - ecoder_output_loss: 0.0769 - classifier_output_loss: 0.4243 - ecoder_output_accuracy: 0.0582 - classifier_output_accuracy: 0.8822\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00016: val_loss improved from 0.12190 to 0.12079, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00016: val_loss improved from 0.12190 to 0.12079, 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: 0.1195 - ecoder_output_loss: 0.0769 - classifier_output_loss: 0.4251 - ecoder_output_accuracy: 0.0582 - classifier_output_accuracy: 0.8821 - val_loss: 0.1208 - val_ecoder_output_loss: 0.0766 - val_classifier_output_loss: 0.4419 - val_ecoder_output_accuracy: 0.0508 - val_classifier_output_accuracy: 0.8780\n",
      "Epoch 17/30\n",
      "211/235 [=========================>....] - ETA: 0s - loss: 0.1187 - ecoder_output_loss: 0.0768 - classifier_output_loss: 0.4186 - ecoder_output_accuracy: 0.0579 - classifier_output_accuracy: 0.8831\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00017: val_loss improved from 0.12079 to 0.11984, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00017: val_loss improved from 0.12079 to 0.11984, 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: 0.1185 - ecoder_output_loss: 0.0768 - classifier_output_loss: 0.4161 - ecoder_output_accuracy: 0.0578 - classifier_output_accuracy: 0.8841 - val_loss: 0.1198 - val_ecoder_output_loss: 0.0764 - val_classifier_output_loss: 0.4342 - val_ecoder_output_accuracy: 0.0582 - val_classifier_output_accuracy: 0.8826\n",
      "Epoch 18/30\n",
      "215/235 [==========================>...] - ETA: 0s - loss: 0.1177 - ecoder_output_loss: 0.0768 - classifier_output_loss: 0.4096 - ecoder_output_accuracy: 0.0582 - classifier_output_accuracy: 0.8864\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00018: val_loss improved from 0.11984 to 0.11882, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00018: val_loss improved from 0.11984 to 0.11882, 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: 0.1176 - ecoder_output_loss: 0.0768 - classifier_output_loss: 0.4086 - ecoder_output_accuracy: 0.0582 - classifier_output_accuracy: 0.8867 - val_loss: 0.1188 - val_ecoder_output_loss: 0.0763 - val_classifier_output_loss: 0.4251 - val_ecoder_output_accuracy: 0.0554 - val_classifier_output_accuracy: 0.8837\n",
      "Epoch 19/30\n",
      "207/235 [=========================>....] - ETA: 0s - loss: 0.1167 - ecoder_output_loss: 0.0766 - classifier_output_loss: 0.4011 - ecoder_output_accuracy: 0.0586 - classifier_output_accuracy: 0.8890\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00019: val_loss improved from 0.11882 to 0.11806, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00019: val_loss improved from 0.11882 to 0.11806, 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: 0.1167 - ecoder_output_loss: 0.0766 - classifier_output_loss: 0.4007 - ecoder_output_accuracy: 0.0583 - classifier_output_accuracy: 0.8892 - val_loss: 0.1181 - val_ecoder_output_loss: 0.0759 - val_classifier_output_loss: 0.4220 - val_ecoder_output_accuracy: 0.0563 - val_classifier_output_accuracy: 0.8854\n",
      "Epoch 20/30\n",
      "216/235 [==========================>...] - ETA: 0s - loss: 0.1160 - ecoder_output_loss: 0.0764 - classifier_output_loss: 0.3959 - ecoder_output_accuracy: 0.0583 - classifier_output_accuracy: 0.8911\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00020: val_loss improved from 0.11806 to 0.11732, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00020: val_loss improved from 0.11806 to 0.11732, 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 [==============================] - 0s 2ms/step - loss: 0.1156 - ecoder_output_loss: 0.0764 - classifier_output_loss: 0.3927 - ecoder_output_accuracy: 0.0582 - classifier_output_accuracy: 0.8918 - val_loss: 0.1173 - val_ecoder_output_loss: 0.0759 - val_classifier_output_loss: 0.4137 - val_ecoder_output_accuracy: 0.0591 - val_classifier_output_accuracy: 0.8880\n",
      "Epoch 21/30\n",
      "203/235 [========================>.....] - ETA: 0s - loss: 0.1144 - ecoder_output_loss: 0.0762 - classifier_output_loss: 0.3820 - ecoder_output_accuracy: 0.0585 - classifier_output_accuracy: 0.8947\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00021: val_loss improved from 0.11732 to 0.11656, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00021: val_loss improved from 0.11732 to 0.11656, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00021: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00021: 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: 0.1147 - ecoder_output_loss: 0.0762 - classifier_output_loss: 0.3850 - ecoder_output_accuracy: 0.0582 - classifier_output_accuracy: 0.8941 - val_loss: 0.1166 - val_ecoder_output_loss: 0.0758 - val_classifier_output_loss: 0.4075 - val_ecoder_output_accuracy: 0.0511 - val_classifier_output_accuracy: 0.8913\n",
      "Epoch 22/30\n",
      "216/235 [==========================>...] - ETA: 0s - loss: 0.1139 - ecoder_output_loss: 0.0761 - classifier_output_loss: 0.3777 - ecoder_output_accuracy: 0.0598 - classifier_output_accuracy: 0.8969\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00022: val_loss did not improve from 0.11656\n",
      "\n",
      "Epoch 00022: val_loss did not improve from 0.11656\n",
      "\n",
      "Epoch 00022: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00022: 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: 0.1140 - ecoder_output_loss: 0.0762 - classifier_output_loss: 0.3783 - ecoder_output_accuracy: 0.0599 - classifier_output_accuracy: 0.8969 - val_loss: 0.1169 - val_ecoder_output_loss: 0.0756 - val_classifier_output_loss: 0.4128 - val_ecoder_output_accuracy: 0.0538 - val_classifier_output_accuracy: 0.8878\n",
      "Epoch 23/30\n",
      "205/235 [=========================>....] - ETA: 0s - loss: 0.1132 - ecoder_output_loss: 0.0760 - classifier_output_loss: 0.3711 - ecoder_output_accuracy: 0.0598 - classifier_output_accuracy: 0.8977\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00023: val_loss improved from 0.11656 to 0.11580, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00023: val_loss improved from 0.11656 to 0.11580, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00023: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00023: 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: 0.1133 - ecoder_output_loss: 0.0760 - classifier_output_loss: 0.3725 - ecoder_output_accuracy: 0.0598 - classifier_output_accuracy: 0.8974 - val_loss: 0.1158 - val_ecoder_output_loss: 0.0755 - val_classifier_output_loss: 0.4026 - val_ecoder_output_accuracy: 0.0590 - val_classifier_output_accuracy: 0.8910\n",
      "Epoch 24/30\n",
      "223/235 [===========================>..] - ETA: 0s - loss: 0.1125 - ecoder_output_loss: 0.0760 - classifier_output_loss: 0.3653 - ecoder_output_accuracy: 0.0576 - classifier_output_accuracy: 0.8999\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00024: val_loss improved from 0.11580 to 0.11522, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00024: val_loss improved from 0.11580 to 0.11522, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00024: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00024: 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: 0.1127 - ecoder_output_loss: 0.0760 - classifier_output_loss: 0.3668 - ecoder_output_accuracy: 0.0581 - classifier_output_accuracy: 0.8997 - val_loss: 0.1152 - val_ecoder_output_loss: 0.0755 - val_classifier_output_loss: 0.3976 - val_ecoder_output_accuracy: 0.0552 - val_classifier_output_accuracy: 0.8945\n",
      "Epoch 25/30\n",
      "199/235 [========================>.....] - ETA: 0s - loss: 0.1121 - ecoder_output_loss: 0.0759 - classifier_output_loss: 0.3614 - ecoder_output_accuracy: 0.0597 - classifier_output_accuracy: 0.9012\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00025: val_loss improved from 0.11522 to 0.11472, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00025: val_loss improved from 0.11522 to 0.11472, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00025: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00025: 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: 0.1121 - ecoder_output_loss: 0.0759 - classifier_output_loss: 0.3618 - ecoder_output_accuracy: 0.0598 - classifier_output_accuracy: 0.9011 - val_loss: 0.1147 - val_ecoder_output_loss: 0.0754 - val_classifier_output_loss: 0.3930 - val_ecoder_output_accuracy: 0.0529 - val_classifier_output_accuracy: 0.8950\n",
      "Epoch 26/30\n",
      "206/235 [=========================>....] - ETA: 0s - loss: 0.1115 - ecoder_output_loss: 0.0759 - classifier_output_loss: 0.3564 - ecoder_output_accuracy: 0.0595 - classifier_output_accuracy: 0.9023\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00026: val_loss improved from 0.11472 to 0.11452, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00026: val_loss improved from 0.11472 to 0.11452, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00026: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00026: 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: 0.1115 - ecoder_output_loss: 0.0759 - classifier_output_loss: 0.3560 - ecoder_output_accuracy: 0.0593 - classifier_output_accuracy: 0.9024 - val_loss: 0.1145 - val_ecoder_output_loss: 0.0753 - val_classifier_output_loss: 0.3924 - val_ecoder_output_accuracy: 0.0600 - val_classifier_output_accuracy: 0.8947\n",
      "Epoch 27/30\n",
      "206/235 [=========================>....] - ETA: 0s - loss: 0.1110 - ecoder_output_loss: 0.0758 - classifier_output_loss: 0.3517 - ecoder_output_accuracy: 0.0596 - classifier_output_accuracy: 0.9051\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00027: val_loss improved from 0.11452 to 0.11403, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00027: val_loss improved from 0.11452 to 0.11403, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00027: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00027: 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: 0.1110 - ecoder_output_loss: 0.0758 - classifier_output_loss: 0.3523 - ecoder_output_accuracy: 0.0595 - classifier_output_accuracy: 0.9045 - val_loss: 0.1140 - val_ecoder_output_loss: 0.0753 - val_classifier_output_loss: 0.3877 - val_ecoder_output_accuracy: 0.0576 - val_classifier_output_accuracy: 0.8977\n",
      "Epoch 28/30\n",
      "212/235 [==========================>...] - ETA: 0s - loss: 0.1107 - ecoder_output_loss: 0.0757 - classifier_output_loss: 0.3496 - ecoder_output_accuracy: 0.0597 - classifier_output_accuracy: 0.9042\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00028: val_loss improved from 0.11403 to 0.11357, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00028: val_loss improved from 0.11403 to 0.11357, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00028: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00028: 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: 0.1107 - ecoder_output_loss: 0.0757 - classifier_output_loss: 0.3500 - ecoder_output_accuracy: 0.0594 - classifier_output_accuracy: 0.9041 - val_loss: 0.1136 - val_ecoder_output_loss: 0.0752 - val_classifier_output_loss: 0.3832 - val_ecoder_output_accuracy: 0.0535 - val_classifier_output_accuracy: 0.8993\n",
      "Epoch 29/30\n",
      "210/235 [=========================>....] - ETA: 0s - loss: 0.1103 - ecoder_output_loss: 0.0757 - classifier_output_loss: 0.3458 - ecoder_output_accuracy: 0.0594 - classifier_output_accuracy: 0.9052\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00029: val_loss did not improve from 0.11357\n",
      "\n",
      "Epoch 00029: val_loss did not improve from 0.11357\n",
      "\n",
      "Epoch 00029: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00029: 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: 0.1102 - ecoder_output_loss: 0.0757 - classifier_output_loss: 0.3448 - ecoder_output_accuracy: 0.0594 - classifier_output_accuracy: 0.9057 - val_loss: 0.1137 - val_ecoder_output_loss: 0.0752 - val_classifier_output_loss: 0.3846 - val_ecoder_output_accuracy: 0.0556 - val_classifier_output_accuracy: 0.8978\n",
      "Epoch 30/30\n",
      "209/235 [=========================>....] - ETA: 0s - loss: 0.1097 - ecoder_output_loss: 0.0756 - classifier_output_loss: 0.3412 - ecoder_output_accuracy: 0.0604 - classifier_output_accuracy: 0.9072\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00030: val_loss improved from 0.11357 to 0.11345, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00030: val_loss improved from 0.11357 to 0.11345, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00030: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00030: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "Epoch 00030: saving model to model/AE_model/model_2\\KERAS_check_model_epoch30.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 0s 2ms/step - loss: 0.1099 - ecoder_output_loss: 0.0757 - classifier_output_loss: 0.3420 - ecoder_output_accuracy: 0.0599 - classifier_output_accuracy: 0.9070 - val_loss: 0.1135 - val_ecoder_output_loss: 0.0752 - val_classifier_output_loss: 0.3829 - val_ecoder_output_accuracy: 0.0615 - val_classifier_output_accuracy: 0.8992\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "network_with_classifier = Autoencoder(data_zoom, pruned=False)\n",
    "network_with_classifier.build_encoder()\n",
    "network_with_classifier.build_decoder()\n",
    "network_with_classifier.build_classifier()\n",
    "network_with_classifier.build_autoencoder(use_latent_classifier=True)\n",
    "network_with_classifier.fit_data( batch_size=256,epochs=30, 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": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- 0.1650536060333252 seconds ---\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "\n",
    "img = data_zoom.x_test[0].reshape(1,-1)\n",
    "start_time = time.time()\n",
    "network_with_classifier.autoencoder.predict(img)\n",
    "print(\"--- %s seconds ---\" % (time.time() - start_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\loren\\AppData\\Roaming\\Python\\Python38\\site-packages\\tensorflow_model_optimization\\python\\core\\sparsity\\keras\\pruning_wrapper.py:212: Layer.add_variable (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `layer.add_weight` method instead.\n",
      "Model: \"functional_3\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "encoder_input (InputLayer)      [(None, 64)]         0                                            \n",
      "__________________________________________________________________________________________________\n",
      "fc1 (Dense)                     (None, 32)           2080        encoder_input[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "prune_low_magnitude_fc2_prun (P (None, 16)           1042        fc1[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "encoder_output (Dense)          (None, 2)            34          prune_low_magnitude_fc2_prun[0][0\n",
      "__________________________________________________________________________________________________\n",
      "prune_low_magnitude_fc3_pruned  (None, 16)           82          encoder_output[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "prune_low_magnitude_fc3_pruncla (None, 16)           82          encoder_output[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "fc4 (Dense)                     (None, 32)           544         prune_low_magnitude_fc3_pruned[0]\n",
      "__________________________________________________________________________________________________\n",
      "fc4_class (Dense)               (None, 32)           544         prune_low_magnitude_fc3_prunclass\n",
      "__________________________________________________________________________________________________\n",
      "ecoder_output (Dense)           (None, 64)           2112        fc4[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "classifier_output (Dense)       (None, 10)           330         fc4_class[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 1/30\n",
      "  2/235 [..............................] - ETA: 1:50 - loss: 0.4391 - ecoder_output_loss: 0.2088 - classifier_output_loss: 2.3027 - ecoder_output_accuracy: 0.0098 - classifier_output_accuracy: 0.0684WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0010s vs `on_train_batch_end` time: 0.9442s). Check your callbacks.\n",
      "225/235 [===========================>..] - ETA: 0s - loss: 0.3449 - ecoder_output_loss: 0.1198 - classifier_output_loss: 2.2512 - ecoder_output_accuracy: 0.0218 - classifier_output_accuracy: 0.1119\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 0.29975, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 0.29975, 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: 0.3433 - ecoder_output_loss: 0.1189 - classifier_output_loss: 2.2435 - ecoder_output_accuracy: 0.0218 - classifier_output_accuracy: 0.1134 - val_loss: 0.2998 - val_ecoder_output_loss: 0.0972 - val_classifier_output_loss: 2.0253 - val_ecoder_output_accuracy: 0.0265 - val_classifier_output_accuracy: 0.1550\n",
      "Epoch 2/30\n",
      "232/235 [============================>.] - ETA: 0s - loss: 0.2597 - ecoder_output_loss: 0.0921 - classifier_output_loss: 1.6751 - ecoder_output_accuracy: 0.0689 - classifier_output_accuracy: 0.3465\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00002: val_loss improved from 0.29975 to 0.22307, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00002: val_loss improved from 0.29975 to 0.22307, 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: 0.2593 - ecoder_output_loss: 0.0921 - classifier_output_loss: 1.6717 - ecoder_output_accuracy: 0.0690 - classifier_output_accuracy: 0.3478 - val_loss: 0.2231 - val_ecoder_output_loss: 0.0870 - val_classifier_output_loss: 1.3602 - val_ecoder_output_accuracy: 0.0491 - val_classifier_output_accuracy: 0.4677\n",
      "Epoch 3/30\n",
      "210/235 [=========================>....] - ETA: 0s - loss: 0.1974 - ecoder_output_loss: 0.0852 - classifier_output_loss: 1.1225 - ecoder_output_accuracy: 0.0541 - classifier_output_accuracy: 0.5996\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00003: val_loss improved from 0.22307 to 0.17089, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00003: val_loss improved from 0.22307 to 0.17089, 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: 0.1950 - ecoder_output_loss: 0.0850 - classifier_output_loss: 1.1005 - ecoder_output_accuracy: 0.0551 - classifier_output_accuracy: 0.6101 - val_loss: 0.1709 - val_ecoder_output_loss: 0.0824 - val_classifier_output_loss: 0.8846 - val_ecoder_output_accuracy: 0.0592 - val_classifier_output_accuracy: 0.7156\n",
      "Epoch 4/30\n",
      "219/235 [==========================>...] - ETA: 0s - loss: 0.1610 - ecoder_output_loss: 0.0817 - classifier_output_loss: 0.7928 - ecoder_output_accuracy: 0.0551 - classifier_output_accuracy: 0.7489\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00004: val_loss improved from 0.17089 to 0.15376, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00004: val_loss improved from 0.17089 to 0.15376, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00004: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00004: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 2ms/step - loss: 0.1604 - ecoder_output_loss: 0.0816 - classifier_output_loss: 0.7875 - ecoder_output_accuracy: 0.0552 - classifier_output_accuracy: 0.7509 - val_loss: 0.1538 - val_ecoder_output_loss: 0.0807 - val_classifier_output_loss: 0.7302 - val_ecoder_output_accuracy: 0.0428 - val_classifier_output_accuracy: 0.7895\n",
      "Epoch 5/30\n",
      "216/235 [==========================>...] - ETA: 0s - loss: 0.1477 - ecoder_output_loss: 0.0807 - classifier_output_loss: 0.6695 - ecoder_output_accuracy: 0.0495 - classifier_output_accuracy: 0.8011\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00005: val_loss improved from 0.15376 to 0.14330, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00005: val_loss improved from 0.15376 to 0.14330, 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 2ms/step - loss: 0.1475 - ecoder_output_loss: 0.0807 - classifier_output_loss: 0.6677 - ecoder_output_accuracy: 0.0502 - classifier_output_accuracy: 0.8011 - val_loss: 0.1433 - val_ecoder_output_loss: 0.0803 - val_classifier_output_loss: 0.6303 - val_ecoder_output_accuracy: 0.0449 - val_classifier_output_accuracy: 0.8260\n",
      "Epoch 6/30\n",
      "213/235 [==========================>...] - ETA: 0s - loss: 0.1407 - ecoder_output_loss: 0.0802 - classifier_output_loss: 0.6048 - ecoder_output_accuracy: 0.0516 - classifier_output_accuracy: 0.8246\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00006: val_loss improved from 0.14330 to 0.13781, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00006: val_loss improved from 0.14330 to 0.13781, 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 2ms/step - loss: 0.1403 - ecoder_output_loss: 0.0801 - classifier_output_loss: 0.6021 - ecoder_output_accuracy: 0.0513 - classifier_output_accuracy: 0.8253 - val_loss: 0.1378 - val_ecoder_output_loss: 0.0796 - val_classifier_output_loss: 0.5823 - val_ecoder_output_accuracy: 0.0432 - val_classifier_output_accuracy: 0.8416\n",
      "Epoch 7/30\n",
      "224/235 [===========================>..] - ETA: 0s - loss: 0.1358 - ecoder_output_loss: 0.0796 - classifier_output_loss: 0.5621 - ecoder_output_accuracy: 0.0496 - classifier_output_accuracy: 0.8393\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00007: val_loss improved from 0.13781 to 0.13407, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00007: val_loss improved from 0.13781 to 0.13407, 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: 0.1357 - ecoder_output_loss: 0.0796 - classifier_output_loss: 0.5609 - ecoder_output_accuracy: 0.0501 - classifier_output_accuracy: 0.8395 - val_loss: 0.1341 - val_ecoder_output_loss: 0.0790 - val_classifier_output_loss: 0.5505 - val_ecoder_output_accuracy: 0.0569 - val_classifier_output_accuracy: 0.8532\n",
      "Epoch 8/30\n",
      "219/235 [==========================>...] - ETA: 0s - loss: 0.1319 - ecoder_output_loss: 0.0789 - classifier_output_loss: 0.5305 - ecoder_output_accuracy: 0.0542 - classifier_output_accuracy: 0.8497\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00008: val_loss improved from 0.13407 to 0.13068, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00008: val_loss improved from 0.13407 to 0.13068, 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 2ms/step - loss: 0.1319 - ecoder_output_loss: 0.0789 - classifier_output_loss: 0.5304 - ecoder_output_accuracy: 0.0547 - classifier_output_accuracy: 0.8496 - val_loss: 0.1307 - val_ecoder_output_loss: 0.0781 - val_classifier_output_loss: 0.5258 - val_ecoder_output_accuracy: 0.0528 - val_classifier_output_accuracy: 0.8625\n",
      "Epoch 9/30\n",
      "219/235 [==========================>...] - ETA: 0s - loss: 0.1688 - ecoder_output_loss: 0.0867 - classifier_output_loss: 0.8210 - ecoder_output_accuracy: 0.0538 - classifier_output_accuracy: 0.7527\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00009: val_loss did not improve from 0.13068\n",
      "\n",
      "Epoch 00009: val_loss did not improve from 0.13068\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: 0.1696 - ecoder_output_loss: 0.0870 - classifier_output_loss: 0.8254 - ecoder_output_accuracy: 0.0523 - classifier_output_accuracy: 0.7532 - val_loss: 0.1789 - val_ecoder_output_loss: 0.0913 - val_classifier_output_loss: 0.8760 - val_ecoder_output_accuracy: 0.0251 - val_classifier_output_accuracy: 0.7578\n",
      "Epoch 10/30\n",
      "224/235 [===========================>..] - ETA: 0s - loss: 0.1652 - ecoder_output_loss: 0.0895 - classifier_output_loss: 0.7563 - ecoder_output_accuracy: 0.0258 - classifier_output_accuracy: 0.7838\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00010: val_loss did not improve from 0.13068\n",
      "\n",
      "Epoch 00010: val_loss did not improve from 0.13068\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: 0.1647 - ecoder_output_loss: 0.0895 - classifier_output_loss: 0.7522 - ecoder_output_accuracy: 0.0259 - classifier_output_accuracy: 0.7848 - val_loss: 0.1548 - val_ecoder_output_loss: 0.0878 - val_classifier_output_loss: 0.6702 - val_ecoder_output_accuracy: 0.0241 - val_classifier_output_accuracy: 0.8160\n",
      "Epoch 11/30\n",
      "224/235 [===========================>..] - ETA: 0s - loss: 0.1508 - ecoder_output_loss: 0.0879 - classifier_output_loss: 0.6295 - ecoder_output_accuracy: 0.0272 - classifier_output_accuracy: 0.8188\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00011: val_loss did not improve from 0.13068\n",
      "\n",
      "Epoch 00011: val_loss did not improve from 0.13068\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: 0.1506 - ecoder_output_loss: 0.0879 - classifier_output_loss: 0.6274 - ecoder_output_accuracy: 0.0273 - classifier_output_accuracy: 0.8191 - val_loss: 0.1474 - val_ecoder_output_loss: 0.0869 - val_classifier_output_loss: 0.6047 - val_ecoder_output_accuracy: 0.0277 - val_classifier_output_accuracy: 0.8367\n",
      "Epoch 12/30\n",
      "223/235 [===========================>..] - ETA: 0s - loss: 0.1450 - ecoder_output_loss: 0.0872 - classifier_output_loss: 0.5784 - ecoder_output_accuracy: 0.0296 - classifier_output_accuracy: 0.8343\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00012: val_loss did not improve from 0.13068\n",
      "\n",
      "Epoch 00012: val_loss did not improve from 0.13068\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: 0.1450 - ecoder_output_loss: 0.0872 - classifier_output_loss: 0.5787 - ecoder_output_accuracy: 0.0297 - classifier_output_accuracy: 0.8344 - val_loss: 0.1432 - val_ecoder_output_loss: 0.0864 - val_classifier_output_loss: 0.5684 - val_ecoder_output_accuracy: 0.0299 - val_classifier_output_accuracy: 0.8462\n",
      "Epoch 13/30\n",
      "229/235 [============================>.] - ETA: 0s - loss: 0.1417 - ecoder_output_loss: 0.0867 - classifier_output_loss: 0.5497 - ecoder_output_accuracy: 0.0310 - classifier_output_accuracy: 0.8436\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00013: val_loss did not improve from 0.13068\n",
      "\n",
      "Epoch 00013: val_loss did not improve from 0.13068\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: 0.1417 - ecoder_output_loss: 0.0867 - classifier_output_loss: 0.5499 - ecoder_output_accuracy: 0.0311 - classifier_output_accuracy: 0.8435 - val_loss: 0.1405 - val_ecoder_output_loss: 0.0858 - val_classifier_output_loss: 0.5473 - val_ecoder_output_accuracy: 0.0278 - val_classifier_output_accuracy: 0.8545\n",
      "Epoch 14/30\n",
      "226/235 [===========================>..] - ETA: 0s - loss: 0.1394 - ecoder_output_loss: 0.0864 - classifier_output_loss: 0.5297 - ecoder_output_accuracy: 0.0317 - classifier_output_accuracy: 0.8494\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00014: val_loss did not improve from 0.13068\n",
      "\n",
      "Epoch 00014: val_loss did not improve from 0.13068\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: 0.1393 - ecoder_output_loss: 0.0864 - classifier_output_loss: 0.5295 - ecoder_output_accuracy: 0.0317 - classifier_output_accuracy: 0.8494 - val_loss: 0.1388 - val_ecoder_output_loss: 0.0856 - val_classifier_output_loss: 0.5319 - val_ecoder_output_accuracy: 0.0283 - val_classifier_output_accuracy: 0.8591\n",
      "Epoch 15/30\n",
      "212/235 [==========================>...] - ETA: 0s - loss: 0.1378 - ecoder_output_loss: 0.0862 - classifier_output_loss: 0.5155 - ecoder_output_accuracy: 0.0328 - classifier_output_accuracy: 0.8550\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00015: val_loss did not improve from 0.13068\n",
      "\n",
      "Epoch 00015: val_loss did not improve from 0.13068\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: 0.1376 - ecoder_output_loss: 0.0862 - classifier_output_loss: 0.5139 - ecoder_output_accuracy: 0.0328 - classifier_output_accuracy: 0.8548 - val_loss: 0.1369 - val_ecoder_output_loss: 0.0854 - val_classifier_output_loss: 0.5147 - val_ecoder_output_accuracy: 0.0281 - val_classifier_output_accuracy: 0.8628\n",
      "Epoch 16/30\n",
      "217/235 [==========================>...] - ETA: 0s - loss: 0.1360 - ecoder_output_loss: 0.0860 - classifier_output_loss: 0.4997 - ecoder_output_accuracy: 0.0329 - classifier_output_accuracy: 0.8594\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00016: val_loss did not improve from 0.13068\n",
      "\n",
      "Epoch 00016: val_loss did not improve from 0.13068\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: 0.1360 - ecoder_output_loss: 0.0860 - classifier_output_loss: 0.5000 - ecoder_output_accuracy: 0.0327 - classifier_output_accuracy: 0.8594 - val_loss: 0.1358 - val_ecoder_output_loss: 0.0854 - val_classifier_output_loss: 0.5043 - val_ecoder_output_accuracy: 0.0249 - val_classifier_output_accuracy: 0.8683\n",
      "Epoch 17/30\n",
      "227/235 [===========================>..] - ETA: 0s - loss: 0.1346 - ecoder_output_loss: 0.0859 - classifier_output_loss: 0.4874 - ecoder_output_accuracy: 0.0318 - classifier_output_accuracy: 0.8635\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00017: val_loss did not improve from 0.13068\n",
      "\n",
      "Epoch 00017: val_loss did not improve from 0.13068\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: 0.1347 - ecoder_output_loss: 0.0859 - classifier_output_loss: 0.4881 - ecoder_output_accuracy: 0.0318 - classifier_output_accuracy: 0.8633 - val_loss: 0.1347 - val_ecoder_output_loss: 0.0851 - val_classifier_output_loss: 0.4955 - val_ecoder_output_accuracy: 0.0300 - val_classifier_output_accuracy: 0.8693\n",
      "Epoch 18/30\n",
      "196/235 [========================>.....] - ETA: 0s - loss: 0.1337 - ecoder_output_loss: 0.0858 - classifier_output_loss: 0.4792 - ecoder_output_accuracy: 0.0316 - classifier_output_accuracy: 0.8667\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00018: val_loss did not improve from 0.13068\n",
      "\n",
      "Epoch 00018: val_loss did not improve from 0.13068\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",
      "Epoch 00018: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 0s 2ms/step - loss: 0.1335 - ecoder_output_loss: 0.0857 - classifier_output_loss: 0.4781 - ecoder_output_accuracy: 0.0317 - classifier_output_accuracy: 0.8661 - val_loss: 0.1338 - val_ecoder_output_loss: 0.0850 - val_classifier_output_loss: 0.4884 - val_ecoder_output_accuracy: 0.0294 - val_classifier_output_accuracy: 0.8702\n",
      "Epoch 19/30\n",
      "198/235 [========================>.....] - ETA: 0s - loss: 0.1322 - ecoder_output_loss: 0.0855 - classifier_output_loss: 0.4670 - ecoder_output_accuracy: 0.0311 - classifier_output_accuracy: 0.8705\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00019: val_loss did not improve from 0.13068\n",
      "\n",
      "Epoch 00019: val_loss did not improve from 0.13068\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: 0.1322 - ecoder_output_loss: 0.0855 - classifier_output_loss: 0.4668 - ecoder_output_accuracy: 0.0316 - classifier_output_accuracy: 0.8697 - val_loss: 0.1327 - val_ecoder_output_loss: 0.0847 - val_classifier_output_loss: 0.4797 - val_ecoder_output_accuracy: 0.0292 - val_classifier_output_accuracy: 0.8728\n",
      "Epoch 20/30\n",
      "233/235 [============================>.] - ETA: 0s - loss: 0.1316 - ecoder_output_loss: 0.0854 - classifier_output_loss: 0.4619 - ecoder_output_accuracy: 0.0320 - classifier_output_accuracy: 0.8711\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00020: val_loss did not improve from 0.13068\n",
      "\n",
      "Epoch 00020: val_loss did not improve from 0.13068\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 [==============================] - 0s 2ms/step - loss: 0.1316 - ecoder_output_loss: 0.0854 - classifier_output_loss: 0.4620 - ecoder_output_accuracy: 0.0320 - classifier_output_accuracy: 0.8711 - val_loss: 0.1321 - val_ecoder_output_loss: 0.0846 - val_classifier_output_loss: 0.4757 - val_ecoder_output_accuracy: 0.0253 - val_classifier_output_accuracy: 0.8735\n",
      "Epoch 21/30\n",
      "213/235 [==========================>...] - ETA: 0s - loss: 0.1314 - ecoder_output_loss: 0.0853 - classifier_output_loss: 0.4608 - ecoder_output_accuracy: 0.0307 - classifier_output_accuracy: 0.8716\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00021: val_loss did not improve from 0.13068\n",
      "\n",
      "Epoch 00021: val_loss did not improve from 0.13068\n",
      "\n",
      "Epoch 00021: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00021: 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: 0.1311 - ecoder_output_loss: 0.0853 - classifier_output_loss: 0.4578 - ecoder_output_accuracy: 0.0309 - classifier_output_accuracy: 0.8720 - val_loss: 0.1319 - val_ecoder_output_loss: 0.0846 - val_classifier_output_loss: 0.4726 - val_ecoder_output_accuracy: 0.0264 - val_classifier_output_accuracy: 0.8760\n",
      "Epoch 22/30\n",
      "235/235 [==============================] - ETA: 0s - loss: 0.1306 - ecoder_output_loss: 0.0852 - classifier_output_loss: 0.4536 - ecoder_output_accuracy: 0.0311 - classifier_output_accuracy: 0.8733\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00022: val_loss did not improve from 0.13068\n",
      "\n",
      "Epoch 00022: val_loss did not improve from 0.13068\n",
      "\n",
      "Epoch 00022: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00022: 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: 0.1306 - ecoder_output_loss: 0.0852 - classifier_output_loss: 0.4536 - ecoder_output_accuracy: 0.0311 - classifier_output_accuracy: 0.8733 - val_loss: 0.1314 - val_ecoder_output_loss: 0.0845 - val_classifier_output_loss: 0.4697 - val_ecoder_output_accuracy: 0.0271 - val_classifier_output_accuracy: 0.8748\n",
      "Epoch 23/30\n",
      "201/235 [========================>.....] - ETA: 0s - loss: 0.1299 - ecoder_output_loss: 0.0852 - classifier_output_loss: 0.4477 - ecoder_output_accuracy: 0.0306 - classifier_output_accuracy: 0.8751\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00023: val_loss did not improve from 0.13068\n",
      "\n",
      "Epoch 00023: val_loss did not improve from 0.13068\n",
      "\n",
      "Epoch 00023: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00023: 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: 0.1301 - ecoder_output_loss: 0.0851 - classifier_output_loss: 0.4497 - ecoder_output_accuracy: 0.0306 - classifier_output_accuracy: 0.8745 - val_loss: 0.1310 - val_ecoder_output_loss: 0.0844 - val_classifier_output_loss: 0.4664 - val_ecoder_output_accuracy: 0.0278 - val_classifier_output_accuracy: 0.8776\n",
      "Epoch 24/30\n",
      "231/235 [============================>.] - ETA: 0s - loss: 0.1298 - ecoder_output_loss: 0.0851 - classifier_output_loss: 0.4472 - ecoder_output_accuracy: 0.0310 - classifier_output_accuracy: 0.8758\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00024: val_loss did not improve from 0.13068\n",
      "\n",
      "Epoch 00024: val_loss did not improve from 0.13068\n",
      "\n",
      "Epoch 00024: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00024: 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: 0.1297 - ecoder_output_loss: 0.0851 - classifier_output_loss: 0.4467 - ecoder_output_accuracy: 0.0309 - classifier_output_accuracy: 0.8758 - val_loss: 0.1308 - val_ecoder_output_loss: 0.0843 - val_classifier_output_loss: 0.4649 - val_ecoder_output_accuracy: 0.0273 - val_classifier_output_accuracy: 0.8758\n",
      "Epoch 25/30\n",
      "229/235 [============================>.] - ETA: 0s - loss: 0.1294 - ecoder_output_loss: 0.0850 - classifier_output_loss: 0.4437 - ecoder_output_accuracy: 0.0307 - classifier_output_accuracy: 0.8766\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00025: val_loss improved from 0.13068 to 0.13015, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00025: val_loss improved from 0.13068 to 0.13015, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00025: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00025: 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: 0.1293 - ecoder_output_loss: 0.0850 - classifier_output_loss: 0.4432 - ecoder_output_accuracy: 0.0305 - classifier_output_accuracy: 0.8766 - val_loss: 0.1301 - val_ecoder_output_loss: 0.0843 - val_classifier_output_loss: 0.4586 - val_ecoder_output_accuracy: 0.0280 - val_classifier_output_accuracy: 0.8788\n",
      "Epoch 26/30\n",
      "232/235 [============================>.] - ETA: 0s - loss: 0.1290 - ecoder_output_loss: 0.0849 - classifier_output_loss: 0.4408 - ecoder_output_accuracy: 0.0313 - classifier_output_accuracy: 0.8773\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00026: val_loss improved from 0.13015 to 0.12985, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00026: val_loss improved from 0.13015 to 0.12985, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00026: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00026: 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: 0.1290 - ecoder_output_loss: 0.0849 - classifier_output_loss: 0.4404 - ecoder_output_accuracy: 0.0313 - classifier_output_accuracy: 0.8773 - val_loss: 0.1299 - val_ecoder_output_loss: 0.0842 - val_classifier_output_loss: 0.4570 - val_ecoder_output_accuracy: 0.0277 - val_classifier_output_accuracy: 0.8792\n",
      "Epoch 27/30\n",
      "232/235 [============================>.] - ETA: 0s - loss: 0.1285 - ecoder_output_loss: 0.0848 - classifier_output_loss: 0.4365 - ecoder_output_accuracy: 0.0309 - classifier_output_accuracy: 0.8788\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00027: val_loss improved from 0.12985 to 0.12961, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00027: val_loss improved from 0.12985 to 0.12961, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00027: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00027: 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: 0.1286 - ecoder_output_loss: 0.0848 - classifier_output_loss: 0.4376 - ecoder_output_accuracy: 0.0310 - classifier_output_accuracy: 0.8785 - val_loss: 0.1296 - val_ecoder_output_loss: 0.0841 - val_classifier_output_loss: 0.4555 - val_ecoder_output_accuracy: 0.0258 - val_classifier_output_accuracy: 0.8794\n",
      "Epoch 28/30\n",
      "230/235 [============================>.] - ETA: 0s - loss: 0.1281 - ecoder_output_loss: 0.0848 - classifier_output_loss: 0.4333 - ecoder_output_accuracy: 0.0311 - classifier_output_accuracy: 0.8800\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00028: val_loss improved from 0.12961 to 0.12940, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00028: val_loss improved from 0.12961 to 0.12940, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00028: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00028: 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: 0.1282 - ecoder_output_loss: 0.0848 - classifier_output_loss: 0.4344 - ecoder_output_accuracy: 0.0310 - classifier_output_accuracy: 0.8798 - val_loss: 0.1294 - val_ecoder_output_loss: 0.0840 - val_classifier_output_loss: 0.4540 - val_ecoder_output_accuracy: 0.0256 - val_classifier_output_accuracy: 0.8800\n",
      "Epoch 29/30\n",
      "227/235 [===========================>..] - ETA: 0s - loss: 0.1277 - ecoder_output_loss: 0.0847 - classifier_output_loss: 0.4308 - ecoder_output_accuracy: 0.0311 - classifier_output_accuracy: 0.8806\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00029: val_loss improved from 0.12940 to 0.12881, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00029: val_loss improved from 0.12940 to 0.12881, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00029: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00029: 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: 0.1279 - ecoder_output_loss: 0.0847 - classifier_output_loss: 0.4318 - ecoder_output_accuracy: 0.0313 - classifier_output_accuracy: 0.8803 - val_loss: 0.1288 - val_ecoder_output_loss: 0.0838 - val_classifier_output_loss: 0.4504 - val_ecoder_output_accuracy: 0.0278 - val_classifier_output_accuracy: 0.8808\n",
      "Epoch 30/30\n",
      "232/235 [============================>.] - ETA: 0s - loss: 0.1274 - ecoder_output_loss: 0.0844 - classifier_output_loss: 0.4297 - ecoder_output_accuracy: 0.0323 - classifier_output_accuracy: 0.8813\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00030: val_loss improved from 0.12881 to 0.12836, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00030: val_loss improved from 0.12881 to 0.12836, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00030: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00030: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "Epoch 00030: saving model to model/AE_model/model_2\\KERAS_check_model_epoch30.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 2ms/step - loss: 0.1274 - ecoder_output_loss: 0.0845 - classifier_output_loss: 0.4296 - ecoder_output_accuracy: 0.0323 - classifier_output_accuracy: 0.8813 - val_loss: 0.1284 - val_ecoder_output_loss: 0.0836 - val_classifier_output_loss: 0.4475 - val_ecoder_output_accuracy: 0.0279 - val_classifier_output_accuracy: 0.8809\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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8vr+FvWDJwJhECbdCTbk7yYZb3HRbs3sOt8R4RM9v2/U60ha13FunudY7SVe7k3ZTDdLasE/hNpBBLcPYqVnUkklNZBg1jKFWp7CzY/4wmjVIUNpIp5V0WkijjXRpJY1W0vGepZV02qjXDGrIot6XQ70vhwa/ezQFhtMSGEFzcDjhYDbBYIAMP+QEWsnxNZMtrWRLE1m+ZobRzDBpYpg2k04zGswinJFHJCOPcMZIIpl5+NNzCAR8BHw+gn5hlN/HOJ8Q9E7kAZ/g7zrt3zU/ze/DjZCZuiwZGLMvmmpgxzqoKoMq77l9uqbclSvvJRUf6gsSkQBhCbpf2+KnlQD1ZLJTs6iK5BFqG8f2tkxqNMs7aQ+jhix2ahZNvkwC3q9QfyBIIBAkEAwSCKYTDAYIBNIJpAVJDwQJBgME/T7vIbteB4R0v4+xPqE44OZlBP2kB3Z/7vTaew74JOVPtIOBJQMDkQiEVsLGf8Pm5XDY5TD+sGRHlTyq7td83TaoD7nnuq27XteHYOdmd/JvrOq87bACyJsEE46AWRe51+nDaSFAdbOwo1nZ0QSVTbC9UdlWr2xriLClXgnVh6lsUurbfLQSINJNY7+cjAAF2enkZ6VRkJ1OQY57HpudzsHZ3rzsdApy0slK89uJ2MTFkkEqaq6FTUth4zuw4d9QvgSaa3Ytr94AX3gmaeElVCQC9dugZhPsLPeeN7lf8Ts3u5N+3TZoa4yxscCwfMgeBTljYNyn0bzJNGRNIBQYxwYK2NQQZEt1I1tqmqhY18SW5U1s3dlEbVMbdKmQ9PuEgux0RuVkMCovnTkT0sgdlsaIzOBuj9xh7jknI4jfZyd30/csGaSC2q1Q9oY78W9cBFv/47WuEBg1Aw46H4qPdL9m338SFv7YFXPkTUpy4PugYQdsXuYeoZW7Tv47t7gK02iBDBg+HkaMhwlz3ck+exRkjUKzCqn157GxNYd1DRmsr25h444Gyqsa2bLKnfQbWsLAdu8BIjAqJ52xIzLZvzCbo/fLZ9TwDApz0hmV4538h6eTNyzNTuxmwLBkMBSF26B8Maz5B3z8D6h4380PZkFRCXzyW+7EP74EMnM7NqtpbGVV7ukczl3IskfhxO8mJ/7eaqqBLe/tOvlveheqo7puH1EMuRNgwpHuhD98PIwo6ngOp+eyqbqJ0u11bNzRwIYdDWwobWDDjkY27migrrkCqOjYXUF2OkV5mUwbk8NxB4xiXG4GY0ZkMHZEBmNHZFKYk07Qn+yeXozpHUsGQ8XOLbDmFZcA1i50xT7id790T7od9jsRRh/cqT14dUMLi1ZUsKh0B4vWVfLhlp2owksFRzBt+aNw/M3g8yfvmGIJt7nktnGRK+ravAwq1+xanlsM4w6Fki/BuDkw9hDIzCUSUbbWNrFuez3rttdTtqbee/0+G3Y00BreVYSTHvAxYeQwikcOY+7kkR2vi0cOY8LITIal2b+NGXrsr3qwUnUnxNV/h49fga0fuPk5Y2HG2bD/KTDl+E6//CvrmnlnXYhF63bw79JKVm2tRdWd/A4tzuOGk6by4ead/Objo/i1/+ew9p8w9ZSkHF6H5lp3lbPh37DhbShfCq31btnw8e6Ef8jF3ol/DmTlE44oq7fW8u6GKt5dsp4Vm99nfWUDja27WvakBXxMzs9i/1HZnDJjDJMLhjEpP4tJBVkUZqfjs+Ibk2IsGQw2OzfD8sdg+aOwoxR8AVf8cfI8lwBGz3SF1p6VFTuZv3wzr3y0ldVb6wDIDPo5bGIeZ80ay9wp+cwqGkF6wN+x/tkfzqExI4/Mdx/q/2Swc7M76W9Y5J7b6zfEB2MOhjmXufqN4iNh+DgAqupbWLaxinff3M67Gz7mvY3V1Le4E//IrDRmFY3g6P0LmFSQxeT8LCYXZjF2eIad8I2JYslgMGhrgdUvwrJHXFGQRmDiMfDJm2D6GZAxotPqGyobeO79zTy7fBOrt9bh9wmfmJLPeXPGM3dyPgePH0FaIHaZ9vQxwzl08iieCX2Si1c9j9SFILsfug1f/zYs+KGr6Iao+o2boHguFB0O6TmAq9t46T8VLFr3Hss2VFG63V0p+H3C9DE5nH9oEYdOzOXQ4jyKRw6zppXGxMGSwUC2dYVLAO8/AQ2VkDMOjrkRZn8O8vfrtOq22iaef38Lzy7fzPKN1QAcPimPH5w7kzMOHkt+dnrcb3vFUZP42WPHcEn6s/Den+Do6/vyqDrb+A4s+BGULoCsUXDS92C/E3ar3whHlDdXh3hqaTkvraigpS1CflYac4rz+ExJEYcW5zGraISV5xuzl+w/Z6BpaYD3HnNJYPMy8AVh+pkw5/PuJBlVodv+C/nZ9zbx9tpKIgozxg7n5tOnc/Yh4xifm7lXIZwyYzR3DN+P1TqDA959GI66rlPRU5/YtBQW/NhVeA8rgFPvhJIrXedkUdZsq+WppZt4elk5W3c2MyIzyMWHT+CCQ4uYVTTCfvUb00csGQwkdSF47EKXBEYfBKf9BA6+ELI6jwTa0hbhwbfW8ctXPqa+Jcyk/GFce8L+nDN7HPuPytnnMAJ+H5fOLeb3rx7Df7fc6yqqi4/c5/0Crgnogh+7Yq/MPFfXcfhVkJ7dsUpNQyvPvb+Zp5aWs3xjNX6fcPwBhcw7u4gTDxzVUb9hjOk7lgwGih2l8MfzobYCPvuouxqI8av3jY9DzJu/grWhek6aPorrTprKIQn4hXzxEcWc/OpRNPseIf3dh/c9GVT8x93MtvJvro7jxFvhiK9Axq5eGD/eWssvX/2Ylz/cSktbhGmjc/juGQdy7pxxjMrJ2McjMsb0xJLBQLDpXXj0Qtep2eXz3Q1hXZRXNXDn3z7i7ysqmJg/jPuvKOHE6aMTFlJBdjonzprM/A8/wWdWPI2cdlenE3fcwm0w/zpX9JU+HI6/BY786m6V3gtWbeO6x5bhE/jcEcV85rAiZo4bbsVAxvQTSwbJtuYVeOILrs+by/4ChQd0WtzUGuZ3r5Xy24VrEIFvfWoaVx4zmYxg4otKvnDUJL63/DgulFfhP3+Bki/2fif/+rlLBEddB8d+wxUNRVFVHnyrjB/87UOmjxnOH64oYeyIvavrMMbsvYQmAxE5DfglbtjL+1T1ri7Li4GHgFxvnZu90dFSw/I/wfxrofBAuPTPMHxsxyJV5ZWPtnHH31awcUcjZx48lu+ceeBeVwrvjdkTcmHcoZRWTWTyuw8jvU0Gm96FhXfBQRe4CuIuWsMR5s1fwaOLNnDqjNH8/LOzyUq33yfGJEPCOlARET9wN3A6MAO4RERmdFntVuBJVZ0DXAz8NlHxDCiq8Mb/wjPXwMSj4IvPd0oE67bX88UHF3PVw0vICPh57MtzufvSQ/s1EbT7wlGT+WPzJ5HN77py/3i1NMBfr4bs0XDm/+y2uKaxlS89uJhHF23gmuP2457LDrNEYEwSJbI3rSOANapaqqotwOPAuV3WUaC9IHoEsDmB8QwMkTC8eBO8+n33i/nSpzqVnz+9rJxP/fx1lpRVceuZB/LCDcdy1P4FSQv3zFljeS39RFoJwrI/xr/hP26Dyo/hvN/uVjS0vrKe83/7L/5dWslPPzOLm0+fbncDG5NkiUwG44GNUdPl3rxo84DLRKQceAG4LtaORORqEVkiIktCoVAiYu0frU3w1BfhnXvhE9fC+fe5AcU9S9dX8e2nPmBOcS7//OZxfPnYKUnv/TIj6Of0uTP4e7iEyPLH3THsyeqXYfF97hinHN9p0aLSSs67+19U1rfwxyvnclHJhMQEbozplWT3s3sJ8KCqFgFnAH8Ukd1iUtV7VbVEVUsKC/uha4REaKyGR86HD5915eef+iH4dh1qRU0T1zyylDEjMrjnssMGVFPKS+dO5InwCfiaq13T0J7Ub4dnvwajZsKJt3Va9OclG7nsD4vIy0rjmf93NEdOye9mJ8aY/pbIZLAJiP7ZV+TNi3Yl8CSAqr4NZADJKxNJlLYWeOhs1/XC+fe5ljVRmlrDfOWPS6hvbuP3XyghLystSYHGNi43k5zpJ1LOKMJLH+5+RVV47gY3SPv590LQJbRIRLnrxZV866n3mTs5n6e/ejSTCrL6J3hjTFwSmQwWA1NFZLKIpOEqiOd3WWcDcBKAiByISwaDuByoG0sfcH3wX3AfzLqw0yJV5Tt//YD3ymv434tmM23Mvt9BnAhfOHoKj7ceh7/sNTfgeyzLHnFXDifdDmMOAqC2qZVrHlnKPa+t5dK5xTzwxcMZMSzYj5EbY+KRsGSgqm3AtcBLwEe4VkMrROQOETnHW+0bwFUi8h7wJ+AKVdXYexykmmpc88pJx8KMrvXn8Ic31/HXZZv4r5MP4LSDxiQhwPgcOWUk7448nTA+dNkju6+woxRe/LY7ziO/BsDqrbWce/e/eHXlNm4/awZ3nndQ0utAjDGxJbQtn3fPwAtd5t0e9fpD4OhExpB0b/4cGne4eoIud9O+8XGIH73wEafNHMN1J+6fpADjIyKccXQJrz0/i2OWPkLa8bfs6lU03AZ//YobW+HT94DPx7PLN3HzXz4gKz3Ao1+ea/UDxgxw9jMtkao3wr//D2Z9FsbN7rSobHs91z62jKmjcvifiw4ZFE0rPz1nPM/6TyatoQLWvrprwZs/h/J34Mz/oSVrHPPmr+CGx5dz0PjhPH/9MZYIjBkELBkk0j/vdJWqJ97aaXZtUytffngJIvD7L5QMmputstIDFB56Ltt1OM3vPOBmblrqOqA76DNsmXgWF9/7Ng++VcaVx0zmsauOZPTwgdMqyhjTPUsGibLlPTcozZHXuEHaPZGI8l9PvMe67fXc/blDKc4f1sNOBp5Lj9qPp8KfJLD2ZVeR/NerIWcMiw78Dmf96k1WVtTym8/N4bazZlj9gDGDiP23JoIqvHybu/P2mBs7LfrFK6t55aOt3HrmgRydxDuL99bkgizWTTgfv4bRB8+AyjXMn3wblzyyktxhQeZfezRnzRqX7DCNMb1kySAR1rwC616D474Nmbkds1/4YAu/+ucaLiop4oqjJiUtvH31qeOOYVFkOrJzMy+P+AzXLxrO6QeP5dlrj+mTwXWMMf1vcBRWDybhNndVMHIKlHypY/ZHW3byjSffY05xLj8476BB3U//8QeM4rrML7Cu/mXuCJ3NbWfN4EtHTxrUx2RMqrNk0NeWPwqhj+DChyDg7iSORJSvPrKU4ZkBfnfZYYN+2EafTzjx1HO5e+FMHrpgFodPGpnskIwx+8iSQV9qqYcFP4KiIzrdYPbBphrKKhv434sOYdQQaV1zwWFFXHBYUbLDMMb0EUsGfemt30BdBVz0cKcbzBas2oYIHD9tVBKDM8aY7lkFcl+p3Qr/+iUceA4Uz+20aMGqELMn5DJygHVAZ4wx7SwZ9JWFP4ZwM5w8r9Ps7XXNvF9ezQl2VWCMGcAsGfSFbSvh3Yfg8C9D/n6dFr2+OoQqlgyMMQOaJYO+8Mr3IC0bPnnTbosWrApRkJ3OzHHDY2xojDEDgyWDfbXuDVj9dzj2Rsjq3CFbWzjC66tDnDCtcFB0RGeMSV2WDPZFJAIv3wrDi2DuNbstXr6xmprGVk6YbkVExpiBzZqW7ouP5sOW5fDpeyGYudviBau24fcJx0wdfH0QGWNSi10Z7IuyNyEtBw6+MObif64MUTIxj+EZNsyjMWZgS2gyEJHTRGSViKwRkZtjLP+5iCz3HqtFpDqR8fS50EoonAa+3T/GipomPtqy04qIjDGDQsKKiUTED9wNnAKUA4tFZL431CUAqvpfUetfB8xJVDwJEVoFU0+NuWjhqm2ANSk1xgwOibwyOAJYo6qlqtoCPA7sPiL8LpcAf0pgPH2rYQfUb4NR02MuXrBqG+NGZHDA6Ox+DswYY3ovkclgPLAxarrcm7cbEZkITAb+2c3yq0VkiYgsCYVCfR7oXgmtcs+FuyeDlrYIb368neOnj7JunY0xg8JAqUC+GHhKVcOxFqrqvapaoqolhYWF/RxaN0Ir3XPhtN0WLSnbQX1L2IqIjDGDRiKTwSZgQtR0kTcvlosZTEVE4JJBMMvdY9DFglXbSPP7OGq//BgbGmPMwJPIZLAYmCoik0UkDXfCn991JRGZDuQBbycwlr4XWgmFB8RsSbRgVYi5U0aSlW63cRhjBoeEJQNVbQOuBV4CPgKeVNUVInKHiJwTterFwOOqqomKJSFCq2LWF2zc0cCabXVWRGSMGVQS+tNVVV8AXugy7/Yu0/MSGUNCNFZD7ZaY9QUdTUrt/gJjzCCyxysDEblORPL6I5hBY/tq9xzjymDBqhCT8ocxuSCrn4Myxpi9F08x0WjcDWNPencUW1vJbloSNbWGeWvtdhve0hgz6OwxGajqrcBU4A/AFcDHIvIjEdmvxw2HstAqCGRA7sROs98uraSpNWJFRMaYQSeuCmSvcrfCe7ThWv88JSI/TWBsA1doJRQcAD5/p9kLV24jI+hj7uSRSQrMGGP2Tjx1BjeIyFLgp8C/gINV9avAYcAFCY5vYIrRkkhVWbAqxNH7FZAR9HezoTHGDEzxtCYaCZyvquujZ6pqRETOSkxYA1hzLdRshMIrOs0u3V7Phh0NXPXJKcmJyxhj9kE8xUQvAjvaJ0RkuIjMBVDVjxIV2IDVTUuiBStdk9LjDxgg3WUYY0wvxJMM/g+oi5qu8+alpm3tLYk6J4OFq0JMHZXNhJHDkhCUMcbsm3iSgUTfHayqEVJ5uMzQSvCnQd6kjln1zW0sWlfJidaKyBgzSMWTDEpF5HoRCXqPG4DSRAc2YIVWQf5U8O/Kh/9as53WsNr9BcaYQSueZHANcBSux9FyYC5wdSKDGtDah7qMsmBViOz0ACWT7EZtY8zgtMfiHlXdhutMzrTUQ/UGmH1pxyxVZeGqbRw7tYCgf6AMD2GMMb2zx2QgIhnAlcBMIKN9vqp+KYFxDUzbPwa005XByopattQ08V8nWxGRMWbwiuen7B+BMcCngNdwg9TUJjKoAat9qMtRB3bMWuD1UnrcNGtSaowZvOJJBvur6m1Avao+BJyJqzdIPaGV4AvAyF03li1cGWLmuOGMHp7Rw4bGGDOwxZMMWr3nahE5CBgBpGaZSGgV5O8P/iAANQ2tLN1QZQPZGGMGvXjuF7jXG8/gVtywldnAbQmNaqAKrYQxB3VMvrEmRDiinDDdioiMMYNbj1cGIuIDdqpqlaq+rqpTVHWUqv4unp174x+sEpE1InJzN+tcJCIfisgKEXlsL46hf7Q2QdW6Tncev19eQ3rAxyFFucmLyxhj+kCPycC72/imvdmxiPiBu4HTgRnAJSIyo8s6U4FbgKNVdSbw9b15r35R+TFopFNLorXb6phckEXAmpQaYwa5eM5ir4jIN0VkgoiMbH/Esd0RwBpVLVXVFuBx4Nwu61wF3K2qVdBxT8PA1N6SKOrKYG2ojv1GZScpIGOM6Tvx1Bl81nv+WtQ8BfbUV/N4YGPUdPvdy9EOABCRfwF+YJ6q/r3rjkTkary7nouLi+MIOQFCK0F8rgIZaG4Ls2FHA+ccMi458RhjTB+K5w7kyQl+/6nA8bj7F14XkYNVtbpLDPcC9wKUlJQoyRBa6ZqUBtIBWF/ZQESxKwNjzJAQzx3IX4g1X1Uf3sOmm4AJUdNF3rxo5cAiVW0F1onIalxyWLynuPpdl9HNSkOuV+/9Ci0ZGGMGv3jqDA6PehwLzAPOiWO7xcBUEZksImm4/o3md1nnGdxVASJSgCs2Gng9ora1QOXaLvUF9QBMLshKVlTGGNNn4ikmui56WkRycZXBe9quTUSuBV7C1Qfcr6orROQOYImqzveWnSoiHwJh4FuqWtn7w0iwHWtBw52TwbY6xo3IICs9dYd2MMYMHXtzJqsH4qpHUNUXgBe6zLs96rUCN3qPgSvUPrpZVLPSUB1TrIjIGDNExFNn8Byu9RC4YqUZwJOJDGrACa0CBAqmAq7b6rWhei44dHxy4zLGmD4Sz5XBz6JetwHrVbU8QfEMTKGVbpjLYKabrG2mrrnNWhIZY4aMeJLBBmCLqjYBiEimiExS1bKERjaQbFvZqb5gjbUkMsYMMfG0JvozEImaDnvzUkO4FSrXdKkvcC2JLBkYY4aKeJJBwOtOAgDvdVriQhpgdqyDSOtuLYmy0vyMHp6exMCMMabvxJMMQiLScV+BiJwLbE9cSANMDy2JRCRJQRljTN+Kp87gGuBREfmNN10OxLwreUjq6KBuVzIoDdVz+KS8JAVkjDF9L56bztYCR4pItjddl/CoBpLQSsgthjR3p3FjS5hN1Y1cXDhhDxsaY8zgscdiIhH5kYjkqmqdqtaJSJ6I3NkfwQ0IXfsk2u61JLJmpcaYISSeOoPTo3sR9cYeOCNhEQ0kkTBsXx2zJdGUQuuTyBgzdMSTDPwi0tFsRkQygdRoRlNVBuHm3VoSicCkfEsGxpihI54K5EeBV0XkAW/6i8Ceuq8eGmKMbla6vZ4JecPICPqTFJQxxvS9eCqQfyIi7wEne7N+oKovJTasASL0kXsuOKBj1tptdexnRUTGmCEmrpHcVfXvqvpN4HvAKBF5PrFhDRChVTB8PGQMByASUUq3W2+lxpihJ57WRGki8mkR+TOwBTgRuCfhkQ0EoZWdKo831zTS1BqxbiiMMUNOt8lARE716gnWARfg6gl2qOoXVfW5/gowaSIRCK2OObqZFRMZY4aanq4M/g5MAY5R1cu8BBDpYf3diMhpIrJKRNaIyM0xll8hIiERWe49vty78BOoZgO0NcYe99juMTDGDDE9VSAfihu3+BURKcUNdRl3ExoR8QN3A6fgurBYLCLzVfXDLqs+oarX9i7sfhCjJdHaUB0jMoPkZ6VOP33GmNTQ7ZWBqi5X1ZtVdT9cxfFsICgiL4rI1XHs+whgjaqWej2dPg6c2xdB94uODuqiWxLVM6UwyzqoM8YMOfG2JnpLVa8DioCfA0fGsdl4YGPUdLk3r6sLROR9EXlKRGJ2+CMiV4vIEhFZEgqF4gl534VWQfYYyNzVId3aUJ1VHhtjhqS4kkE7VY2o6suq+qU+ev/ngEmqOgv4B/BQN+97r6qWqGpJYWFhH731HnRpSVTb1Mq22mZLBsaYIalXyaCXNgHRv/SLvHkdVLVSVZu9yfuAwxIYT/xUd++gzloSGWOGsEQmg8XAVBGZLCJpuMro+dEriMjYqMlzgI8SGE/8dm6ClrrdBrQBa0lkjBmaeuyOwmsRtEJVp/e0Xiyq2iYi1wIv4Voh3a+qK0TkDmCJqs4HrvdGUWsDdgBX9PZ9EmJbe+Vx55ZEAZ9QPHJYkoIyxpjE6TEZqGrYu0+gWFU39HbnqvoC8EKXebdHvb4FuKW3+024UIxksK2e4vxhBP2JvJgyxpjkiKfX0jxghYi8A9S3z1TVc7rfZJALrYRhBZCV3zHLWhIZY4ayeJLBbQmPYqAJrYJRB3ZMtoUjrK9s4KQDRycxKGOMSZw9lnmo6mtAGRD0Xi8G3k1wXMnT3pIoqtvq8qpGWsIRa0lkjBmy4um19CrgKeB33qzxwDMJjCm5GquguQby9+uY1d6SyLquNsYMVfHUhn4NOBrYCaCqHwOjEhlUUlWVuefciR2zOpqV2pWBMWaIiicZNHt9CwEgIgFAExdSklWvd895u5JBaaieguw0codZB3XGmKEpnmTwmoh8B8gUkVOAP+O6kRiaurkysCIiY8xQFk8yuBkIAR8AX8HdN3BrIoNKqqr1kDmyY6hLcIPaWBGRMWYo22PTUlWNAL/3HkNf9fpORUQ76lvYUd9i9xgYY4a0bpOBiDypqheJyAfEqCPwehodeqrKYOwhHZMdo5tZMjDGDGE9XRl83Xs+qx/iGBgiYajeCAfuurl6V2+llgyMMUNXT8ngb7ihL+9U1c/3UzzJtXMzRFohb1LHrLWhOtICPsbnZSYvLmOMSbCekkGaiHwOOEpEzu+6UFX/mriwkiRGs9K1oTom52fh99lQl8aYoaunZHANcCmQC5zdZZkCQy8ZVHnJoFOz0noOHJuTpICMMaZ/dJsMVPVN4E0RWaKqf+jHmJKnqgzEByPcAG0tbRE27GjgrFlje97OGGMGuZ5aE52oqv8EqlKqmGj4eAi4O4037KgnHFGrPDbGDHk9FRMdB/yT3YuIYCgXE0UVEa3ZZi2JjDGpoadiou95z1/c252LyGnAL3HDXt6nqnd1s94FuJ5RD1fVJXv7fvusqgz2P7ljsr2Dusl297ExZoiLpwvrG0RkuDj3ici7InJqHNv5gbuB04EZwCUiMiPGejnADcCi3offh1oboa5it5ZEY4ZnkJ0ezxhAxhgzeMXTN9GXVHUncCqQD3weiPkLv4sjgDWqWur1evo4cG6M9X4A/ARoii/kBKn2hnjudI9BPfuNsqsCY8zQF08yaG9gfwbwsKquiJrXk/HAxqjpcm/erh2LHApMUNXnewxA5GoRWSIiS0KhUBxvvRe6NCtVVUpt3GNjTIqIJxksFZGXccngJa9YJ7KvbywiPuB/gW/saV1VvVdVS1S1pLCwcF/fOrYuN5yF6pqpbWqzZGCMSQnxFIZfCcwGSlW1QURGAvFUKm8CJkRNF3nz2uUABwELRQRgDDBfRM5JSiVyVRkEMiDbDXq/1mtJNMUqj40xKSCeK4NPAKtUtVpELsONZVATx3aLgakiMllE0oCLgfntC1W1RlULVHWSqk4C/g0kJxGASwa5E8ElpqihLu3KwBgz9MWTDP4PaBCRQ3BFOmuBh/e0kaq2AdcCLwEfAU+q6goRuUNEzul56yToMo7B2lAdw9L8jBmekcSgjDGmf8RTTNSmqioi5wK/UdU/iMiV8excVV/AjYwWPe/2btY9Pp59JoSqq0Au/kTHrNJQPVMKs/BZB3XGmBQQz5VBrYjcAlwGPO9V/AYTG1Y/a6yC5p27j3tcYEVExpjUEE8y+CzQDFypqhW4iuD/TmhU/a2qzD179xg0toTZVN1o9QXGmJQRzxjIFbgmoO3TG4ijzmBQ6dKsdN32elSxG86MMSkjnu4ojhSRxSJSJyItIhIWkXhaEw0eXW44s5ZExphUE08x0W+AS4CPgUzgy8BvExlUv6sqg8yRkDEccJXHIjC5wK4MjDGpIZ5kgKquAfyqGlbVB4DTEhtWP4vRrHR8biYZQX8SgzLGmP4TT9PSBu+mseUi8lNgC3EmkUGjaj2MObhjcq31SWSMSTHxnNQ/jxuP4FqgHtfFxAWJDKpfRcKux1KvJVEkoh33GBhjTKqIpzWRV7tKI/D9xIaTBLVbINLaUUy0saqBxtYw00bnJDkwY4zpPz2NgfwBbnjLmFR1VkIi6m9d7jFYVVELwLQxlgyMMamjpyuDs/otimTq0qy0PRlMtSsDY0wK6SkZBIHRqvqv6JkicjRQkdCo+lP1ekBghOtte9XWWiaMzLShLo0xKaWnCuRfADtjzN/pLRsaqspgRBEE0gB3ZTBt9PDkxmSMMf2sp2QwWlU/6DrTmzcpYRH1t6r1HUVEzW1h1m2vZ9oYa1ZqjEktPSWD3B6WZfZxHMkTdcNZaaietogybYxdGRhjUktPyWCJiFzVdaaIfBlYmriQ+lFro2ta6rUkWr3Va0lklcfGmBTTUy3p14GnReRSdp38S4A04NMJjqt/VG90z14x0cqKWoJ+sT6JjDEpp9srA1XdqqpH4W40K/Me31fVT3jdWu+RiJwmIqtEZI2I3Bxj+TUi8oGILBeRN0Vkxt4dxl7qco/B6opaphRkkxYYWr1tGGPMnsRzB/ICYEFvdywifuBu4BSgHFgsIvNV9cOo1R5T1Xu89c/BjZvQf53gdRnHYGVFLYdNzOu3tzfGmIEikT+BjwDWqGqpqrYAjwPnRq+gqtFNV7Po4Y7nhKgqg0AGZI+mtqmVTdWNduexMSYlJfLOqvHAxqjpcmBu15VE5GvAjbi6iBNj7UhErgauBiguLu67CKvKXH2BCKu3ugFtrPLYGJOKkl44rqp3q+p+wLeBW7tZ515VLVHVksLCwr5786hmpdYnkTEmlSUyGWzCdXfdrsib153HgfMSGE9nqp1uOFu9tZasND/jc4fOLRTGGBOvRCaDxcBUEZnsDY5zMTA/egURmRo1eSZuaM3+0VgFzTs7WhKtrNjJ1NE5+HzSbyEYY8xAkbA6A1VtE5FrgZdwg+Pcr6orROQOYImqzgeuFZGTgVagCrg8UfHsJqolkaqyqqKWT80c029vb4wxA0lCu+ZU1ReAF7rMuz3q9Q2JfP8eRd1jEKprpqqhlQOs8tgYk6KSXoGcNFHjGKyucC2JplvlsTEmRaVuMqheD5l5kDGclRXudgdrSWSMSVWpmwyqyjp1UFeQnUZ+dnpSQzLGmGRJ4WSwvtNQl3ZVYIxJZamZDCJhqNkIeROJRJTVW+us8tgYk9JSMxnUboFwC+RNYmNVA42tYas8NsaktNRMBlEtidq7obArA2NMKkvRZFDmnvMmWTIwxhhSNRlUrwcERkxg1dZaJozMJCs9offfGWPMgJaayaBqPQwfD4E015Jo9PBkR2SMMUmVosmgDPIm0dwWpnR7PdPGZCc7ImOMSarUTAbeOAaloXrCEWXaGLsyMMakttRLBq1NrmlpVEsiG93MGJPqUi8ZVG9wz3mTWLW1lqBfmFKYldyYjDEmyVKvCU3UOAarltWyX2E2QX/q5URjUklrayvl5eU0NTUlO5SEy8jIoKioiGAw2KvtUi8ZdLrH4EMOm5iX1HCMMYlXXl5OTk4OkyZNQmTojmaoqlRWVlJeXs7kyZN7tW1CfxKLyGkiskpE1ojIzTGW3ygiH4rI+yLyqohMTGQ8gEsGgQxqAyPZVN1oHdQZkwKamprIz88f0okAQETIz8/fqyughCUDEfEDdwOnAzOAS0RkRpfVlgElqjoLeAr4aaLi6VC9HnKLWb2tHrDKY2NSxVBPBO329jgTeWVwBLBGVUtVtQV4HDg3egVVXaCqDd7kv4GiBMbjePcYdLQksisDY4xJaDIYD2yMmi735nXnSuDFBMbjVG1wQ11urSUrzc/43MyEv6UxJrVVVlYye/ZsZs+ezZgxYxg/fnzHdEtLS4/bLlmyhOuvvz7hMQ6ICmQRuQwoAY7rZvnVwNUAxcXFe/9GjVXQXAN5E1n5n50cMCYHny81Lh2NMcmTn5/P8uXLAZg3bx7Z2dl885vf7Fje1tZGIBD7dFxSUkJJSUnCY0xkMtgETIiaLvLmdSIiJwPfBY5T1eZYO1LVe4F7AUpKSnSvI/JaEql3w9mnZo7Z610ZYwan7z+3gg837+zTfc4YN5zvnT2zV9tcccUVZGRksGzZMo4++mguvvhibrjhBpqamsjMzOSBBx5g2rRpLFy4kJ/97Gf87W9/Y968eWzYsIHS0lI2bNjA17/+9T67akhkMlgMTBWRybgkcDHwuegVRGQO8DvgNFXdlsBYHG8cg6r0cVQ1bLX6AmNMUpWXl/PWW2/h9/vZuXMnb7zxBoFAgFdeeYXvfOc7/OUvf9ltm5UrV7JgwQJqa2uZNm0aX/3qV3t9T0EsCUsGqtomItcCLwF+4H5VXSEidwBLVHU+8N9ANvBnrwZ8g6qek6iY2q8MVjXlA1utJZExKai3v+AT6cILL8Tv9wNQU1PD5Zdfzscff4yI0NraGnObM888k/T0dNLT0xk1ahRbt26lqGjf294ktM5AVV8AXugy7/ao1ycn8v13U70eMvNYscOVNNmVgTEmmbKydnWFc9ttt3HCCSfw9NNPU1ZWxvHHHx9zm/T09I7Xfr+ftra2PokltfphqFrf0UFdQXYa+dnpe97GGGP6QU1NDePHuwaXDz74YL+/f4olgzLIm8TqrbV2VWCMGVBuuukmbrnlFubMmdNnv/Z7Q1T3vnFOMpSUlOiSJUt6v2EkAj8cjc79KjPeOIqLj5gwoMoOjTGJ89FHH3HggQcmO4x+E+t4RWSpqnbbRjV1rgxqt0C4hR1pY2lsDTPdrgyMMaZD6iQDryXRunAhgI1uZowxUVInGXjjGHzY6LqsnjrKxj02xph2A6I7in7RUAm+IEtqsike6SMrPXUO3Rhj9iR1rgyOug6+W8FHW5s4wG42M8aYTlInGQDNKpRur7fKY2OM6SKlykpKQ/WEI8oBlgyMMf2osrKSk046CYCKigr8fj+Fha4xyzvvvENaWlqP2y9cuJC0tDSOOuqohMWYUsmgfUAbuzIwxvSnPXVhvScLFy4kOzvbkkFfWbW1lqBfmFyQteeVjTFD04s3Q8UHfbvPMQfD6Xf1apOlS5dy4403UldXR0FBAQ8++CBjx47lV7/6Fffccw+BQIAZM2Zw1113cc899+D3+3nkkUf49a9/zbHHHtu38ZNqyaCilv0Kswn6U6qqxBgzwKgq1113Hc8++yyFhYU88cQTfPe73+X+++/nrrvuYt26daSnp1NdXU1ubi7XXHNNr68meivlksFhE/OSHYYxJpl6+Qs+EZqbm/nPf/7DKaecAkA4HGbs2LEAzJo1i0svvZTzzjuP8847r99iSplkUNvUyqbqRj43dx+GzTTGmD6gqsycOZO33357t2XPP/88r7/+Os899xw//OEP+eCDPi7S6kbKlJes3loHYAPaGGOSLj09nVAo1JEMWltbWbFiBZFIhI0bN3LCCSfwk5/8hJqaGurq6sjJyaG2tjahMaVMMmhvSWRdVxtjks3n8/HUU0/x7W9/m0MOOYTZs2fz1ltvEQ6Hueyyyzj44IOZM2cO119/Pbm5uZx99tk8/fTTzJ49mzfeeCMhMSW0mEhETgN+iRv28j5VvavL8k8CvwBmARer6lOJiqUgO41TZoymKC8zUW9hjDF7NG/evI7Xr7/++m7L33zzzd3mHXDAAbz//vuJDCtxyUBE/MDdwClAObBYROar6odRq20ArgASV0XuOXXmGE6dOSbRb2OMMYNSIq8MjgDWqGopgIg8DpwLdCQDVS3zlkUSGIcxxpg9SGSdwXhgY9R0uTev10TkahFZIiJLQqFQnwRnjEktg21Ux721t8c5KCqQVfVeVS1R1ZL2/jyMMSZeGRkZVFZWDvmEoKpUVlaSkZHR620TWUy0CZgQNV3kzTPGmH5VVFREeXk5qVCykJGRQVFRUa+3S2QyWAxMFZHJuCRwMfC5BL6fMcbEFAwGmTx5crLDGNASVkykqm3AtcBLwEfAk6q6QkTuEJFzAETkcBEpBy4EficiKxIVjzHGmO4l9D4DVX0BeKHLvNujXi/GFR8ZY4xJokFRgWyMMSaxZLDVrotICFi/l5sXANv7MJyBYKgd01A7Hhh6xzTUjgeG3jHFOp6Jqtptc8xBlwz2hYgsUdWSZMfRl4baMQ2144Ghd0xD7Xhg6B3T3hyPFRMZY4yxZGCMMSb1ksG9yQ4gAYbaMQ2144Ghd0xD7Xhg6B1Tr48npeoMjDHGxJZqVwbGGGNisGRgjDEmdZKBiJwmIqtEZI2I3JzsePaViJSJyAcislxEliQ7nr0hIveLyDYR+U/UvJEi8g8R+dh7zktmjL3RzfHME5FN3ve0XETOSGaMvSUiE0RkgYh8KCIrROQGb/6g/J56OJ5B+z2JSIaIvCMi73nH9H1v/mQRWeSd854QkbQe95MKdQbeqGuriRp1Dbiky6hrg4qIlAElqjpob5Txhj2tAx5W1YO8eT8FdqjqXV7SzlPVbyczznh1czzzgDpV/VkyY9tbIjIWGKuq74pIDrAUOA83QuGg+556OJ6LGKTfk4gIkKWqdSISBN4EbgBuBP6qqo+LyD3Ae6r6f93tJ1WuDDpGXVPVFqB91DWTRKr6OrCjy+xzgYe81w/h/lEHhW6OZ1BT1S2q+q73uhbX6eR4Bun31MPxDFrq1HmTQe+hwIlA+7jye/yOUiUZ9NmoawOIAi+LyFIRuTrZwfSh0aq6xXtdAYxOZjB95FoRed8rRhoUxSmxiMgkYA6wiCHwPXU5HhjE35OI+EVkObAN+AewFqj2eo+GOM55qZIMhqJjVPVQ4HTga14RxZCirgxzsJdj/h+wHzAb2AL8T1Kj2Usikg38Bfi6qu6MXjYYv6cYxzOovydVDavqbFwv0EcA03u7j1RJBkNu1DVV3eQ9bwOexv0BDAVbvXLd9vLdbUmOZ5+o6lbvHzUC/J5B+D155dB/AR5V1b96swft9xTreIbC9wSgqtXAAuATQK6ItA9TsMdzXqokg45R17wa9YuB+UmOaa+JSJZX+YWIZAGnAv/peatBYz5wuff6cuDZJMayz9pPmJ5PM8i+J69y8g/AR6r6v1GLBuX31N3xDObvSUQKRSTXe52JayjzES4pfMZbbY/fUUq0JgLwmor9AvAD96vqD5Mb0d4TkSm4qwFwAxQ9NhiPR0T+BByP6253K/A94BngSaAY11X5Rao6KCpluzme43FFDwqUAV+JKmsf8ETkGOAN4AMg4s3+Dq6cfdB9Tz0czyUM0u9JRGbhKoj9uB/4T6rqHd554nFgJLAMuExVm7vdT6okA2OMMd1LlWIiY4wxPbBkYIwxxpKBMcYYSwbGGGOwZGCMMQZLBsbsRkTCUb1XLu/LXm5FZFJ0r6bGDBSBPa9iTMpp9G7tNyZl2JWBMXHyxpD4qTeOxDsisr83f5KI/NPr5OxVESn25o8Wkae9fubfE5GjvF35ReT3Xt/zL3t3jRqTVJYMjNldZpdios9GLatR1YOB3+DuaAf4NfCQqs4CHgV+5c3/FfCaqh4CHAqs8OZPBe5W1ZlANXBBQo/GmDjYHcjGdCEidaqaHWN+GXCiqpZ6nZ1VqGq+iGzHDZjS6s3foqoFIhICiqK7APC6Tf6Hqk71pr8NBFX1zn44NGO6ZVcGxvSOdvO6N6L7hwljdXdmALBkYEzvfDbq+W3v9Vu4nnABLsV1hAbwKvBV6Bh8ZER/BWlMb9kvEmN2l+mNGtXu76ra3rw0T0Tex/26v8Sbdx3wgIh8CwgBX/Tm3wDcKyJX4q4AvoobOMWYAcfqDIyJk1dnUKKq25MdizF9zYqJjDHG2JWBMcYYuzIwxhiDJQNjjDFYMjDGGIMlA2OMMVgyMMYYA/x/Gu2YwOAXxcgAAAAASUVORK5CYII=",
      "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(use_latent_classifier=True)\n",
    "network_with_classifier.plot_latent_dimension(\"Compressed AE with classifier\")\n",
    "network_with_classifier.plot_score(use_latent_classifier=True,model_name=\"Compressed AE with classifier\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- 0.09423089027404785 seconds ---\n"
     ]
    }
   ],
   "source": [
    "\n",
    "img = data_zoom.x_test[0].reshape(1,-1)\n",
    "start_time = time.time()\n",
    "network_with_classifier.autoencoder.predict(img)\n",
    "print(\"--- %s seconds ---\" % (time.time() - start_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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
}