QAE.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, Sequential\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, Activation\n",
    "from tensorflow.python.keras.regularizers import l2\n",
    "import os\n",
    "\n",
    "from tensorflow_model_optimization.python.core.sparsity.keras import prune, pruning_callbacks, pruning_schedule\n",
    "from tensorflow_model_optimization.sparsity.keras import strip_pruning, prune_low_magnitude\n",
    "\n",
    "from callbacks import all_callbacks\n",
    "from tensorflow.keras.regularizers import l1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qkeras.qlayers import QDense, QActivation\n",
    "from qkeras.quantizers import quantized_bits, quantized_relu\n",
    "from tensorflow.quantization import quantize"
   ]
  },
  {
   "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": null,
   "metadata": {},
   "outputs": [],
   "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": 3,
   "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_database 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",
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    "editable": false,
    "nbgrader": {
     "cell_type": "markdown",
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     "schema_version": 3,
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    }
   },
   "source": [
    "# Autoencoder using Keras\n",
    "The aim of this class is to implement a simple autoencoder for the MNIST data and then build an autoencoder that is\n",
    "able to classify MNIST data in its latent dimension.\n",
    "\n",
    "Code partially adapted from [Keras Documentation](https://blog.keras.io/building-autoencoders-in-keras.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
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    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "class QAutoencoder:\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.autoencoder = None\n",
    "\n",
    "        self.history = None\n",
    "\n",
    "        self.pruning_params = {\"pruning_schedule\" : pruning_schedule.ConstantSparsity(0.8, 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 = QDense(15,  name='fc1',\n",
    "                        kernel_quantizer=quantized_bits(5,0, alpha=1),\n",
    "                        bias_quantizer=quantized_bits(5,0, alpha=1),\n",
    "                        kernel_initializer='lecun_uniform')(self.input)\n",
    "        self.encoder = QActivation(activation=quantized_relu(5), name='relu1')(self.encoder)\n",
    "\n",
    "        if self.pruned:\n",
    "            self.encoder = prune_low_magnitude(QDense(15,  name='fc2_prun',\n",
    "                        kernel_quantizer=quantized_bits(5, 0, alpha=1),\n",
    "                        bias_quantizer=quantized_bits(5, 0, alpha=1),\n",
    "                        kernel_initializer='lecun_uniform'), **self.pruning_params)(self.encoder) \n",
    "            self.encoder = QActivation(activation=quantized_relu(5), name='relu2')(self.encoder)\n",
    "\n",
    "            self.encoder = prune_low_magnitude(QDense(15,  name='fc3_prun',\n",
    "                        kernel_quantizer=quantized_bits(5, 0, alpha=1),\n",
    "                        bias_quantizer=quantized_bits(5, 0, alpha=1),\n",
    "                        kernel_initializer='lecun_uniform'), **self.pruning_params)(self.encoder) \n",
    "            self.encoder = QActivation(activation=quantized_relu(5), name='relu3')(self.encoder)\n",
    "            \n",
    "        \n",
    "        if not self.pruned:\n",
    "            self.encoder = QDense(15,  name='fc2',\n",
    "                        kernel_quantizer=quantized_bits(5,0,alpha=1),\n",
    "                        bias_quantizer=quantized_bits(5,0, alpha=1),\n",
    "                        kernel_initializer='lecun_uniform')(self.encoder)\n",
    "            self.encoder = QActivation(activation=quantized_relu(5), name='relu2')(self.encoder)\n",
    "        \n",
    "        self.encoder = Dense(self.latent_dim,  activation='relu', name = 'encoder_output')(self.encoder)\n",
    "\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(QDense(16,  name='fc3',\n",
    "                        kernel_quantizer=quantized_bits(5,0,alpha=1),\n",
    "                        bias_quantizer=quantized_bits(5,0,alpha=1),\n",
    "                        kernel_initializer='lecun_uniform'), **self.pruning_params)(self.encoder)\n",
    "            self.decoder= QActivation(activation=quantized_relu(5,0), name='relu3')(self.decoder)\n",
    "\n",
    "        if not self.pruned:\n",
    "            self.decoder = QDense(16,  name='fc3',\n",
    "                        kernel_quantizer=quantized_bits(5,0,alpha=1),\n",
    "                        bias_quantizer=quantized_bits(5,0, alpha=1),\n",
    "                        kernel_initializer='lecun_uniform')(self.encoder)\n",
    "            self.decoder=QActivation(activation=quantized_relu(5), name='relu3')(self.decoder)\n",
    "\n",
    "        self.decoder = QDense(32,  name='fc4',\n",
    "                        kernel_quantizer=quantized_bits(5,0,alpha=1),\n",
    "                        bias_quantizer=quantized_bits(5,0, alpha=1),\n",
    "                        kernel_initializer='lecun_uniform')(self.decoder)\n",
    "        self.decoder = QActivation(activation=quantized_relu(5), name='relu4')(self.decoder)\n",
    "        \n",
    "        self.decoder = QDense(32,  name='fc5',\n",
    "                        kernel_quantizer=quantized_bits(5,0,alpha=1),\n",
    "                        bias_quantizer=quantized_bits(5,0, alpha=1),\n",
    "                        kernel_initializer='lecun_uniform')(self.decoder)\n",
    "        self.decoder = QActivation(activation=quantized_relu(5), name='relu5')(self.decoder) \n",
    "\n",
    "        self.decoder = QDense(self.input_shape[0],name='decoder_output',\n",
    "                        kernel_quantizer=quantized_bits(6,0,alpha=1),\n",
    "                        bias_quantizer=quantized_bits(6,0, alpha=1),\n",
    "                        kernel_initializer='lecun_uniform')(self.decoder)\n",
    "        #self.decoder = Activation(activation='sigmoid', name='sigmoid')(self.decoder)\n",
    "        self.decoder = QActivation(activation=quantized_relu(6, use_sigmoid=1), name='relu_decoder')(self.decoder) \n",
    "\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(QDense(16,  name='fc4_prunedclass',\n",
    "                        kernel_quantizer=quantized_bits(6,0,alpha=1),\n",
    "                        bias_quantizer=quantized_bits(6,0,alpha=1),\n",
    "                        kernel_initializer='lecun_uniform', \n",
    "                        kernel_regularizer=l1(0.0001)), **self.pruning_params)(self.encoder)\n",
    "            self.latent_classifier = QActivation(activation=quantized_relu(6,0), name='prunclass_relu4')(self.latent_classifier)\n",
    "        \n",
    "        if not self.pruned:\n",
    "            self.latent_classifier = QDense(16,  name='fc4_class',\n",
    "                        kernel_quantizer=quantized_bits(6,0,alpha=1),\n",
    "                        bias_quantizer=quantized_bits(6,0, alpha=1),\n",
    "                        kernel_initializer='lecun_uniform',\n",
    "                        kernel_regularizer=l1(0.0001))(self.encoder)\n",
    "            self.latent_classifier=QActivation(activation=quantized_relu(6), name='class_relu4')(self.latent_classifier)\n",
    "\n",
    "        self.latent_classifier = QDense(40,  name='fc5_class',\n",
    "                        kernel_quantizer=quantized_bits(6,0,alpha=1),\n",
    "                        bias_quantizer=quantized_bits(6,0, alpha=1),\n",
    "                        kernel_initializer='lecun_uniform', \n",
    "                        kernel_regularizer=l1(0.0001))(self.latent_classifier)\n",
    "        self.latent_classifier = QActivation(activation=quantized_relu(6), name='class_relu5')(self.latent_classifier)\n",
    "        \n",
    "        self.latent_classifier = QDense(self.num_classes,  name='classifier_out',\n",
    "                        kernel_quantizer=quantized_bits(16,6,alpha=1),\n",
    "                        bias_quantizer=quantized_bits(16,6, alpha=1),\n",
    "                        kernel_initializer='lecun_uniform', \n",
    "                        kernel_regularizer=l1(0.0001))(self.latent_classifier)\n",
    "        self.latent_classifier = Activation(activation='softmax', name = 'classifier_output')(self.latent_classifier)\n",
    "        \n",
    "    def build_autoencoder(self, use_latent_classifier=False):\n",
    "        \"\"\" Build the whole autoencoder Model from self.encoder and self.decoder.\"\"\"\n",
    "        self.dir()\n",
    "        \n",
    "        if self.encoder is None:\n",
    "            raise RuntimeError(\"The encoder has to be built before you can build the autoencoder!\")\n",
    "        #if self.decoder is None:\n",
    "        #    raise RuntimeError(\"The decoder has to be built before you can build the autoencoder!\")\n",
    "        \n",
    "        if use_latent_classifier:\n",
    "            if self.latent_classifier is None:\n",
    "                raise RuntimeError(\"If you want to use the option with the latent classifier, you have to build it \"\n",
    "                                   \"beforehand!\")\n",
    "            #self.autoencoder = Model(self.input, outputs=[self.decoder, self.latent_classifier])\n",
    "            #self.autoencoder.compile(loss=['mse', 'categorical_crossentropy'], loss_weights=[1, 0.1], optimizer='adam',\n",
    "            #                         metrics=\"accuracy\")\n",
    "            \n",
    "            self.autoencoder = Model (self.input, outputs = self.latent_classifier, name = 'classifier')\n",
    "            self.autoencoder.compile(loss = 'categorical_crossentropy' , optimizer='adam', metrics = \"accuracy\")\n",
    "\n",
    "            if not self.pruned:\n",
    "                self.autoencoder.save('model/QAE_model/KERAS_check_model_w_classifier.h5')\n",
    "            if self.pruned:\n",
    "                self.autoencoder.save('model/QAE_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/QAE_model/KERAS_check_model_wo_classifier.h5')\n",
    "            if self.pruned:\n",
    "                self.autoencoder.save('model/QAE_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/QAE_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.history = self.autoencoder.fit(self.x_train, self.y_train,\n",
    "                                                validation_data=(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/QAE_model/KERAS_check_best_model_classifier1.h5')\n",
    "            if self.pruned:\n",
    "                self.autoencoder.save('model/QAE_model/KERAS_check_pruned_best_model_classifier1.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/QAE_model/KERAS_check_best_model.h5')\n",
    "            if self.pruned:\n",
    "                self.autoencoder.save('model/QAE_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/QAE\")\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/QAE_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['accuracy'])\n",
    "            plt.plot(self.history['val_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/QAE/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/QAE/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/QAE/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()"
   ]
  },
  {
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "network = QAutoencoder(data_zoom, pruned=False)\n",
    "network.build_encoder()\n",
    "network.build_decoder()\n",
    "network.build_autoencoder()\n",
    "network.fit_data(epochs=40)\n",
    "network.plot_latent_dimension(\"QAutoencoder 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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "network = QAutoencoder(data_zoom, pruned=True)\n",
    "network.build_encoder()\n",
    "network.build_decoder()\n",
    "network.build_autoencoder()\n",
    "network.fit_data(epochs=40)\n",
    "network.plot_latent_dimension(\"Compressed QAE without classifier\")\n",
    "network.plot_reco(model_name=\" compressed\")\n",
    "network.plot_score(use_latent_classifier=False,model_name=\"Compressed QAE without classifier\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Repeating the same procedure for the autoencoder with classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "network_with_classifier = QAutoencoder(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=40, use_latent_classifier=True)\n",
    "network_with_classifier.plot_latent_dimension(\"QAutoencoder with classifier\")\n",
    "network_with_classifier.plot_score(use_latent_classifier=True,model_name=\"QAutoencoder with classifier\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quantized only classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"classifier\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "encoder_input (InputLayer)   [(None, 64)]              0         \n",
      "_________________________________________________________________\n",
      "fc1 (QDense)                 (None, 15)                975       \n",
      "_________________________________________________________________\n",
      "relu1 (QActivation)          (None, 15)                0         \n",
      "_________________________________________________________________\n",
      "prune_low_magnitude_fc2_prun (None, 15)                467       \n",
      "_________________________________________________________________\n",
      "relu2 (QActivation)          (None, 15)                0         \n",
      "_________________________________________________________________\n",
      "prune_low_magnitude_fc3_prun (None, 15)                467       \n",
      "_________________________________________________________________\n",
      "relu3 (QActivation)          (None, 15)                0         \n",
      "_________________________________________________________________\n",
      "encoder_output (Dense)       (None, 2)                 32        \n",
      "_________________________________________________________________\n",
      "prune_low_magnitude_fc4_prun (None, 16)                82        \n",
      "_________________________________________________________________\n",
      "prunclass_relu4 (QActivation (None, 16)                0         \n",
      "_________________________________________________________________\n",
      "fc5_class (QDense)           (None, 40)                680       \n",
      "_________________________________________________________________\n",
      "class_relu5 (QActivation)    (None, 40)                0         \n",
      "_________________________________________________________________\n",
      "classifier_out (QDense)      (None, 10)                410       \n",
      "_________________________________________________________________\n",
      "classifier_output (Activatio (None, 10)                0         \n",
      "=================================================================\n",
      "Total params: 3,113\n",
      "Trainable params: 2,625\n",
      "Non-trainable params: 488\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 1/40\n",
      "WARNING:tensorflow:AutoGraph could not transform <function Pruning.conditional_mask_update.<locals>.maybe_update_masks at 0x0000022483E45430> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: module 'gast' has no attribute 'Index'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function Pruning.conditional_mask_update.<locals>.maybe_update_masks at 0x0000022483E45430> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: module 'gast' has no attribute 'Index'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING:tensorflow:AutoGraph could not transform <function Pruning.conditional_mask_update.<locals>.maybe_update_masks at 0x0000022483E454C0> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: module 'gast' has no attribute 'Index'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function Pruning.conditional_mask_update.<locals>.maybe_update_masks at 0x0000022483E454C0> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: module 'gast' has no attribute 'Index'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING:tensorflow:AutoGraph could not transform <function Pruning.conditional_mask_update.<locals>.maybe_update_masks at 0x00000224F8999550> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: module 'gast' has no attribute 'Index'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function Pruning.conditional_mask_update.<locals>.maybe_update_masks at 0x00000224F8999550> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: module 'gast' has no attribute 'Index'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING:tensorflow:AutoGraph could not transform <function Pruning.conditional_mask_update.<locals>.maybe_update_masks at 0x00000224FF055DC0> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: module 'gast' has no attribute 'Index'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function Pruning.conditional_mask_update.<locals>.maybe_update_masks at 0x00000224FF055DC0> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: module 'gast' has no attribute 'Index'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING:tensorflow:AutoGraph could not transform <function Pruning.conditional_mask_update.<locals>.maybe_update_masks at 0x00000224FF055CA0> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: module 'gast' has no attribute 'Index'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function Pruning.conditional_mask_update.<locals>.maybe_update_masks at 0x00000224FF055CA0> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: module 'gast' has no attribute 'Index'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING:tensorflow:AutoGraph could not transform <function Pruning.conditional_mask_update.<locals>.maybe_update_masks at 0x00000224FAC7A3A0> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: module 'gast' has no attribute 'Index'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function Pruning.conditional_mask_update.<locals>.maybe_update_masks at 0x00000224FAC7A3A0> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: module 'gast' has no attribute 'Index'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "  2/235 [..............................] - ETA: 3:19 - loss: 2.3206 - accuracy: 0.1484WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0020s vs `on_train_batch_end` time: 1.7056s). Check your callbacks.\n",
      "234/235 [============================>.] - ETA: 0s - loss: 1.8872 - accuracy: 0.2998\n",
      "***callbacks***\n",
      "saving losses to model/QAE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 1.50337, saving model to model/QAE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 1.50337, saving model to model/QAE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00001: saving model to model/QAE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00001: saving model to model/QAE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 4s 15ms/step - loss: 1.8864 - accuracy: 0.3001 - val_loss: 1.5034 - val_accuracy: 0.4062\n",
      "Epoch 2/40\n",
      "209/235 [=========================>....] - ETA: 0s - loss: 1.3603 - accuracy: 0.4673\n",
      "***callbacks***\n",
      "saving losses to model/QAE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00002: val_loss improved from 1.50337 to 1.21450, saving model to model/QAE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00002: val_loss improved from 1.50337 to 1.21450, saving model to model/QAE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00002: saving model to model/QAE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00002: saving model to model/QAE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 6ms/step - loss: 1.3464 - accuracy: 0.4736 - val_loss: 1.2145 - val_accuracy: 0.5321\n",
      "Epoch 3/40\n",
      "224/235 [===========================>..] - ETA: 0s - loss: 1.1118 - accuracy: 0.5969\n",
      "***callbacks***\n",
      "saving losses to model/QAE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00003: val_loss improved from 1.21450 to 1.00293, saving model to model/QAE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00003: val_loss improved from 1.21450 to 1.00293, saving model to model/QAE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00003: saving model to model/QAE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00003: saving model to model/QAE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 6ms/step - loss: 1.1081 - accuracy: 0.5994 - val_loss: 1.0029 - val_accuracy: 0.6510\n",
      "Epoch 4/40\n",
      "228/235 [============================>.] - ETA: 0s - loss: 0.8838 - accuracy: 0.7171\n",
      "***callbacks***\n",
      "saving losses to model/QAE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00004: val_loss improved from 1.00293 to 0.77910, saving model to model/QAE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00004: val_loss improved from 1.00293 to 0.77910, saving model to model/QAE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00004: saving model to model/QAE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00004: saving model to model/QAE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 6ms/step - loss: 0.8818 - accuracy: 0.7174 - val_loss: 0.7791 - val_accuracy: 0.7640\n",
      "Epoch 5/40\n",
      "233/235 [============================>.] - ETA: 0s - loss: 0.7598 - accuracy: 0.7630\n",
      "***callbacks***\n",
      "saving losses to model/QAE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00005: val_loss improved from 0.77910 to 0.71367, saving model to model/QAE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00005: val_loss improved from 0.77910 to 0.71367, saving model to model/QAE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00005: saving model to model/QAE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00005: saving model to model/QAE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 2s 7ms/step - loss: 0.7602 - accuracy: 0.7630 - val_loss: 0.7137 - val_accuracy: 0.7898\n",
      "Epoch 6/40\n",
      "232/235 [============================>.] - ETA: 0s - loss: 0.7016 - accuracy: 0.7866\n",
      "***callbacks***\n",
      "saving losses to model/QAE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00006: val_loss improved from 0.71367 to 0.68579, saving model to model/QAE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00006: val_loss improved from 0.71367 to 0.68579, saving model to model/QAE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00006: saving model to model/QAE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00006: saving model to model/QAE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 2s 6ms/step - loss: 0.7017 - accuracy: 0.7865 - val_loss: 0.6858 - val_accuracy: 0.7883\n",
      "Epoch 7/40\n",
      "234/235 [============================>.] - ETA: 0s - loss: 0.6537 - accuracy: 0.8083\n",
      "***callbacks***\n",
      "saving losses to model/QAE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00007: val_loss improved from 0.68579 to 0.61161, saving model to model/QAE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00007: val_loss improved from 0.68579 to 0.61161, saving model to model/QAE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00007: saving model to model/QAE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00007: saving model to model/QAE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 6ms/step - loss: 0.6537 - accuracy: 0.8084 - val_loss: 0.6116 - val_accuracy: 0.8328\n",
      "Epoch 8/40\n",
      "232/235 [============================>.] - ETA: 0s - loss: 0.6123 - accuracy: 0.8265\n",
      "***callbacks***\n",
      "saving losses to model/QAE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00008: val_loss improved from 0.61161 to 0.57230, saving model to model/QAE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00008: val_loss improved from 0.61161 to 0.57230, saving model to model/QAE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00008: saving model to model/QAE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00008: saving model to model/QAE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 2s 6ms/step - loss: 0.6116 - accuracy: 0.8269 - val_loss: 0.5723 - val_accuracy: 0.8517\n",
      "Epoch 9/40\n",
      "230/235 [============================>.] - ETA: 0s - loss: 1.0872 - accuracy: 0.6695\n",
      "***callbacks***\n",
      "saving losses to model/QAE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00009: val_loss did not improve from 0.57230\n",
      "\n",
      "Epoch 00009: val_loss did not improve from 0.57230\n",
      "\n",
      "Epoch 00009: saving model to model/QAE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00009: saving model to model/QAE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 6ms/step - loss: 1.0881 - accuracy: 0.6693 - val_loss: 1.1380 - val_accuracy: 0.6334\n",
      "Epoch 10/40\n",
      "215/235 [==========================>...] - ETA: 0s - loss: 0.9989 - accuracy: 0.7119\n",
      "***callbacks***\n",
      "saving losses to model/QAE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00010: val_loss did not improve from 0.57230\n",
      "\n",
      "Epoch 00010: val_loss did not improve from 0.57230\n",
      "\n",
      "Epoch 00010: saving model to model/QAE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00010: saving model to model/QAE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "Epoch 00010: saving model to model/QAE_model/model_2\\KERAS_check_model_epoch10.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 6ms/step - loss: 0.9889 - accuracy: 0.7161 - val_loss: 0.8781 - val_accuracy: 0.7555\n",
      "Epoch 11/40\n",
      "233/235 [============================>.] - ETA: 0s - loss: 0.8528 - accuracy: 0.7621\n",
      "***callbacks***\n",
      "saving losses to model/QAE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00011: val_loss did not improve from 0.57230\n",
      "\n",
      "Epoch 00011: val_loss did not improve from 0.57230\n",
      "\n",
      "Epoch 00011: saving model to model/QAE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00011: saving model to model/QAE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 6ms/step - loss: 0.8529 - accuracy: 0.7620 - val_loss: 0.7945 - val_accuracy: 0.7848\n",
      "Epoch 12/40\n",
      "233/235 [============================>.] - ETA: 0s - loss: 0.7833 - accuracy: 0.7845\n",
      "***callbacks***\n",
      "saving losses to model/QAE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00012: val_loss did not improve from 0.57230\n",
      "\n",
      "Epoch 00012: val_loss did not improve from 0.57230\n",
      "\n",
      "Epoch 00012: saving model to model/QAE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00012: saving model to model/QAE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 6ms/step - loss: 0.7831 - accuracy: 0.7846 - val_loss: 0.7338 - val_accuracy: 0.8019\n",
      "Epoch 13/40\n",
      "221/235 [===========================>..] - ETA: 0s - loss: 0.7396 - accuracy: 0.7936\n",
      "***callbacks***\n",
      "saving losses to model/QAE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00013: val_loss did not improve from 0.57230\n",
      "\n",
      "Epoch 00013: val_loss did not improve from 0.57230\n",
      "\n",
      "Epoch 00013: saving model to model/QAE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00013: saving model to model/QAE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 5ms/step - loss: 0.7385 - accuracy: 0.7940 - val_loss: 0.7226 - val_accuracy: 0.8107\n",
      "Epoch 14/40\n",
      "220/235 [===========================>..] - ETA: 0s - loss: 0.7086 - accuracy: 0.8034\n",
      "***callbacks***\n",
      "saving losses to model/QAE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00014: val_loss did not improve from 0.57230\n",
      "\n",
      "Epoch 00014: val_loss did not improve from 0.57230\n",
      "\n",
      "Epoch 00014: saving model to model/QAE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00014: saving model to model/QAE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 5ms/step - loss: 0.7072 - accuracy: 0.8042 - val_loss: 0.6692 - val_accuracy: 0.8210\n",
      "Epoch 15/40\n",
      "233/235 [============================>.] - ETA: 0s - loss: 0.6796 - accuracy: 0.8109\n",
      "***callbacks***\n",
      "saving losses to model/QAE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00015: val_loss did not improve from 0.57230\n",
      "\n",
      "Epoch 00015: val_loss did not improve from 0.57230\n",
      "\n",
      "Epoch 00015: saving model to model/QAE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00015: saving model to model/QAE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 2s 7ms/step - loss: 0.6797 - accuracy: 0.8109 - val_loss: 0.6650 - val_accuracy: 0.8207\n",
      "Epoch 16/40\n",
      "227/235 [===========================>..] - ETA: 0s - loss: 0.6605 - accuracy: 0.8153\n",
      "***callbacks***\n",
      "saving losses to model/QAE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00016: val_loss did not improve from 0.57230\n",
      "\n",
      "Epoch 00016: val_loss did not improve from 0.57230\n",
      "\n",
      "Epoch 00016: saving model to model/QAE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00016: saving model to model/QAE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 6ms/step - loss: 0.6603 - accuracy: 0.8155 - val_loss: 0.6358 - val_accuracy: 0.8296\n",
      "Epoch 17/40\n",
      "233/235 [============================>.] - ETA: 0s - loss: 0.6415 - accuracy: 0.8234\n",
      "***callbacks***\n",
      "saving losses to model/QAE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00017: val_loss did not improve from 0.57230\n",
      "\n",
      "Epoch 00017: val_loss did not improve from 0.57230\n",
      "\n",
      "Epoch 00017: saving model to model/QAE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00017: saving model to model/QAE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 6ms/step - loss: 0.6414 - accuracy: 0.8234 - val_loss: 0.6183 - val_accuracy: 0.8277\n",
      "Epoch 18/40\n",
      "218/235 [==========================>...] - ETA: 0s - loss: 0.6227 - accuracy: 0.8317\n",
      "***callbacks***\n",
      "saving losses to model/QAE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00018: val_loss did not improve from 0.57230\n",
      "\n",
      "Epoch 00018: val_loss did not improve from 0.57230\n",
      "\n",
      "Epoch 00018: saving model to model/QAE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00018: saving model to model/QAE_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 [==============================] - 1s 5ms/step - loss: 0.6229 - accuracy: 0.8319 - val_loss: 0.6106 - val_accuracy: 0.8400\n",
      "Epoch 19/40\n",
      "205/235 [=========================>....] - ETA: 0s - loss: 0.6036 - accuracy: 0.8416\n",
      "***callbacks***\n",
      "saving losses to model/QAE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00019: val_loss did not improve from 0.57230\n",
      "\n",
      "Epoch 00019: val_loss did not improve from 0.57230\n",
      "\n",
      "Epoch 00019: saving model to model/QAE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00019: saving model to model/QAE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 2s 7ms/step - loss: 0.6031 - accuracy: 0.8421 - val_loss: 0.5904 - val_accuracy: 0.8512\n",
      "Epoch 20/40\n",
      "213/235 [==========================>...] - ETA: 0s - loss: 0.5955 - accuracy: 0.8451\n",
      "***callbacks***\n",
      "saving losses to model/QAE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00020: val_loss did not improve from 0.57230\n",
      "\n",
      "Epoch 00020: val_loss did not improve from 0.57230\n",
      "\n",
      "Epoch 00020: saving model to model/QAE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00020: saving model to model/QAE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "Epoch 00020: saving model to model/QAE_model/model_2\\KERAS_check_model_epoch20.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 6ms/step - loss: 0.5966 - accuracy: 0.8451 - val_loss: 0.5733 - val_accuracy: 0.8563\n",
      "Epoch 21/40\n",
      "214/235 [==========================>...] - ETA: 0s - loss: 0.5868 - accuracy: 0.8488\n",
      "***callbacks***\n",
      "saving losses to model/QAE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00021: val_loss did not improve from 0.57230\n",
      "\n",
      "Epoch 00021: val_loss did not improve from 0.57230\n",
      "\n",
      "Epoch 00021: saving model to model/QAE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00021: saving model to model/QAE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 6ms/step - loss: 0.5864 - accuracy: 0.8490 - val_loss: 0.5758 - val_accuracy: 0.8576\n",
      "Epoch 22/40\n",
      "232/235 [============================>.] - ETA: 0s - loss: 0.5831 - accuracy: 0.8489\n",
      "***callbacks***\n",
      "saving losses to model/QAE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00022: val_loss improved from 0.57230 to 0.55982, saving model to model/QAE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00022: val_loss improved from 0.57230 to 0.55982, saving model to model/QAE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00022: saving model to model/QAE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00022: saving model to model/QAE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 6ms/step - loss: 0.5832 - accuracy: 0.8489 - val_loss: 0.5598 - val_accuracy: 0.8626\n",
      "Epoch 23/40\n",
      "232/235 [============================>.] - ETA: 0s - loss: 0.5769 - accuracy: 0.8515\n",
      "***callbacks***\n",
      "saving losses to model/QAE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00023: val_loss did not improve from 0.55982\n",
      "\n",
      "Epoch 00023: val_loss did not improve from 0.55982\n",
      "\n",
      "Epoch 00023: saving model to model/QAE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00023: saving model to model/QAE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 6ms/step - loss: 0.5766 - accuracy: 0.8515 - val_loss: 0.5832 - val_accuracy: 0.8543\n",
      "Epoch 24/40\n",
      "235/235 [==============================] - ETA: 0s - loss: 0.5696 - accuracy: 0.8544\n",
      "***callbacks***\n",
      "saving losses to model/QAE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00024: val_loss improved from 0.55982 to 0.55643, saving model to model/QAE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00024: val_loss improved from 0.55982 to 0.55643, saving model to model/QAE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00024: saving model to model/QAE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00024: saving model to model/QAE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 2s 6ms/step - loss: 0.5696 - accuracy: 0.8544 - val_loss: 0.5564 - val_accuracy: 0.8637\n",
      "Epoch 25/40\n",
      "234/235 [============================>.] - ETA: 0s - loss: 0.5641 - accuracy: 0.8556\n",
      "***callbacks***\n",
      "saving losses to model/QAE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00025: val_loss improved from 0.55643 to 0.54610, saving model to model/QAE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00025: val_loss improved from 0.55643 to 0.54610, saving model to model/QAE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00025: saving model to model/QAE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00025: saving model to model/QAE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 2s 7ms/step - loss: 0.5641 - accuracy: 0.8557 - val_loss: 0.5461 - val_accuracy: 0.8655\n",
      "Epoch 26/40\n",
      "235/235 [==============================] - ETA: 0s - loss: 0.5636 - accuracy: 0.8558\n",
      "***callbacks***\n",
      "saving losses to model/QAE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00026: val_loss improved from 0.54610 to 0.53334, saving model to model/QAE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00026: val_loss improved from 0.54610 to 0.53334, saving model to model/QAE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00026: saving model to model/QAE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00026: saving model to model/QAE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 2s 7ms/step - loss: 0.5636 - accuracy: 0.8558 - val_loss: 0.5333 - val_accuracy: 0.8672\n",
      "Epoch 27/40\n",
      "232/235 [============================>.] - ETA: 0s - loss: 0.5535 - accuracy: 0.8594\n",
      "***callbacks***\n",
      "saving losses to model/QAE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00027: val_loss did not improve from 0.53334\n",
      "\n",
      "Epoch 00027: val_loss did not improve from 0.53334\n",
      "\n",
      "Epoch 00027: saving model to model/QAE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00027: saving model to model/QAE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 6ms/step - loss: 0.5538 - accuracy: 0.8593 - val_loss: 0.5451 - val_accuracy: 0.8639\n",
      "Epoch 28/40\n",
      "223/235 [===========================>..] - ETA: 0s - loss: 0.5532 - accuracy: 0.8579\n",
      "***callbacks***\n",
      "saving losses to model/QAE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00028: val_loss did not improve from 0.53334\n",
      "\n",
      "Epoch 00028: val_loss did not improve from 0.53334\n",
      "\n",
      "Epoch 00028: saving model to model/QAE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00028: saving model to model/QAE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 2s 7ms/step - loss: 0.5542 - accuracy: 0.8578 - val_loss: 0.5529 - val_accuracy: 0.8591\n",
      "Epoch 29/40\n",
      "232/235 [============================>.] - ETA: 0s - loss: 0.5459 - accuracy: 0.8602\n",
      "***callbacks***\n",
      "saving losses to model/QAE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00029: val_loss did not improve from 0.53334\n",
      "\n",
      "Epoch 00029: val_loss did not improve from 0.53334\n",
      "\n",
      "Epoch 00029: saving model to model/QAE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00029: saving model to model/QAE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 6ms/step - loss: 0.5465 - accuracy: 0.8599 - val_loss: 0.5598 - val_accuracy: 0.8609\n",
      "Epoch 30/40\n",
      "233/235 [============================>.] - ETA: 0s - loss: 0.5451 - accuracy: 0.8608\n",
      "***callbacks***\n",
      "saving losses to model/QAE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00030: val_loss did not improve from 0.53334\n",
      "\n",
      "Epoch 00030: val_loss did not improve from 0.53334\n",
      "\n",
      "Epoch 00030: saving model to model/QAE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00030: saving model to model/QAE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "Epoch 00030: saving model to model/QAE_model/model_2\\KERAS_check_model_epoch30.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 6ms/step - loss: 0.5451 - accuracy: 0.8608 - val_loss: 0.5356 - val_accuracy: 0.8686\n",
      "Epoch 31/40\n",
      "234/235 [============================>.] - ETA: 0s - loss: 0.5399 - accuracy: 0.8621\n",
      "***callbacks***\n",
      "saving losses to model/QAE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00031: val_loss improved from 0.53334 to 0.52716, saving model to model/QAE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00031: val_loss improved from 0.53334 to 0.52716, saving model to model/QAE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00031: saving model to model/QAE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00031: saving model to model/QAE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 2s 7ms/step - loss: 0.5400 - accuracy: 0.8620 - val_loss: 0.5272 - val_accuracy: 0.8689\n",
      "Epoch 32/40\n",
      "235/235 [==============================] - ETA: 0s - loss: 0.5357 - accuracy: 0.8632\n",
      "***callbacks***\n",
      "saving losses to model/QAE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00032: val_loss improved from 0.52716 to 0.51413, saving model to model/QAE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00032: val_loss improved from 0.52716 to 0.51413, saving model to model/QAE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00032: saving model to model/QAE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00032: saving model to model/QAE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 6ms/step - loss: 0.5357 - accuracy: 0.8632 - val_loss: 0.5141 - val_accuracy: 0.8716\n",
      "Epoch 33/40\n",
      "230/235 [============================>.] - ETA: 0s - loss: 0.5297 - accuracy: 0.8646\n",
      "***callbacks***\n",
      "saving losses to model/QAE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00033: val_loss did not improve from 0.51413\n",
      "\n",
      "Epoch 00033: val_loss did not improve from 0.51413\n",
      "\n",
      "Epoch 00033: saving model to model/QAE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00033: saving model to model/QAE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 5ms/step - loss: 0.5303 - accuracy: 0.8645 - val_loss: 0.5143 - val_accuracy: 0.8723\n",
      "Epoch 34/40\n",
      "227/235 [===========================>..] - ETA: 0s - loss: 0.5274 - accuracy: 0.8653\n",
      "***callbacks***\n",
      "saving losses to model/QAE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00034: val_loss improved from 0.51413 to 0.50660, saving model to model/QAE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00034: val_loss improved from 0.51413 to 0.50660, saving model to model/QAE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00034: saving model to model/QAE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00034: saving model to model/QAE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 2s 6ms/step - loss: 0.5265 - accuracy: 0.8656 - val_loss: 0.5066 - val_accuracy: 0.8741\n",
      "Epoch 35/40\n",
      "218/235 [==========================>...] - ETA: 0s - loss: 0.5231 - accuracy: 0.8661\n",
      "***callbacks***\n",
      "saving losses to model/QAE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00035: val_loss did not improve from 0.50660\n",
      "\n",
      "Epoch 00035: val_loss did not improve from 0.50660\n",
      "\n",
      "Epoch 00035: saving model to model/QAE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00035: saving model to model/QAE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 2s 7ms/step - loss: 0.5239 - accuracy: 0.8658 - val_loss: 0.5191 - val_accuracy: 0.8696\n",
      "Epoch 36/40\n",
      "234/235 [============================>.] - ETA: 0s - loss: 0.5205 - accuracy: 0.8675\n",
      "***callbacks***\n",
      "saving losses to model/QAE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00036: val_loss did not improve from 0.50660\n",
      "\n",
      "Epoch 00036: val_loss did not improve from 0.50660\n",
      "\n",
      "Epoch 00036: saving model to model/QAE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00036: saving model to model/QAE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 5ms/step - loss: 0.5207 - accuracy: 0.8675 - val_loss: 0.5311 - val_accuracy: 0.8665\n",
      "Epoch 37/40\n",
      "233/235 [============================>.] - ETA: 0s - loss: 0.5175 - accuracy: 0.8673\n",
      "***callbacks***\n",
      "saving losses to model/QAE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00037: val_loss did not improve from 0.50660\n",
      "\n",
      "Epoch 00037: val_loss did not improve from 0.50660\n",
      "\n",
      "Epoch 00037: saving model to model/QAE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00037: saving model to model/QAE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 2s 7ms/step - loss: 0.5169 - accuracy: 0.8675 - val_loss: 0.5098 - val_accuracy: 0.8694\n",
      "Epoch 38/40\n",
      "235/235 [==============================] - ETA: 0s - loss: 0.5212 - accuracy: 0.8655\n",
      "***callbacks***\n",
      "saving losses to model/QAE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00038: val_loss improved from 0.50660 to 0.50087, saving model to model/QAE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00038: val_loss improved from 0.50660 to 0.50087, saving model to model/QAE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00038: saving model to model/QAE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00038: saving model to model/QAE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 2s 6ms/step - loss: 0.5212 - accuracy: 0.8655 - val_loss: 0.5009 - val_accuracy: 0.8753\n",
      "Epoch 39/40\n",
      "224/235 [===========================>..] - ETA: 0s - loss: 0.5139 - accuracy: 0.8672\n",
      "***callbacks***\n",
      "saving losses to model/QAE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00039: val_loss did not improve from 0.50087\n",
      "\n",
      "Epoch 00039: val_loss did not improve from 0.50087\n",
      "\n",
      "Epoch 00039: saving model to model/QAE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00039: saving model to model/QAE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 6ms/step - loss: 0.5143 - accuracy: 0.8673 - val_loss: 0.5123 - val_accuracy: 0.8745\n",
      "Epoch 40/40\n",
      "219/235 [==========================>...] - ETA: 0s - loss: 0.5116 - accuracy: 0.8686\n",
      "***callbacks***\n",
      "saving losses to model/QAE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00040: val_loss did not improve from 0.50087\n",
      "\n",
      "Epoch 00040: val_loss did not improve from 0.50087\n",
      "\n",
      "Epoch 00040: saving model to model/QAE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00040: saving model to model/QAE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "Epoch 00040: saving model to model/QAE_model/model_2\\KERAS_check_model_epoch40.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 6ms/step - loss: 0.5125 - accuracy: 0.8683 - val_loss: 0.5198 - val_accuracy: 0.8716\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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1BtxeB2313nPdrufOZhhXBLkzIPcgyDkIsqdCfOJeP2K/1GyAkqdg9V/cwTIU8cmQWQyZk2DWae4MObMYMgogIdUrYkkOeA54Hefr30yvX2nt6qG10z06e/zoIaB+xddcRkrtKpJrVpNas4rUdX8jYcXuI6S2xGezM76IHVJAueaQ6W+kwF/JBH8V+f4a4gNGvOwmno1Jc9iWdwU784+mc8JCcjLSyctIIk8TSartoL6ti9qWLurbeqlrVWpb4qhvi6e2NYnmjgLau/Jp6zqMtq4e2rp66ejuZrzWUyzVnNZxKNcMyx8kaFeHYZvGmL3p6XIH47TcfV+3sxlWPQE713oH/BrvuQ5aa6C3c/B1k7Pc2XJiGpS9787M+0icO+jmzoC8mVB0OEw8wp1Rx+1Hu5K6LVDyF1j9FFStAgQmHwNn/Nydbcf53Jl4nA/i4ne99iWg6QW0J2TR0tVLa2dv/0G8tauHlpZemju6ae7ooaWjh+aONpo7mmjq6KG5o5sWb9kWb7327r0NTTwOONZ7QAZtTJYqpkgVU+N2MkOqmaY7OVhXcZy/hiZfFrXxE9ieNJePEgqojS+gJr6AmoRCdkou1W1KTUsX1ZWddC3ftNfdlJboIyc9kZzURMalJJCfnkRqoo+UxHhSE33eax+pCT7mT8ra979DCEbdeASLFi1S63TOjDpN5VD2AZS+D2XLoGKFKw6YfCwsuBDmngvJmUNvo2oNLLsHPn4MulogKdMlkr4ikNTcgCKR3F3vU3Lcc3IW+ILO/drqXFFN3abdnrVmA9Ld6pZJznRJoXiRSwwTj4C0fBdDa43bRlst/tYaOhp30tFQSVLZW6TVrASgLmchWwtOY0PuydT68mjv6qWl0x3EWzrdo9l73do3vauHUA5NcQIZyQlkJMfvek6KJ817pCf5vOdd0xJ9ccQJxIkgAc8iQpxAelI82amJZKcmkpEcT1xcQG85qiAD9Z6zJ1WlpbOHmpYuals6qWnppLPHT3ZqIjlpieSmu89ITvDtfWPDQEQ+VNVFA86zRGDMAejphI4m1xqls8mdrfe9b62GHR+5A39TmVvel+jKoScthqQMd2ZfuwF8SXDwmbDgIjjoZPAleNvvgrXPwgf3wPa33XLzzoMjvwITDw/5oDSQXr+yo76dTTUtbK5uZXN1C5uq3evalnbmJ1ZyZMIWFsZtYo5/A5N6tuDD79aVeHy6R7dgAHSrjzU6hed6j+b53qMpJ2+3+SKQnhhPerI7QPc9ZyTHk5boDtYZyd6BO9EXcFDfdXDPSE4gPcmdMcsB7INYYonAmOHQ3uAO6mXvuzP7HR+6A/5QMifDpCNdZWXxYiiYt3vTRVUo/8id5a96wpXhp+bBoV9yxTcfPegqELOnwqKr6Zh3ETu6Uiirb6eutdMVjfSdUXf0nV27YpNev+JXxa/u7NSv9L/v6umltL6drh7/rlBTEpien8b0vHQKM5Np6eyhqb2bRu/R2d5CQdsnHNS1jhxtpDMxi96UHCQ1j7j0PJIz80nJHE9mVi6ZqYmkJPpISfD1P6cm+khO8JEUH2cH7wiwRDDW1W5yB5S8GZGOZGxpKIXNr+068Fevc9MlzrX+KF7kmvIlZULyOEga587y+16nZEFKduif19NF57qX6F7+R1K3vIT4e9iUfTz/SPscL3fOpbShk53NA5f/x8dJf/FIunf2nBAvXrGH9BeFuFIOIcEnTMpJZXpeGgeNT2d6Xho5aYl2gB7DhkoEVlk82q16Ap653jV9u/a1SEczNtRshDd+ASsfd80Zk7NcUc68L7rniYe7A/4A/H6ltL6N9RXNbKutx691rvyZ3cuhBejq9VPe0EF5Qzvlje2UN3RQ1yrAJYzjXJLopq4qm6KsZCZlJ3Di7HFMyk6lOCeF4uxU8tOT+otV7CzbHAhLBKOV3w+v/RSW3u5aWzRsj3REo1/1elj6C1j9hCuLP+o6OOJyyJ25R6uZXr9S0djOhp0tbKhqZn1lC59UNbNxZ0sIrVR2SUv0MTE7haKsFOYXZzExK4WirGSKMt20wsxk4n3WE4wJL0sEo1FXKzx1natEPOxSyCiEpT93FZcjfev8WFC1xu2/kqcgIQWOuQGOvZG2xBw2V7eyvaSK7XVtlNa19T/vaGinu3dXser4jCRmTcjgosWTmTUhnVkFGRyUl05CvPSX0SugflBcOX28T8hIirczeRNxlghGm8Yd8OiFULkKPvtfcMz1sNy7Aaa5wlUqmtBUrYHXb4M1z0BiOhx/ExxzA2Vdqdz32lYee/8jWrt2nd1npyYwKSeVuRMzOX1eIZNyUpiRn86sCRlkp4XpRixjRoAlgtGkbBk8djF0tcHFj8Osz7rpGYXuubnSEkEoGsvg1Z/Aij+6BHDCd+CY61lV5+POZzfzwqoKAM6eX8jp8wqYlJPKpJxUxiUnRDhwY8LDEsFoseoJePrr7vb6Lz/jKof7jPMSQVP5wOsap70e3vglvPcHQOGY6/Ef921eK+vhzofX8+7mOtKT4rn6+GlccexUirJSIh2xMSPCEsFosPwReObrMOU4OP+hPbsl6L8iqBj52EaD7g54/w/wxn+7m70WXEjj0f/Gs1t9PHDnajbubKEoM5nvn3kIFyyeZGf+JuZYIoh23e3wyo9h0lFw2dMDdwqWku1aucRSImiqgBWPwIZ/uBuv0vLcjVhpeQGv8133v6/+BJrK8M/4DO9Pv5EHNmfwyu8+oavXz7yJ4/j1hQs589BCEqx1jolRlgii3Qf3uP7Xv3jfgEmgo7uXn76wlm8l5JHVNMYTQW8PbPyHu9v2kxddG/+iw6Gj23XT0FoLff3jBGjPn8+fZ93KbzYXULO6ldy0bi49egrnHTGRuUV76d/HmBhgiSCadbbAm/8D00+EqcftMbuysYOvPrSMj8saOTclk8OaK0c+xpFQvxU+eshdATRXQNp4OPZGOPzLrgvlQF1t0FZDdWUZH6zZwBubGnm0dDqJ5T5OOSSH8w4v5tOz8+3s35gAlgii2Qd3uS6GT/r+HrM+2l7PVx/6kLbOHk6YmUfZ1nEsaCofPYNQ+/2w4SV47w43sEd/N8RxAa/jXQ+d5R+5bh1mnApn/sL1Te/bsxy/rrWL51dV89cV5by/tR7IY8GkGfy/4ydy9vwia+JpzCAsEUSrjiZ469fu4Ddp8W6z/ryslO8/tZqCzGQevvooNuxspmJLDjSv2KduciOiuwNW/gne+Z0rvx9X7Prs0V6XHPw93mtv1KrENJcIF17sBiUJ0tLZw0sllTz7cTlvbKih16/MHJ/Ov546i7MXFDE1Ly0CX9KY0cUSQbR67w+uueNJ3+uf1NPr5ycvrOPet7Zw3IxcfnfR4WSnJdLd62eFZhPX0wEdja6zs2jTVufqO97/g+ueuWA+nHcPzDlnwLP7gfT6lc3VLXxc1siqsgZW7mikpLyJrh4/E7NSuPZT0/n8giIOLsiwu3WN2QeWCKJRewO881uYfabr4AxoaOvixkeX88aGGq48birfP/OQ/j5opualUaVeL5fNFdGVCGo3wbv/5+5+7ml3VzjHfQOmnrDXK5eGti7e2FDDitIGVpU1srq8kTbvTt+0RB9zJ2ZyxbFTOW3uBA6fnG0Hf2P2kyWCaPTO792ZvXc1sHFnC1c/8AEVDR3c/sX5nL9o0m6LpyfF05EyAXpxN5UF3mwWCV1trtuG5Q/BtrcgLgHmX+C6w5gwZ9DVVJVPqlp4ZV0Vr67byYfb6vErJMXHMbdoHOcvmsT84kzmF2cyLS8dX5wd+I0ZDmFNBCJyOvBrwAfcraq3Bc3PBB4GJnux/EJV7wtnTFGvrc6dQc85BwoOpaS8kcvueZ84ER699miOmDJw//bJ2ROhBtfNRCT0DbDy0UOw+kk3YEvOdDjlR658P6NgwNU6unt5Z3Mt/1y7k3+u28mOhnYA5haN44aTZnDSweM5dGKm9cBpTBiFLRGIiA/4PXAqUAZ8ICLPquqagMWuB9ao6tkikg+sF5FHVLUrXHFFvbd/48aCPfG7fFzawGX3vEd6UjyPXHM004ao+EzPn+QlghHuZqKzxRX7fPQg7CyB+BQ3/u5hl8GUYwct/qlq6uD+t7fyx/e209jeTUqCj+Nm5HHDyTM4afZ4CjKTR/Z7GBPDwnlFsBjYqKqbAUTkMeAcIDARKJAhrnA3HagDBh4INRa0VMN7d8K88/igbQJX3vce2WkJ/PErRzMpJ3XIVYvH51C/Jp30+h2MWAcJlavhz5dD7UYoOgzO+iUc+sUhB2FfU97E3W9u5q8fl9PrV06bW8D5R07imOm5IzaItzFmd+FMBBOB0oD3ZcBRQcv8DngWKAcygAtU1R+0DCJyLXAtwOTJk8MSbFR461fQ085H077Kl+95n8KsZP74laNDOjue5lUY++pGIBGouiuAv93sRu+6/K8w7VODLu73K69/Us3db27mrY21pCb6uOSoKVx13DQm5w6d4Iwx4RfORDBQmUDwAMmnASuAk4GDgH+IyBuqutuI4Kp6J3AnuDGLhz/UKNBcCR/cTcWUc7jwqVqm56Xx0NVHkZ8R2kAzU3NdIihoDHPRUGcLPPctWPU4TD8JvnAXpOcPuGhrZw9Pr9jBfW9tZePOFgrGJXPrGQdz0ZGTyUy1jt2MiRbhTARlQGDzlmLcmX+gK4HbVFWBjSKyBTgYeD+McUVGR5O7QSo5E+IGKAJ583/w93Zz6YZPM6sgnYeuOmqf7oSdmpfKKs3hiLaSYQw6SFUJPH451G2Ck/4dTvjXPYZwBNhQ1czD727jyY920NLZw9yicfzPBQs469AiEuOt0teYaBPORPABMFNEpgE7gAuBi4OW2Q6cArwhIhOA2cDmMMYUGRtfhkcvht5O9z4pE1IyXbFKShYkZ9G7/u880fMpMifO4r4rF5OZsm9nzKmJ8bQm5pHaVes6Z/MN459W1VUIv/BvkDzOjYcQVBTU3evnpZIqHnp3K+9uriPRF8dZ8wu59OgpHD45y9r4GxPFwpYIVLVHRG4AXsQ1H71XVUtE5Dpv/h3AfwD3i8gqXFHSLapaE66YIqLiY3cWnTcLDrvE3SzW0bDbc1NZCZU9+SwtvJKHrj6KtKT9+7P0phcS1+SH1p0wrmh44u/ugOdugo8fdQf/L9wNGRP6Zze2dXPPW1t47P3t7GzupDg7hVtOP5jzFxWTm27jJxszGoT1PgJVfQF4IWjaHQGvy4HPhjOGiGoohUfOd2f+l/x510hiAZ5fWcGNj37E0dNzuefyI0lJ3P+WM4nZRdCEu7t4uBLBC99xSeDTt8Knb96tWKuutYuL73qX9VXNnDR7PJcdPYVPzcq3G72MGWXszuJwaa+HR77oBpa5+sUBk8A/1lTxzceWc8SUbO6+fNEBJQGAtPxJsA3aa8tImXjEAW0LcC2Dlj8En/o3OOm7u82qbenkkrvfY0tNKw9etZgTZg5cYWyMiX5WcxcOPZ3w2KVQtxkufGTALh9e/6Sa6x/5iLkTM7n3iiNJTTzwnJxTMAWAusptB7wtylfA899xLYNOHDwJ3HP5kZYEjBnl7IpguPn98PTXYNubrnfNaSfsscjbm2q49sFlzBifzoNXLiZjmMbILSqaRI/G0VpTdmAbaquDxy9zQz2ed89uxUE1LZ1cctd7bKtr5d4rjuS4GXkHGLUxJtIsEQy3V5a4vnY+s8TdZRtk2dY6rr5/GVNyU3n4K0cNa3v6KXnj2EkWPQ079n8jfj889VU3JvBVf4e03P5ZNS2dXHzXu2yva+Pey4/kWEsCxowJVjQ0nN6/yw0ms+hqOO6mPWavKG3givs+oDAzmYe/chQ5wzxiVkqij7q4XHytBzB28Rv/7UYOO/2nbsAYT3VzJxfdaUnAmLHIEsFwWfeC63Jh1hlwxu17dLZWUt7Il+9xfQc9cs1RjM8IT6dqbUnjSemo3r+VN74Cr/6X6zL6yK/0T65udlcCpfVt3HuFJQFjxhpLBMNBFZ69wY269cV79riZq7alk8vueZ/0pHj++JWjKcxMCVsoPWkFZPfux60YDaXw5Fdcxfbn/qc/kTW0dXHRXe9SVt/OfVcs5tiDLAkYM9ZYIhgOdZuhrRYWXenG2A1y5xubaWjr4r4rF++1F9EDFZ9ZSAZtNDY2hL5STyc8/mXXBcYFD+/2Hf7v9U1srm7hnisWccxBuUNsxBgzWlkiGA7ly91z0WF7zKpt6eShd7Zx9oIiZhdkhD2UlFzXvVN56ZbQV/r7d92gMuf+L+Qe1D+5rrWrP3a7EjBm7LJEMBwqVoAvEfL3vF/grje20N7dy40nzxyRULK9ewlqK7aGtkJDKSy7B47+Ohxy9m6z7n5jsxf7jGGO0hgTTSwRDIfyFTBhHsTv3gqorrWLB9/Zytnzi5gxPn1EQskvcomgubp0L0t6Kj52z/PO221yfWsXD7y9lbMOLWTG+PBfyRhjIscSwYHy+93BtGjhHrPu8s6ov3HKyJ1RJ2UXA9BVH+K9BFWrQeJg/O6Dyt/71hZau3r5xikjcyVjjIkcSwQHqn6LG6i9cOFuk+u8M+rPzS8a2TPq5HG0SwrSEuK9BJWrIOcgSNxVid3Y1s39b23lzEMLmDXBrgaMGessERyoQSqK+8rXvxGB8vWWxDyS2neGtnDlSig4dLdJ9761hebOnhGr1zDGRJYlggNVvhx8Sbt1LFcfcDUwMwJn1N2pBeT6a6lv7Rp6wfYGaNgOBfP6JzW2d3PvW1s4be4EDikcF95AjTFRwRLBgSpf4Q6kvl19Bt395mbaInQ1ACDjCimQOrbUtg69YJU3rGXB/P5JD7y9leaOHqsbMCaGWCI4EH0VxQH1A/WtXdz/lmttE4mrAYDknGLGU8/W6pahF6xa7Z69oqHmjm7ueXMLnzlkAnOLMsMcpTEmWlgiOBB1m6Crebf6gXve3OKuBiJ4Rp2RP5lE6aWqqnzoBStXQmoepLuhJx98ZxuN7d18064GjIkplggORPkK9+w1HW1o6+L+t7dy5qGFEW1tE5/pRkNrrNo+9IKVq9zVgAgtnT3c9cZmTj54PIcW29WAMbHEEsGBqFgB8cmQfzDgrgZaOnv4RqRb23jjFXfWDzFATW837FzXX1H80DvbaGjrtroBY2KQJYIDUb7c3VHsS3Cdynl1AyPRp9CQMtwVgTaVo6oDL1OzAXo7oWA+bV3uauDTs/JZOClr5OI0xkSFvSYCEblBRLJHIphRpf+OYlc/0Hc1cOMI3kU8KK/MP7O7lrrBmpAGVBQ//O426lq77GrAmBgVyhVBAfCBiDwuIqeLBI24EqtqN0JXCxQtRFV5+N1tnDZ3AgcXREHb+/hEupJymSB1bB2sCWnlSvAl0T5uOncu3cwJM/M4Yorle2Ni0V4Tgar+OzATuAe4AtggIj8RkYOGXHGsq1jhnosOo6y+nfq2bj41Kz+iIQXSjEIKpJ4tNW0DL1C5CsYfwqPLyqlpsasBY2JZSHUE6gqaK71HD5ANPCEit4cxtuhWvhziUyBvNiXljQDMi6K29wnZRUyQerbWDHBFoAqVq+kZP5c/LN3E0dNzOHJqzsgHaYyJCqHUEXxDRD4EbgfeAg5V1a8BRwDn7WXd00VkvYhsFJFbB5j/byKywnusFpFeERkdR6T+O4rjKSlvwhcnka8kDhA3roiiuIaB7y5uroS2GpZ3TaKqqdP6FDImxsXvfRHygC+o6rbAiarqF5HPDbaSiPiA3wOnAmW4eoZnVXVNwDZ+DvzcW/5s4FuqWrfvX2OE+XtdRfFhlwCwekcjM/LTSU7wRTiwABlFZNNIWXXDnvO8iuL7NmVw+OQsjrUhKI2JaaEUDb0A9B+cRSRDRI4CUNW1Q6y3GNioqptVtQt4DDhniOUvAh4NIZ7Iq90I3a39XUuUlDcxtygKKokDZRQA0Fq7Y88mpJUrAXijaQI3njwTq/83JraFkgj+DwjstKbVm7Y3E4HAYbLKvGl7EJFU4HTgyUHmXysiy0RkWXV1dQgfHWb9dxQfxs7mDnY2dzJ3YvTUDwD9N5WN666hpmX3JqT+ilWUywSmTCzgxNnRU8FtjImMUBKBaMAppar6Ca1IaaDTzEHubuJs4K3BioVU9U5VXaSqi/Lzo+DA1V9RPIuS8iaAqL0imCD1ezQhbd2+glU9k7jhJLsaMMaElgg2exXGCd7jm8DmENYrAyYFvC8GBusF7UJGS7EQuKajhfPBF88aLxHMibpE4K4ICqSOLQEth/wdLaS1bKUqdSafnTMhUtEZY6JIKIngOuBYYAfu4H4UcG0I630AzBSRaSKSiDvYPxu8kIhkAp8Gngk16Ijqqyjurx9oZHJOKuOSE4Zeb6Sl5qC+RArjdm9C+u57bxKHMnvhscTF2dWAMSaEIh5V3Yk7iO8TVe0RkRuAFwEfcK+qlojIdd78O7xF/wV4SVX3MopKlKjZAN1t/V1LlJQ3MW9ilF0NAIggGQVMa2nmKa9oSFX58L2lHAssOupTkY3PGBM19poIRCQZuBqYCyT3TVfVq/a2rqq+gGt1FDjtjqD39wP3hxRtNOgfo3ghTR3dbKtt4/xFk4ZeJ1Iyiihub+i/u/jV9TvJblpHV3IGidlTIhycMSZahFI09BCuv6HTgNdxZf3N4QwqqlWsgIRUyJsVvfUDfcYVMp46ttW2oqr85pWNLEgsI77IjUFgjDEQWiKYoao/AFpV9QHgLODQ8IYVxcqXuzF+43zR22KoT0Yh43pqaOvq4S8f7eDj0joOlu3EFc7f+7rGmJgRSiLo9p4bRGQekAlMDVtE0czf6zpr66sf2NHI+Iwkxmck72XFCMkoJKG3nQza+c/n17Aoo4GE3vb+MYqNMQZCSwR3euMR/Duu1c8a4GdhjSpa1XziVRQvBKL0juJA3k1l46We+rZuvn6I1xPphHkRDMoYE22GrCwWkTigSVXrgaXA9BGJKlr1VRQXLqSju5eN1S2cGs1t8b2byop99TQmT+P49EqIi+8fWtMYY2AvVwTeXcQ3jFAs0a98BSSkQd5M1lc20+vX6L4i8Ias/NKseH509lwSqksgbxYkRGlRljEmIkIpGvqHiHxHRCaJSE7fI+yRRaPy5e6O4jgfq/vGIIi2PoYCeYngc9Pg7AVFrn7D6geMMUFC6TOo736B6wOmKbFWTNTb4w6ki64EXP3AuOR4irNTIhzYEBJTITkTmiqgtRaayy0RGGP2EMqdxdNGIpCoV7Meetp363p6TtG46O+0LaMImiugapV7bxXFxpggodxZ/OWBpqvqg8MfThQL6Hq6p9fPuoomLj16FNydm1HgEkGllwjsisAYEySUoqEjA14nA6cAHwExlgiWQ2I65M5g085WOnv80dnHULBxRbBpPVSudnUGaXmRjsgYE2VCKRq6MfC911voQ2GLKFpVfOzdURzXP1j93CgarH5QGYXQUuW6xrCrAWPMAEJpNRSsDYit0c79vW6cX69rhpLyJpLi45ielxbhwEKQUQDaC9XrLBEYYwYUSh3BX9k1slgcMAd4PJxBRZ3aTe6O4gKXCFbvaOSQwnHE+/Ynj44w7+5iwCqKjTEDCqWO4BcBr3uAbapaFqZ4opM32DuF81FV1lQ08fkFRUOvEy28ewmA/kRmjDGBQkkE24EKVe0AEJEUEZmqqlvDGlk0qfgYfImQfzClde00d/SMjvoB2JUIElIhx1oCG2P2FErZxp8Bf8D7Xm9a7KhcCeMPAV9CQEXxKGgxBJA+HiQOJsyFOF+kozHGRKFQEkG8qnb1vfFeJ4YvpCijChUrd9UPlDfiixNmF2REOLAQxflg/FyYekKkIzHGRKlQioaqReTzqvosgIicA9SEN6wo0rQD2uugcAHgWgzNHJ9OcsIoOru+5p92NWCMGVQoieA64BER+Z33vgwY8G7jMSnojtyS8iZOmDnKbsqKj50LOGPMvgvlhrJNwNEikg6IqsbWeMUVKwGBCfPY2dRBdXPn6KkoNsaYEOy1jkBEfiIiWaraoqrNIpItIv85EsFFhcqVkHsQJKX3j1E8b7RUFBtjTAhCqSw+Q1Ub+t54o5WdGbaIok1ARXFfi6E5lgiMMWNIKInAJyJJfW9EJAVIGmL5saOtDhq379a1xJTcVDKSEyIcmDHGDJ9QKosfBl4RkftwXU1cRaz0PNpfUbyr6eih0TwimTHG7IdQKotvF5GVwGcAAf5DVV8Me2TRoL9riQU0tndTWtfOhUdOjmxMxhgzzELqNU1V/66q3wF+COSLyPOhrCcip4vIehHZKCK3DrLMiSKyQkRKROT10EMfARUr3QhfaXms8SqKR80dxcYYE6JQWg0lisi5IvI4UIEbmOaOENbzAb8HzsD1WHqRiMwJWiYL+F/g86o6F/jSPn+DcKpcGXD/wCgag8AYY/bBoIlARE4VkXuBLcAXcYPR1Knqlar61xC2vRjYqKqbvW4pHgPOCVrmYuAvqrodQFV37s+XCIvudqj5ZLeK4vEZSeRnxEY9uTEmdgx1RfAicBBwvKpe6h38/UMsH2wiUBrwvsybFmgWkC0ir4nIh4ONjywi14rIMhFZVl1dvQ8hHICqNaD+/oritRVN1mzUGDMmDZUIjgDeBV4WkX+IyNXAvnRYIwNM06D38d7nnAWcBvxARGbtsZLqnaq6SFUX5efn70MIB6DyY/dcOJ+uHj8bd7ZwSKElAmPM2DNoIlDV5ap6i6oeBCwBDgMSReRvInJtCNsuAyYFvC8GygdY5u+q2qqqNcBSYMG+fIGwqVgJyZmQNYVN1S30+JWDR0uPo8YYsw9CbTX0lqregCva+RVwTAirfQDMFJFpIpIIXAg8G7TMM8AJIhIvIqnAUcDaUIMPq0rvjmIR1la4FkNz7IrAGDMGhXJDWT9V9ePqDvZ6H4Gq9ojIDd6yPuBeVS0Rkeu8+Xeo6loR+TuwElf/cLeqrt7XLzHsenugqgQWXQ3AuspmEn1xTBsNg9UbY8w+2qdEsK9U9QXghaBpdwS9/znw83DGsc9qN0BPR3+LobUVTcyckD46Bqs3xph9ZEe2gVR4dxR7LYbWVTZzcIEVCxljxqYhE4GIxIlI5ItqRlrlSvAlQd5Malo6qW7u5JBCqyg2xoxNQyYCr07gYxGJrQ52KlfChDngS2B9pRuHx5qOGmPGqlDqCAqBEhF5H2jtm6iqnw9bVJHUN1j9HHcTdF+LIWs6aowZq0JJBD8OexTRpLEUOhoCKoqbyc9IIjfdupYwxoxNoXRD/bqITAFmqurLXnv/fbnDeHTpryh297Wtq2yyqwFjzJgWSu+j1wBPAH/wJk0Eng5jTJFVuRIkDibMpafXz4Yq61rCGDO2hdJ89HrgOKAJQFU3AOPDGVREVayE3JmQmMqWmla6ev12RWCMGdNCSQSdXjfSAIhIPHt2Hjd2VK7srx9Y41UU2xWBMWYsCyURvC4i3wNSRORU4M9AKOMRjD6ttdC0o38wmnWVzcTHCQflp0c4MGOMCZ9QEsGtQDWwCvgqrsuIfw9nUBHT1/V03x3FFU3MGJ9OYrzdgG2MGbtCaTXkB+7yHmNb5Sr3XNjXYqiZo6blRDAgY4wJv0ETgYg8rqrni8gqBqgTUNX5YY0sEipWwrhiSM2hoa2LisYODrb6AWPMGDfUFcFN3vPnRiCO6BBQUbzOupYwxsSIoQq/n/Oe/1NVtwU/RiK4EdXVCjUbdhujGOAQazpqjBnjhroiSBSRy4FjReQLwTNV9S/hCysCqkoA3XVFUNFMTloi+RnWtYQxZmwbKhFcB1wCZAFnB81TYGwlgoqgFkNe1xIiEsGgjDEm/AZNBKr6JvCmiCxT1XtGMKbIqFoNyVmQWUyvX1lf1czFi6dEOipjjAm7oVoNnayq/wTqY6ZoqOBQEGFbTQsd3X4OtsFojDExYKiioU8D/2TPYiEYa0VDfj9UrYHDLwNc19MAc6zFkDEmBgxVNPQj7/nKkQsnQuq3QHcrTJgLuPqBOIEZ461rCWPM2BdKN9TfFJFx4twtIh+JyGdHIrgRU1Xinr1EsLaimen56SQnjN1hF4wxpk8onehcpapNwGdx3U9fCdwW1qhGWlUJIJB/CGCD0RhjYksoiaCv/eSZwH2q+nHAtLGhajXkHgSJqTR1dFNW3253FBtjYkYoieBDEXkJlwheFJEMwB/esEZYVQlMmAfAJ/1dS9gVgTEmNoSSCK7GdUV9pKq2AQm44qG9EpHTRWS9iGwUkVsHmH+iiDSKyArv8cN9in44dDa7ymIvEfR1LXFwgV0RGGNiw167oQaOAVaoaquIXAocDvx6byuJiA/4PXAqUAZ8ICLPquqaoEXfUNXIdWy3c6177qsormxmXHI8hZnJEQvJGGNGUihXBP8HtInIAuBmYBvwYAjrLQY2qupmb6jLx4Bz9jvScKla7Z77mo5WNHFw4TjrWsIYEzNCSQQ9qqq4g/ivVfXXQCgF6BOB0oD3Zd60YMeIyMci8jcRmTvQhkTkWhFZJiLLqqurQ/jofVBVAknjIGsyfr+yvrLZehw1xsSUUBJBs4h8F7gUeN4r8kkIYb2BTqmDB7j5CJiiqguA3wJPD7QhVb1TVRep6qL8/PwQPnofVJW4qwERyurbae3qtRZDxpiYEkoiuADoBK5W1UrcWf3PQ1ivDJgU8L4YKA9cQFWbVLXFe/0CkCAieaEEPixUdyUCYE1fRbElAmNMDAllzOJK4JcB77cTWh3BB8BMEZkG7AAuBC4OXEBECoAqVVURWYxLTLWhh3+AGrZDZ9NuXUuIwKwJ1rWEMSZ27DURiMjRuGKbQ4BEwAe0qGrmUOupao+I3AC86K1zr6qWiMh13vw7gC8CXxORHqAduNCrjxgZ/V1LuKaj6yqamZqbRmpiKI2pjDFmbAjliPc73Nn8n4FFwJeBmaFs3CvueSFo2h0Br3/nbT8y+hLB+F1dS1j9gDEm1oRSR4CqbgR8qtqrqvcBJ4Y1qpFStRqyp0JSBq2dPWyra7MbyYwxMSeUK4I2EUkEVojI7UAFkBbesEZIYNcSVc2oWtcSxpjYE8oVwWW4Mv4bgFZcS6DzwhnUiOhqg7pNAV1L9PUxZFcExpjYEkqroW3ey3bgx+ENZwRVrwX197cY2rCzmZQEHxOzUiIcmDHGjKyhxixexZ43gPVT1flhiWikBA1GU1rXxuScVOLirGsJY0xsGeqKIHIdwY2EqhJISIPsaQCU1rUzKSc1wkEZY8zIG6qOIAEoVtVtgQ9gMqFVMke3qhKYMAfi4lBVtte1MSnHioWMMbFnqETwK6B5gOnt3rzRS9U1HfWKhWpbu2jv7mWyXREYY2LQUIlgqqquDJ6oqsuAqWGLaCQ0V0B7fX+Loe11bQBMyrZEYIyJPUMlgqFGZhndZSiVu49BUOolgsm5lgiMMbFnqETwgYhcEzxRRK4GPgxfSCOgbzCa8XOAXYmgOHt05zdjjNkfQ1X63gQ8JSKXsOvAvwjX8dy/hDmu8KoqgcxJkJIFuBZDeelJ1tmcMSYmDXrkU9Uq4FgROQmY501+XlX/OSKRhVNA1xKAtRgyxsS0UO4sfhV4dQRiGRk9nVDzCRx8Vv+k0vo2jpiSHcGgjDEmckLqfXRMqV4H2ttfUdzd66e8od2ajhpjYlbsJYKgwWgqGjrwqzUdNcbErthMBPHJkDMdCLiHwK4IjDExKgYTwWo3IpnPVY+U1vclAqssNsbEphhMBCX99QPgrgji44TCTEsExpjYFFuJoGUntFbv1nS0tK6Nidkp+Kz7aWNMjIqtRFC5yj0HXBH0jUNgjDGxKrYSQV+LofEBiaC+nWJrMWSMiWGxlwgyCiEtF4CWzh7qWrvsisAYE9NiLxEE1Q+AtRgyxsS22EkEvd3uruKgFkOAXREYY2JaWBOBiJwuIutFZKOI3DrEckeKSK+IfDFswdRsAH/3wFcEVkdgjIlhYUsEIuIDfg+cAcwBLhKROYMs9zPgxXDFAuwagyCoxVBGUjxZqQlh/WhjjIlm4bwiWAxsVNXNqtoFPAacM8ByNwJPAjvDGAvMPhOuehHyZvZPKq1vpzgnFRG7h8AYE7vCmQgmAqUB78u8af1EZCJukJs7whiHk5QOk48G366z/+11bUy2imJjTIwLZyIY6DRbg97/CrhFVXuH3JDItSKyTESWVVdXD0twqkppXZvVDxhjYl44x2YsAyYFvC8GyoOWWQQ85hXN5AFnikiPqj4duJCq3gncCbBo0aLgZLJfqps76ezx24D1xpiYF85E8AEwU0SmATuAC4GLAxdQ1Wl9r0XkfuC54CQQLv29jtoVgTEmxoUtEahqj4jcgGsN5APuVdUSEbnOmx/+eoEh2DgExhjjhPOKAFV9AXghaNqACUBVrwhnLMFK69oBKM62ymJjTGyLnTuLg2yva2PCuCSSE3yRDsUYYyIqrFcE0cxaDBkTO7q7uykrK6OjoyPSoYRdcnIyxcXFJCSEfqNsTCeCo6fnRjoMY8wIKCsrIyMjg6lTp47pG0hVldraWsrKypg2bdreV/DEZNFQV4+fiqYOiq2i2JiY0NHRQW5u7phOAgAiQm5u7j5f+cRkItjR0I6q9TpqTCwZ60mgz/58z5hMBLt6HbUWQ8YYE5OJoH8cArur2BgzAmpra1m4cCELFy6koKCAiRMn9r/v6uoact1ly5bxjW98I6zxxWRlcWl9G4m+OCZkJEc6FGNMDMjNzWXFihUALFmyhPT0dL7zne/0z+/p6SE+fuDD8aJFi1i0aFFY44vNRFDXRnF2CnFxsVFmaIzZ5cd/LWFNedOwbnNO0Th+dPbcvS8Y4IorriAnJ4fly5dz+OGHc8EFF3DTTTfR3t5OSkoK9913H7Nnz+a1117jF7/4Bc899xxLlixh+/btbN68me3bt3PTTTcNy9VCjCaCdmsxZIyJuE8++YSXX34Zn89HU1MTS5cuJT4+npdffpnvfe97PPnkk3uss27dOl599VWam5uZPXs2X/va1/bpnoGBxGQi2F7XxoJJmZEOwxgTAft65h5OX/rSl/D5XO8GjY2NXH755WzYsAERobu7e8B1zjrrLJKSkkhKSmL8+PFUVVVRXFx8QHHEXGVxY3s3je3ddlexMSbi0tLS+l//4Ac/4KSTTmL16tX89a9/HfRegKSkpP7XPp+Pnp6eA44j5hJBX9NRu4fAGBNNGhsbmTjRDeJ4//33j+hnx2wisO6njTHR5Oabb+a73/0uxx13HL29Qw7aOOxEdVgG/BoxixYt0mXLlu33+ncu3cRPXljHxz/6LJkpB1bBYowZHdauXcshhxwS6TBGzEDfV0Q+VNUB26HG3BXB9ro2xiXHWxIwxhhPzCWC0rp2u6PYGGMCxGAisHEIjDEmUEwlAr9fKatvtxZDxhgTIKYSQVVzB129frur2BhjAsRUIugbsN6uCIwxZpeY6mJiu91MZoyJgNraWk455RQAKisr8fl85OfnA/D++++TmJg45PqvvfYaiYmJHHvssWGJL6YSQWldGyJQlGXdTxtjRs7euqHem9dee4309HRLBMOhtK6NwnHJJMX7Ih2KMSZS/nYrVK4a3m0WHApn3LZPq3z44Yd8+9vfpqWlhby8PO6//34KCwv5zW9+wx133EF8fDxz5szhtttu44477sDn8/Hwww/z29/+lhNOOGFYw4+tRFDfZhXFxpiIU1VuvPFGnnnmGfLz8/nTn/7E97//fe69915uu+02tmzZQlJSEg0NDWRlZXHdddft81XEvoipRLC9ro0TZuZHOgxjTCTt45l7OHR2drJ69WpOPfVUAHp7eyksLARg/vz5XHLJJZx77rmce+65IxJPWFsNicjpIrJeRDaKyK0DzD9HRFaKyAoRWSYix4crlo7uXqqaOu1mMmNMxKkqc+fOZcWKFaxYsYJVq1bx0ksvAfD8889z/fXX8+GHH3LEEUcMSzfTexO2RCAiPuD3wBnAHOAiEZkTtNgrwAJVXQhcBdwdrnjK6r2mo7kp4foIY4wJSVJSEtXV1bzzzjsAdHd3U1JSgt/vp7S0lJNOOonbb7+dhoYGWlpayMjIoLm5OWzxhPOKYDGwUVU3q2oX8BhwTuACqtqiu7o/TQPC1hVqab3X/bRdERhjIiwuLo4nnniCW265hQULFrBw4ULefvttent7ufTSSzn00EM57LDD+Na3vkVWVhZnn302Tz31FAsXLuSNN94Y9njCWUcwESgNeF8GHBW8kIj8C/BTYDxw1kAbEpFrgWsBJk+evF/BZCTF89k5E5iSm7b3hY0xJkyWLFnS/3rp0qV7zH/zzTf3mDZr1ixWrlwZtpjCmQhkgGl7nPGr6lPAUyLyKeA/gM8MsMydwJ3gxiPYn2AWTc1h0dSc/VnVGGPGtHAWDZUBkwLeFwPlgy2sqkuBg0QkL4wxGWOMCRLORPABMFNEpolIInAh8GzgAiIyQ0TEe304kAjUhjEmY0yMGm2jMe6v/fmeYSsaUtUeEbkBeBHwAfeqaomIXOfNvwM4D/iyiHQD7cAFGit/LWPMiElOTqa2tpbc3Fy8c88xSVWpra0lOXnfutGJuTGLjTGxp7u7m7KyMjo6OiIdStglJydTXFxMQsLuw/EONWZxTN1ZbIyJTQkJCUybNi3SYUStmBqPwBhjzJ4sERhjTIyzRGCMMTFu1FUWi0g1sG0/V88DaoYxnOFkse2faI4Nojs+i23/jNbYpqjqgN0vj7pEcCBEZNlgteaRZrHtn2iODaI7Pott/4zF2KxoyBhjYpwlAmOMiXGxlgjujHQAQ7DY9k80xwbRHZ/Ftn/GXGwxVUdgjDFmT7F2RWCMMSaIJQJjjIlxMZMIROR0EVkvIhtF5NZIxxNIRLaKyCoRWSEiEe1RT0TuFZGdIrI6YFqOiPxDRDZ4z9lRFNsSEdnh7bsVInJmhGKbJCKvishaESkRkW960yO+74aILeL7TkSSReR9EfnYi+3H3vRo2G+DxRbx/RYQo09ElovIc977/dpvMVFHICI+4BPgVNyAOR8AF6nqmogG5hGRrcAiVY34TSreSHEtwIOqOs+bdjtQp6q3eUk0W1VviZLYlgAtqvqLkY4nKLZCoFBVPxKRDOBD4FzgCiK874aI7XwivO+88UjSVLVFRBKAN4FvAl8g8vttsNhOJwp+cwAi8m1gETBOVT+3v/+rsXJFsBjYqKqbVbULeAw4J8IxRSVvpLi6oMnnAA94rx/AHURG3CCxRQVVrVDVj7zXzcBa3LjdEd93Q8QWceq0eG8TvIcSHfttsNiigogU48Z5vztg8n7tt1hJBBOB0oD3ZUTJP4JHgZdE5EMRuTbSwQxggqpWgDuoAOMjHE+wG0RkpVd0FJFiq0AiMhU4DHiPKNt3QbFBFOw7r3hjBbAT+IeqRs1+GyQ2iIL9BvwKuBnwB0zbr/0WK4lgoCGJoiazA8ep6uHAGcD1XhGICc3/AQcBC4EK4L8jGYyIpANPAjepalMkYwk2QGxRse9UtVdVF+LGNV8sIvMiEcdABokt4vtNRD4H7FTVD4dje7GSCMqASQHvi4HyCMWyB1Ut9553Ak/hirKiSZVXztxX3rwzwvH0U9Uq75/VD9xFBPedV478JPCIqv7FmxwV+26g2KJp33nxNACv4crgo2K/9QmMLUr223HA5736xceAk0XkYfZzv8VKIvgAmCki00QkEbgQeDbCMQEgImleBR4ikgZ8Flg99Foj7lngcu/15cAzEYxlN30/es+/EKF951Us3gOsVdVfBsyK+L4bLLZo2Hciki8iWd7rFOAzwDqiY78NGFs07DdV/a6qFqvqVNzx7J+qein7u99UNSYewJm4lkObgO9HOp6AuKYDH3uPkkjHBjyKu9ztxl1JXQ3kAq8AG7znnCiK7SFgFbDS+ycojFBsx+OKG1cCK7zHmdGw74aILeL7DpgPLPdiWA380JseDfttsNgivt+C4jwReO5A9ltMNB81xhgzuFgpGjLGGDMISwTGGBPjLBEYY0yMs0RgjDExzhKBMcbEOEsExgQRkd6AniVXyDD2VisiUyWg91RjokF8pAMwJgq1q+tWwJiYYFcExoRI3LgRP/P6qH9fRGZ406eIyCteJ2SviMhkb/oEEXnK68/+YxE51tuUT0Tu8vq4f8m7a9WYiLFEYMyeUoKKhi4ImNekqouB3+F6f8R7/aCqzgceAX7jTf8N8LqqLgAOx905DjAT+L2qzgUagPPC+m2M2Qu7s9iYICLSoqrpA0zfCpysqpu9TtwqVTVXRGpw3Qx0e9MrVDVPRKqBYlXtDNjGVFx3xjO997cACar6nyPw1YwZkF0RGLNvdJDXgy0zkM6A171YXZ2JMEsExuybCwKe3/Fev43rARLgEtyQhuA6/foa9A9wMm6kgjRmX9iZiDF7SvFGperzd1Xta0KaJCLv4U6iLvKmfQO4V0T+DagGrvSmfxO4U0Suxp35fw3Xe6oxUcXqCIwJkVdHsEhVayIdizHDyYqGjDEmxtkVgTHGxDi7IjDGmBhnicAYY2KcJQJjjIlxlgiMMSbGWSIwxpgY9/8BMugq2rDFrsMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "network_with_classifier = QAutoencoder(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(epochs=40,use_latent_classifier=True)\n",
    "network_with_classifier.plot_latent_dimension(\"Compressed QAE with classifier\")\n",
    "network_with_classifier.plot_score(use_latent_classifier=True,model_name=\"Compressed QAE with classifier\")"
   ]
  },
  {
   "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
}