layer-share-network (bottom share).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "\n",
    "train1_dir = 'C:/Users/KIMHAKBIN/A_My Python/Datacloud/digits/MNIST_mni/train'\n",
    "test1_dir = 'C:/Users/KIMHAKBIN/A_My Python/Datacloud/digits/MNIST_mni/test'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train2_dir = 'C:/Users/KIMHAKBIN/A_My Python/Datacloud/digits/Original/USPS_categorize/train'\n",
    "test2_dir = 'C:/Users/KIMHAKBIN/A_My Python/Datacloud/digits/Original/USPS_categorize/test'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train3_dir = 'C:/Users/KIMHAKBIN/A_My Python/Datacloud/digits/SVHN_mni/train'\n",
    "test3_dir = 'C:/Users/KIMHAKBIN/A_My Python/Datacloud/digits/SVHN_mni/test'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "train_datagen = ImageDataGenerator(rescale = 1./255)\n",
    "test_datagen = ImageDataGenerator(rescale = 1./255)\n",
    "\n",
    "def generate_generator_multiple(generator, dir1, dir2, dir3):\n",
    "    genX1 = generator.flow_from_directory(dir1,\n",
    "                                          target_size = (28,28),\n",
    "                                          batch_size=64,\n",
    "                                          color_mode=\"grayscale\",\n",
    "                                          class_mode='categorical')\n",
    "    \n",
    "    genX2 = generator.flow_from_directory(dir2,\n",
    "                                          target_size = (28,28),\n",
    "                                          batch_size=64,\n",
    "                                          color_mode=\"grayscale\",\n",
    "                                          class_mode='categorical')\n",
    "    \n",
    "    genX3 = generator.flow_from_directory(dir3,\n",
    "                                          target_size = (28,28),\n",
    "                                          batch_size=64,                                          \n",
    "                                          class_mode='categorical')\n",
    "    \n",
    "    while True:\n",
    "        X_1, Y_1 = genX1.next()\n",
    "        X_2, Y_2 = genX2.next()\n",
    "        X_3, Y_3 = genX3.next()\n",
    "        \n",
    "        X_1 = np.tile(X_1, [1,1,1,3])\n",
    "        X_2 = np.tile(X_2, [1,1,1,3])\n",
    "        \n",
    "        \n",
    "        yield [X_1, X_2, X_3],[Y_1, Y_2, Y_3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputgenerator=generate_generator_multiple(generator=train_datagen,\n",
    "                                           dir1=train1_dir,\n",
    "                                           dir2=train2_dir,\n",
    "                                           dir3=train3_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "testgenerator=generate_generator_multiple(generator=test_datagen,\n",
    "                                           dir1=test1_dir,\n",
    "                                           dir2=test2_dir,\n",
    "                                           dir3=test3_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# mdoel1\n",
    "from keras.models import Model\n",
    "from keras import layers\n",
    "from keras import Input\n",
    "\n",
    "# input1 주입\n",
    "input_MNIST = Input(shape=(28, 28, 3))\n",
    "# input2 주입\n",
    "input_USPS = Input(shape=(28, 28, 3))\n",
    "# input3 주입\n",
    "input_SVHN = Input(shape=(28, 28, 3))\n",
    "\n",
    "# shared layer(ALL) 생성\n",
    "shALL_Conv2D_1 = layers.Conv2D(32, (3, 3), activation='relu')\n",
    "\n",
    "\n",
    "# shaerd layer(ALL)에 data 주입\n",
    "x_1 = shALL_Conv2D_1(input_MNIST)\n",
    "\n",
    "\n",
    "x_2 = shALL_Conv2D_1(input_USPS)\n",
    "\n",
    "\n",
    "x_3 = shALL_Conv2D_1(input_SVHN)\n",
    "\n",
    "\n",
    "# shared layer(MNIST, USPS) 생성\n",
    "\n",
    "shMU_Conv2D_2 = layers.Conv2D(32, (3, 3), activation='relu')\n",
    "shMU_MaxPooling2D_1 = layers.MaxPooling2D((2,2))\n",
    "\n",
    "# shared layer(MNIST, USPS)에 data 주입\n",
    "\n",
    "x_1 = shMU_Conv2D_2(x_1)\n",
    "x_1 = shMU_MaxPooling2D_1(x_1)\n",
    "\n",
    "x_2 = shMU_Conv2D_2(x_2)\n",
    "x_2 = shMU_MaxPooling2D_1(x_2)\n",
    "\n",
    "## private layer에 data 주입\n",
    "\n",
    "# MNIST\n",
    "x_1 = layers.Conv2D(64, (3, 3), activation='relu')(x_1)\n",
    "x_1 = layers.Conv2D(64, (3, 3), activation='relu')(x_1)\n",
    "x_1 = layers.MaxPooling2D((2,2))(x_1)\n",
    "\n",
    "x_1 = layers.Flatten()(x_1)\n",
    "x_1 = layers.Dense(128, activation='relu')(x_1)\n",
    "output_1 = layers.Dense(10, activation='softmax')(x_1)\n",
    "\n",
    "# USPS\n",
    "x_2 = layers.Conv2D(64, (3, 3), activation='relu')(x_2)\n",
    "x_2 = layers.Conv2D(64, (3, 3), activation='relu')(x_2)\n",
    "x_2 = layers.MaxPooling2D((2,2))(x_2)\n",
    "\n",
    "x_2 = layers.Flatten()(x_2)\n",
    "x_2 = layers.Dense(128, activation='relu')(x_2)\n",
    "output_2 = layers.Dense(10, activation='softmax')(x_2)\n",
    "\n",
    "# SVHN\n",
    "\n",
    "x_3 = layers.Conv2D(32, (3, 3), activation='relu')(x_3)\n",
    "x_3 = layers.MaxPooling2D((2,2))(x_3)\n",
    "\n",
    "x_3 = layers.Conv2D(64, (3, 3), activation='relu')(x_3)\n",
    "x_3 = layers.Conv2D(64, (3, 3), activation='relu')(x_3)\n",
    "x_3 = layers.MaxPooling2D((2,2))(x_3)\n",
    "\n",
    "x_3 = layers.Flatten()(x_3)\n",
    "x_3 = layers.Dense(128, activation='relu')(x_3)\n",
    "output_3 = layers.Dense(10, activation='softmax')(x_3)\n",
    "\n",
    "model = Model([input_MNIST, input_USPS, input_SVHN], [output_1, output_2, output_3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            (None, 28, 28, 3)    0                                            \n",
      "__________________________________________________________________________________________________\n",
      "input_2 (InputLayer)            (None, 28, 28, 3)    0                                            \n",
      "__________________________________________________________________________________________________\n",
      "input_3 (InputLayer)            (None, 28, 28, 3)    0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_1 (Conv2D)               (None, 26, 26, 32)   896         input_1[0][0]                    \n",
      "                                                                 input_2[0][0]                    \n",
      "                                                                 input_3[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_2 (Conv2D)               (None, 24, 24, 32)   9248        conv2d_1[0][0]                   \n",
      "                                                                 conv2d_1[1][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_7 (Conv2D)               (None, 24, 24, 32)   9248        conv2d_1[2][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2D)  (None, 12, 12, 32)   0           conv2d_2[0][0]                   \n",
      "                                                                 conv2d_2[1][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2D)  (None, 12, 12, 32)   0           conv2d_7[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_3 (Conv2D)               (None, 10, 10, 64)   18496       max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_5 (Conv2D)               (None, 10, 10, 64)   18496       max_pooling2d_1[1][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_8 (Conv2D)               (None, 10, 10, 64)   18496       max_pooling2d_4[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_4 (Conv2D)               (None, 8, 8, 64)     36928       conv2d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_6 (Conv2D)               (None, 8, 8, 64)     36928       conv2d_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_9 (Conv2D)               (None, 8, 8, 64)     36928       conv2d_8[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2D)  (None, 4, 4, 64)     0           conv2d_4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2D)  (None, 4, 4, 64)     0           conv2d_6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_5 (MaxPooling2D)  (None, 4, 4, 64)     0           conv2d_9[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "flatten_1 (Flatten)             (None, 1024)         0           max_pooling2d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "flatten_2 (Flatten)             (None, 1024)         0           max_pooling2d_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "flatten_3 (Flatten)             (None, 1024)         0           max_pooling2d_5[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, 128)          131200      flatten_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dense_3 (Dense)                 (None, 128)          131200      flatten_2[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dense_5 (Dense)                 (None, 128)          131200      flatten_3[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dense_2 (Dense)                 (None, 10)           1290        dense_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_4 (Dense)                 (None, 10)           1290        dense_3[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_6 (Dense)                 (None, 10)           1290        dense_5[0][0]                    \n",
      "==================================================================================================\n",
      "Total params: 583,134\n",
      "Trainable params: 583,134\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.optimizers import Optimizer\n",
    "from keras_lr_multiplier import LRMultiplier\n",
    "\n",
    "\n",
    "multipliers = {'conv2d_1': 0.333, 'conv2d_2': 0.5}\n",
    "# LRMultiplier('rmsprop', multipliers)\n",
    "model.compile(optimizer='rmsprop',\n",
    "             loss=['categorical_crossentropy','categorical_crossentropy','categorical_crossentropy'],\n",
    "             metrics=['acc', 'acc', 'acc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "Found 2007 images belonging to 10 classes.\n",
      "Found 2007 images belonging to 10 classes.\n",
      "Found 7291 images belonging to 10 classes.\n",
      "Found 2007 images belonging to 10 classes.\n",
      "Found 7291 images belonging to 10 classes.\n",
      "Found 7291 images belonging to 10 classes.\n",
      "120/120 [==============================] - 19s 160ms/step - loss: 2.8468 - dense_2_loss: 0.4383 - dense_4_loss: 0.4143 - dense_6_loss: 1.9942 - dense_2_acc: 0.8605 - dense_2_acc_1: 0.8605 - dense_2_acc_2: 0.8605 - dense_4_acc: 0.8659 - dense_4_acc_1: 0.8659 - dense_4_acc_2: 0.8659 - dense_6_acc: 0.3040 - dense_6_acc_1: 0.3040 - dense_6_acc_2: 0.3040 - val_loss: 1.7203 - val_dense_2_loss: 0.1706 - val_dense_4_loss: 0.2660 - val_dense_6_loss: 1.2837 - val_dense_2_acc: 0.9472 - val_dense_2_acc_1: 0.9472 - val_dense_2_acc_2: 0.9472 - val_dense_4_acc: 0.9277 - val_dense_4_acc_1: 0.9277 - val_dense_4_acc_2: 0.9277 - val_dense_6_acc: 0.6053 - val_dense_6_acc_1: 0.6053 - val_dense_6_acc_2: 0.6053\n",
      "Epoch 2/30\n",
      "120/120 [==============================] - 15s 129ms/step - loss: 1.1876 - dense_2_loss: 0.1009 - dense_4_loss: 0.0898 - dense_6_loss: 0.9968 - dense_2_acc: 0.9701 - dense_2_acc_1: 0.9701 - dense_2_acc_2: 0.9701 - dense_4_acc: 0.9715 - dense_4_acc_1: 0.9715 - dense_4_acc_2: 0.9715 - dense_6_acc: 0.6879 - dense_6_acc_1: 0.6879 - dense_6_acc_2: 0.6879 - val_loss: 1.2816 - val_dense_2_loss: 0.1405 - val_dense_4_loss: 0.2170 - val_dense_6_loss: 0.9240 - val_dense_2_acc: 0.9604 - val_dense_2_acc_1: 0.9604 - val_dense_2_acc_2: 0.9604 - val_dense_4_acc: 0.9459 - val_dense_4_acc_1: 0.9459 - val_dense_4_acc_2: 0.9459 - val_dense_6_acc: 0.7344 - val_dense_6_acc_1: 0.7344 - val_dense_6_acc_2: 0.7344\n",
      "Epoch 3/30\n",
      "120/120 [==============================] - 15s 128ms/step - loss: 0.8074 - dense_2_loss: 0.0601 - dense_4_loss: 0.0460 - dense_6_loss: 0.7014 - dense_2_acc: 0.9826 - dense_2_acc_1: 0.9826 - dense_2_acc_2: 0.9826 - dense_4_acc: 0.9867 - dense_4_acc_1: 0.9867 - dense_4_acc_2: 0.9867 - dense_6_acc: 0.7849 - dense_6_acc_1: 0.7849 - dense_6_acc_2: 0.7849 - val_loss: 1.1126 - val_dense_2_loss: 0.1098 - val_dense_4_loss: 0.2447 - val_dense_6_loss: 0.7580 - val_dense_2_acc: 0.9623 - val_dense_2_acc_1: 0.9623 - val_dense_2_acc_2: 0.9623 - val_dense_4_acc: 0.9491 - val_dense_4_acc_1: 0.9491 - val_dense_4_acc_2: 0.9491 - val_dense_6_acc: 0.7653 - val_dense_6_acc_1: 0.7653 - val_dense_6_acc_2: 0.7653\n",
      "Epoch 4/30\n",
      "120/120 [==============================] - 15s 127ms/step - loss: 0.6274 - dense_2_loss: 0.0387 - dense_4_loss: 0.0318 - dense_6_loss: 0.5569 - dense_2_acc: 0.9876 - dense_2_acc_1: 0.9876 - dense_2_acc_2: 0.9876 - dense_4_acc: 0.9897 - dense_4_acc_1: 0.9897 - dense_4_acc_2: 0.9897 - dense_6_acc: 0.8325 - dense_6_acc_1: 0.8325 - dense_6_acc_2: 0.8325 - val_loss: 1.1077 - val_dense_2_loss: 0.0973 - val_dense_4_loss: 0.2837 - val_dense_6_loss: 0.7267 - val_dense_2_acc: 0.9718 - val_dense_2_acc_1: 0.9718 - val_dense_2_acc_2: 0.9718 - val_dense_4_acc: 0.9445 - val_dense_4_acc_1: 0.9445 - val_dense_4_acc_2: 0.9445 - val_dense_6_acc: 0.7935 - val_dense_6_acc_1: 0.7935 - val_dense_6_acc_2: 0.7935\n",
      "Epoch 5/30\n",
      "120/120 [==============================] - 15s 127ms/step - loss: 0.5058 - dense_2_loss: 0.0282 - dense_4_loss: 0.0258 - dense_6_loss: 0.4519 - dense_2_acc: 0.9906 - dense_2_acc_1: 0.9906 - dense_2_acc_2: 0.9906 - dense_4_acc: 0.9922 - dense_4_acc_1: 0.9922 - dense_4_acc_2: 0.9922 - dense_6_acc: 0.8640 - dense_6_acc_1: 0.8640 - dense_6_acc_2: 0.8640 - val_loss: 0.8916 - val_dense_2_loss: 0.0869 - val_dense_4_loss: 0.1485 - val_dense_6_loss: 0.6562 - val_dense_2_acc: 0.9759 - val_dense_2_acc_1: 0.9759 - val_dense_2_acc_2: 0.9759 - val_dense_4_acc: 0.9659 - val_dense_4_acc_1: 0.9659 - val_dense_4_acc_2: 0.9659 - val_dense_6_acc: 0.8158 - val_dense_6_acc_1: 0.8158 - val_dense_6_acc_2: 0.8158\n",
      "Epoch 6/30\n",
      "120/120 [==============================] - 15s 128ms/step - loss: 0.4124 - dense_2_loss: 0.0198 - dense_4_loss: 0.0179 - dense_6_loss: 0.3748 - dense_2_acc: 0.9932 - dense_2_acc_1: 0.9932 - dense_2_acc_2: 0.9932 - dense_4_acc: 0.9943 - dense_4_acc_1: 0.9943 - dense_4_acc_2: 0.9943 - dense_6_acc: 0.8847 - dense_6_acc_1: 0.8847 - dense_6_acc_2: 0.8847 - val_loss: 1.0324 - val_dense_2_loss: 0.1220 - val_dense_4_loss: 0.2107 - val_dense_6_loss: 0.6996 - val_dense_2_acc: 0.9668 - val_dense_2_acc_1: 0.9668 - val_dense_2_acc_2: 0.9668 - val_dense_4_acc: 0.9586 - val_dense_4_acc_1: 0.9586 - val_dense_4_acc_2: 0.9586 - val_dense_6_acc: 0.8104 - val_dense_6_acc_1: 0.8104 - val_dense_6_acc_2: 0.8104\n",
      "Epoch 7/30\n",
      "120/120 [==============================] - 15s 128ms/step - loss: 0.3301 - dense_2_loss: 0.0146 - dense_4_loss: 0.0111 - dense_6_loss: 0.3045 - dense_2_acc: 0.9958 - dense_2_acc_1: 0.9958 - dense_2_acc_2: 0.9958 - dense_4_acc: 0.9973 - dense_4_acc_1: 0.9973 - dense_4_acc_2: 0.9973 - dense_6_acc: 0.9061 - dense_6_acc_1: 0.9061 - dense_6_acc_2: 0.9061 - val_loss: 1.0370 - val_dense_2_loss: 0.0967 - val_dense_4_loss: 0.2106 - val_dense_6_loss: 0.7297 - val_dense_2_acc: 0.9736 - val_dense_2_acc_1: 0.9736 - val_dense_2_acc_2: 0.9736 - val_dense_4_acc: 0.9673 - val_dense_4_acc_1: 0.9673 - val_dense_4_acc_2: 0.9673 - val_dense_6_acc: 0.8167 - val_dense_6_acc_1: 0.8167 - val_dense_6_acc_2: 0.8167\n",
      "Epoch 8/30\n",
      "120/120 [==============================] - 16s 129ms/step - loss: 0.2895 - dense_2_loss: 0.0120 - dense_4_loss: 0.0115 - dense_6_loss: 0.2660 - dense_2_acc: 0.9970 - dense_2_acc_1: 0.9970 - dense_2_acc_2: 0.9970 - dense_4_acc: 0.9977 - dense_4_acc_1: 0.9977 - dense_4_acc_2: 0.9977 - dense_6_acc: 0.9235 - dense_6_acc_1: 0.9235 - dense_6_acc_2: 0.9235 - val_loss: 1.1176 - val_dense_2_loss: 0.1124 - val_dense_4_loss: 0.1894 - val_dense_6_loss: 0.8159 - val_dense_2_acc: 0.9723 - val_dense_2_acc_1: 0.9723 - val_dense_2_acc_2: 0.9723 - val_dense_4_acc: 0.9623 - val_dense_4_acc_1: 0.9623 - val_dense_4_acc_2: 0.9623 - val_dense_6_acc: 0.7917 - val_dense_6_acc_1: 0.7917 - val_dense_6_acc_2: 0.7917\n",
      "Epoch 9/30\n",
      "120/120 [==============================] - 15s 129ms/step - loss: 0.2251 - dense_2_loss: 0.0095 - dense_4_loss: 0.0126 - dense_6_loss: 0.2030 - dense_2_acc: 0.9966 - dense_2_acc_1: 0.9966 - dense_2_acc_2: 0.9966 - dense_4_acc: 0.9977 - dense_4_acc_1: 0.9977 - dense_4_acc_2: 0.9977 - dense_6_acc: 0.9365 - dense_6_acc_1: 0.9365 - dense_6_acc_2: 0.9365 - val_loss: 1.0129 - val_dense_2_loss: 0.1048 - val_dense_4_loss: 0.2197 - val_dense_6_loss: 0.6884 - val_dense_2_acc: 0.9777 - val_dense_2_acc_1: 0.9777 - val_dense_2_acc_2: 0.9777 - val_dense_4_acc: 0.9650 - val_dense_4_acc_1: 0.9650 - val_dense_4_acc_2: 0.9650 - val_dense_6_acc: 0.8395 - val_dense_6_acc_1: 0.8395 - val_dense_6_acc_2: 0.8395\n",
      "Epoch 10/30\n",
      "120/120 [==============================] - 16s 131ms/step - loss: 0.1916 - dense_2_loss: 0.0108 - dense_4_loss: 0.0061 - dense_6_loss: 0.1748 - dense_2_acc: 0.9974 - dense_2_acc_1: 0.9974 - dense_2_acc_2: 0.9974 - dense_4_acc: 0.9982 - dense_4_acc_1: 0.9982 - dense_4_acc_2: 0.9982 - dense_6_acc: 0.9454 - dense_6_acc_1: 0.9454 - dense_6_acc_2: 0.9454 - val_loss: 1.1933 - val_dense_2_loss: 0.0778 - val_dense_4_loss: 0.1988 - val_dense_6_loss: 0.9167 - val_dense_2_acc: 0.9823 - val_dense_2_acc_1: 0.9823 - val_dense_2_acc_2: 0.9823 - val_dense_4_acc: 0.9659 - val_dense_4_acc_1: 0.9659 - val_dense_4_acc_2: 0.9659 - val_dense_6_acc: 0.8076 - val_dense_6_acc_1: 0.8076 - val_dense_6_acc_2: 0.8076\n",
      "Epoch 11/30\n",
      "120/120 [==============================] - 15s 128ms/step - loss: 0.1492 - dense_2_loss: 0.0053 - dense_4_loss: 0.0082 - dense_6_loss: 0.1357 - dense_2_acc: 0.9987 - dense_2_acc_1: 0.9987 - dense_2_acc_2: 0.9987 - dense_4_acc: 0.9978 - dense_4_acc_1: 0.9978 - dense_4_acc_2: 0.9978 - dense_6_acc: 0.9565 - dense_6_acc_1: 0.9565 - dense_6_acc_2: 0.9565 - val_loss: 1.3086 - val_dense_2_loss: 0.0871 - val_dense_4_loss: 0.2676 - val_dense_6_loss: 0.9539 - val_dense_2_acc: 0.9805 - val_dense_2_acc_1: 0.9805 - val_dense_2_acc_2: 0.9805 - val_dense_4_acc: 0.9620 - val_dense_4_acc_1: 0.9620 - val_dense_4_acc_2: 0.9620 - val_dense_6_acc: 0.8017 - val_dense_6_acc_1: 0.8017 - val_dense_6_acc_2: 0.8017\n",
      "Epoch 12/30\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "120/120 [==============================] - 16s 132ms/step - loss: 0.1239 - dense_2_loss: 0.0062 - dense_4_loss: 0.0059 - dense_6_loss: 0.1118 - dense_2_acc: 0.9982 - dense_2_acc_1: 0.9982 - dense_2_acc_2: 0.9982 - dense_4_acc: 0.9990 - dense_4_acc_1: 0.9990 - dense_4_acc_2: 0.9990 - dense_6_acc: 0.9648 - dense_6_acc_1: 0.9648 - dense_6_acc_2: 0.9648 - val_loss: 1.3745 - val_dense_2_loss: 0.1395 - val_dense_4_loss: 0.2711 - val_dense_6_loss: 0.9639 - val_dense_2_acc: 0.9745 - val_dense_2_acc_1: 0.9745 - val_dense_2_acc_2: 0.9745 - val_dense_4_acc: 0.9623 - val_dense_4_acc_1: 0.9623 - val_dense_4_acc_2: 0.9623 - val_dense_6_acc: 0.8049 - val_dense_6_acc_1: 0.8049 - val_dense_6_acc_2: 0.8049\n",
      "Epoch 13/30\n",
      "120/120 [==============================] - 15s 129ms/step - loss: 0.0925 - dense_2_loss: 0.0013 - dense_4_loss: 0.0029 - dense_6_loss: 0.0883 - dense_2_acc: 0.9993 - dense_2_acc_1: 0.9993 - dense_2_acc_2: 0.9993 - dense_4_acc: 0.9992 - dense_4_acc_1: 0.9992 - dense_4_acc_2: 0.9992 - dense_6_acc: 0.9721 - dense_6_acc_1: 0.9721 - dense_6_acc_2: 0.9721 - val_loss: 1.5997 - val_dense_2_loss: 0.1422 - val_dense_4_loss: 0.3186 - val_dense_6_loss: 1.1389 - val_dense_2_acc: 0.9736 - val_dense_2_acc_1: 0.9736 - val_dense_2_acc_2: 0.9736 - val_dense_4_acc: 0.9600 - val_dense_4_acc_1: 0.9600 - val_dense_4_acc_2: 0.9600 - val_dense_6_acc: 0.8095 - val_dense_6_acc_1: 0.8095 - val_dense_6_acc_2: 0.8095\n",
      "Epoch 14/30\n",
      "120/120 [==============================] - 16s 131ms/step - loss: 0.0858 - dense_2_loss: 0.0066 - dense_4_loss: 0.0124 - dense_6_loss: 0.0667 - dense_2_acc: 0.9986 - dense_2_acc_1: 0.9986 - dense_2_acc_2: 0.9986 - dense_4_acc: 0.9975 - dense_4_acc_1: 0.9975 - dense_4_acc_2: 0.9975 - dense_6_acc: 0.9767 - dense_6_acc_1: 0.9767 - dense_6_acc_2: 0.9767 - val_loss: 1.5734 - val_dense_2_loss: 0.1010 - val_dense_4_loss: 0.2694 - val_dense_6_loss: 1.2030 - val_dense_2_acc: 0.9764 - val_dense_2_acc_1: 0.9764 - val_dense_2_acc_2: 0.9764 - val_dense_4_acc: 0.9641 - val_dense_4_acc_1: 0.9641 - val_dense_4_acc_2: 0.9641 - val_dense_6_acc: 0.8190 - val_dense_6_acc_1: 0.8190 - val_dense_6_acc_2: 0.8190\n",
      "Epoch 15/30\n",
      "120/120 [==============================] - 16s 129ms/step - loss: 0.0694 - dense_2_loss: 0.0019 - dense_4_loss: 0.0061 - dense_6_loss: 0.0615 - dense_2_acc: 0.9991 - dense_2_acc_1: 0.9991 - dense_2_acc_2: 0.9991 - dense_4_acc: 0.9983 - dense_4_acc_1: 0.9983 - dense_4_acc_2: 0.9983 - dense_6_acc: 0.9807 - dense_6_acc_1: 0.9807 - dense_6_acc_2: 0.9807 - val_loss: 1.6159 - val_dense_2_loss: 0.1680 - val_dense_4_loss: 0.2484 - val_dense_6_loss: 1.1995 - val_dense_2_acc: 0.9736 - val_dense_2_acc_1: 0.9736 - val_dense_2_acc_2: 0.9736 - val_dense_4_acc: 0.9677 - val_dense_4_acc_1: 0.9677 - val_dense_4_acc_2: 0.9677 - val_dense_6_acc: 0.8267 - val_dense_6_acc_1: 0.8267 - val_dense_6_acc_2: 0.8267\n",
      "Epoch 16/30\n",
      "120/120 [==============================] - 16s 134ms/step - loss: 0.0685 - dense_2_loss: 0.0079 - dense_4_loss: 0.0086 - dense_6_loss: 0.0521 - dense_2_acc: 0.9975 - dense_2_acc_1: 0.9975 - dense_2_acc_2: 0.9975 - dense_4_acc: 0.9978 - dense_4_acc_1: 0.9978 - dense_4_acc_2: 0.9978 - dense_6_acc: 0.9839 - dense_6_acc_1: 0.9839 - dense_6_acc_2: 0.9839 - val_loss: 1.5780 - val_dense_2_loss: 0.1080 - val_dense_4_loss: 0.3023 - val_dense_6_loss: 1.1677 - val_dense_2_acc: 0.9818 - val_dense_2_acc_1: 0.9818 - val_dense_2_acc_2: 0.9818 - val_dense_4_acc: 0.9600 - val_dense_4_acc_1: 0.9600 - val_dense_4_acc_2: 0.9600 - val_dense_6_acc: 0.8186 - val_dense_6_acc_1: 0.8186 - val_dense_6_acc_2: 0.8186\n",
      "Epoch 17/30\n",
      "120/120 [==============================] - 16s 135ms/step - loss: 0.0553 - dense_2_loss: 0.0040 - dense_4_loss: 0.0029 - dense_6_loss: 0.0483 - dense_2_acc: 0.9987 - dense_2_acc_1: 0.9987 - dense_2_acc_2: 0.9987 - dense_4_acc: 0.9993 - dense_4_acc_1: 0.9993 - dense_4_acc_2: 0.9993 - dense_6_acc: 0.9852 - dense_6_acc_1: 0.9852 - dense_6_acc_2: 0.9852 - val_loss: 1.9932 - val_dense_2_loss: 0.1688 - val_dense_4_loss: 0.3065 - val_dense_6_loss: 1.5179 - val_dense_2_acc: 0.9736 - val_dense_2_acc_1: 0.9736 - val_dense_2_acc_2: 0.9736 - val_dense_4_acc: 0.9636 - val_dense_4_acc_1: 0.9636 - val_dense_4_acc_2: 0.9636 - val_dense_6_acc: 0.8104 - val_dense_6_acc_1: 0.8104 - val_dense_6_acc_2: 0.8104\n",
      "Epoch 18/30\n",
      "120/120 [==============================] - 15s 128ms/step - loss: 0.0457 - dense_2_loss: 0.0031 - dense_4_loss: 0.0052 - dense_6_loss: 0.0374 - dense_2_acc: 0.9987 - dense_2_acc_1: 0.9987 - dense_2_acc_2: 0.9987 - dense_4_acc: 0.9987 - dense_4_acc_1: 0.9987 - dense_4_acc_2: 0.9987 - dense_6_acc: 0.9898 - dense_6_acc_1: 0.9898 - dense_6_acc_2: 0.9898 - val_loss: 1.9190 - val_dense_2_loss: 0.1802 - val_dense_4_loss: 0.3213 - val_dense_6_loss: 1.4175 - val_dense_2_acc: 0.9741 - val_dense_2_acc_1: 0.9741 - val_dense_2_acc_2: 0.9741 - val_dense_4_acc: 0.9604 - val_dense_4_acc_1: 0.9604 - val_dense_4_acc_2: 0.9604 - val_dense_6_acc: 0.8263 - val_dense_6_acc_1: 0.8263 - val_dense_6_acc_2: 0.8263\n",
      "Epoch 19/30\n",
      "120/120 [==============================] - 15s 128ms/step - loss: 0.0522 - dense_2_loss: 0.0038 - dense_4_loss: 0.0073 - dense_6_loss: 0.0412 - dense_2_acc: 0.9991 - dense_2_acc_1: 0.9991 - dense_2_acc_2: 0.9991 - dense_4_acc: 0.9987 - dense_4_acc_1: 0.9987 - dense_4_acc_2: 0.9987 - dense_6_acc: 0.9872 - dense_6_acc_1: 0.9872 - dense_6_acc_2: 0.9872 - val_loss: 2.0894 - val_dense_2_loss: 0.1561 - val_dense_4_loss: 0.3869 - val_dense_6_loss: 1.5463 - val_dense_2_acc: 0.9804 - val_dense_2_acc_1: 0.9804 - val_dense_2_acc_2: 0.9804 - val_dense_4_acc: 0.9613 - val_dense_4_acc_1: 0.9613 - val_dense_4_acc_2: 0.9613 - val_dense_6_acc: 0.7976 - val_dense_6_acc_1: 0.7976 - val_dense_6_acc_2: 0.7976\n",
      "Epoch 20/30\n",
      "120/120 [==============================] - 15s 129ms/step - loss: 0.0473 - dense_2_loss: 0.0048 - dense_4_loss: 0.0035 - dense_6_loss: 0.0390 - dense_2_acc: 0.9986 - dense_2_acc_1: 0.9986 - dense_2_acc_2: 0.9986 - dense_4_acc: 0.9989 - dense_4_acc_1: 0.9989 - dense_4_acc_2: 0.9989 - dense_6_acc: 0.9877 - dense_6_acc_1: 0.9877 - dense_6_acc_2: 0.9877 - val_loss: 1.8448 - val_dense_2_loss: 0.1471 - val_dense_4_loss: 0.2986 - val_dense_6_loss: 1.3990 - val_dense_2_acc: 0.9750 - val_dense_2_acc_1: 0.9750 - val_dense_2_acc_2: 0.9750 - val_dense_4_acc: 0.9623 - val_dense_4_acc_1: 0.9623 - val_dense_4_acc_2: 0.9623 - val_dense_6_acc: 0.8113 - val_dense_6_acc_1: 0.8113 - val_dense_6_acc_2: 0.8113\n",
      "Epoch 21/30\n",
      "120/120 [==============================] - 16s 130ms/step - loss: 0.0347 - dense_2_loss: 0.0011 - dense_4_loss: 0.0043 - dense_6_loss: 0.0292 - dense_2_acc: 0.9995 - dense_2_acc_1: 0.9995 - dense_2_acc_2: 0.9995 - dense_4_acc: 0.9987 - dense_4_acc_1: 0.9987 - dense_4_acc_2: 0.9987 - dense_6_acc: 0.9918 - dense_6_acc_1: 0.9918 - dense_6_acc_2: 0.9918 - val_loss: 1.9485 - val_dense_2_loss: 0.1511 - val_dense_4_loss: 0.3706 - val_dense_6_loss: 1.4268 - val_dense_2_acc: 0.9773 - val_dense_2_acc_1: 0.9773 - val_dense_2_acc_2: 0.9773 - val_dense_4_acc: 0.9623 - val_dense_4_acc_1: 0.9623 - val_dense_4_acc_2: 0.9623 - val_dense_6_acc: 0.8104 - val_dense_6_acc_1: 0.8104 - val_dense_6_acc_2: 0.8104\n",
      "Epoch 22/30\n",
      "120/120 [==============================] - 15s 127ms/step - loss: 0.0458 - dense_2_loss: 0.0049 - dense_4_loss: 0.0019 - dense_6_loss: 0.0390 - dense_2_acc: 0.9987 - dense_2_acc_1: 0.9987 - dense_2_acc_2: 0.9987 - dense_4_acc: 0.9995 - dense_4_acc_1: 0.9995 - dense_4_acc_2: 0.9995 - dense_6_acc: 0.9892 - dense_6_acc_1: 0.9892 - dense_6_acc_2: 0.9892 - val_loss: 1.8992 - val_dense_2_loss: 0.1602 - val_dense_4_loss: 0.3322 - val_dense_6_loss: 1.4068 - val_dense_2_acc: 0.9778 - val_dense_2_acc_1: 0.9778 - val_dense_2_acc_2: 0.9778 - val_dense_4_acc: 0.9639 - val_dense_4_acc_1: 0.9639 - val_dense_4_acc_2: 0.9639 - val_dense_6_acc: 0.8285 - val_dense_6_acc_1: 0.8285 - val_dense_6_acc_2: 0.8285\n",
      "Epoch 23/30\n",
      "120/120 [==============================] - 15s 128ms/step - loss: 0.0352 - dense_2_loss: 0.0058 - dense_4_loss: 0.0027 - dense_6_loss: 0.0267 - dense_2_acc: 0.9984 - dense_2_acc_1: 0.9984 - dense_2_acc_2: 0.9984 - dense_4_acc: 0.9995 - dense_4_acc_1: 0.9995 - dense_4_acc_2: 0.9995 - dense_6_acc: 0.9935 - dense_6_acc_1: 0.9935 - dense_6_acc_2: 0.9935 - val_loss: 2.1802 - val_dense_2_loss: 0.1728 - val_dense_4_loss: 0.3319 - val_dense_6_loss: 1.6755 - val_dense_2_acc: 0.9777 - val_dense_2_acc_1: 0.9777 - val_dense_2_acc_2: 0.9777 - val_dense_4_acc: 0.9677 - val_dense_4_acc_1: 0.9677 - val_dense_4_acc_2: 0.9677 - val_dense_6_acc: 0.7749 - val_dense_6_acc_1: 0.7749 - val_dense_6_acc_2: 0.7749\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 24/30\n",
      "120/120 [==============================] - 15s 128ms/step - loss: 0.0457 - dense_2_loss: 0.0050 - dense_4_loss: 0.0044 - dense_6_loss: 0.0363 - dense_2_acc: 0.9991 - dense_2_acc_1: 0.9991 - dense_2_acc_2: 0.9991 - dense_4_acc: 0.9991 - dense_4_acc_1: 0.9991 - dense_4_acc_2: 0.9991 - dense_6_acc: 0.9902 - dense_6_acc_1: 0.9902 - dense_6_acc_2: 0.9902 - val_loss: 2.0480 - val_dense_2_loss: 0.1421 - val_dense_4_loss: 0.2738 - val_dense_6_loss: 1.6322 - val_dense_2_acc: 0.9804 - val_dense_2_acc_1: 0.9804 - val_dense_2_acc_2: 0.9804 - val_dense_4_acc: 0.9677 - val_dense_4_acc_1: 0.9677 - val_dense_4_acc_2: 0.9677 - val_dense_6_acc: 0.8054 - val_dense_6_acc_1: 0.8054 - val_dense_6_acc_2: 0.8054\n",
      "Epoch 25/30\n",
      "120/120 [==============================] - 16s 130ms/step - loss: 0.0373 - dense_2_loss: 0.0015 - dense_4_loss: 0.0018 - dense_6_loss: 0.0340 - dense_2_acc: 0.9996 - dense_2_acc_1: 0.9996 - dense_2_acc_2: 0.9996 - dense_4_acc: 0.9995 - dense_4_acc_1: 0.9995 - dense_4_acc_2: 0.9995 - dense_6_acc: 0.9915 - dense_6_acc_1: 0.9915 - dense_6_acc_2: 0.9915 - val_loss: 2.0550 - val_dense_2_loss: 0.1719 - val_dense_4_loss: 0.3811 - val_dense_6_loss: 1.5020 - val_dense_2_acc: 0.9777 - val_dense_2_acc_1: 0.9777 - val_dense_2_acc_2: 0.9777 - val_dense_4_acc: 0.9586 - val_dense_4_acc_1: 0.9586 - val_dense_4_acc_2: 0.9586 - val_dense_6_acc: 0.8208 - val_dense_6_acc_1: 0.8208 - val_dense_6_acc_2: 0.8208\n",
      "Epoch 26/30\n",
      "120/120 [==============================] - 16s 129ms/step - loss: 0.0356 - dense_2_loss: 0.0011 - dense_4_loss: 0.0037 - dense_6_loss: 0.0307 - dense_2_acc: 0.9999 - dense_2_acc_1: 0.9999 - dense_2_acc_2: 0.9999 - dense_4_acc: 0.9993 - dense_4_acc_1: 0.9993 - dense_4_acc_2: 0.9993 - dense_6_acc: 0.9908 - dense_6_acc_1: 0.9908 - dense_6_acc_2: 0.9908 - val_loss: 2.0945 - val_dense_2_loss: 0.1876 - val_dense_4_loss: 0.3946 - val_dense_6_loss: 1.5123 - val_dense_2_acc: 0.9777 - val_dense_2_acc_1: 0.9777 - val_dense_2_acc_2: 0.9777 - val_dense_4_acc: 0.9604 - val_dense_4_acc_1: 0.9604 - val_dense_4_acc_2: 0.9604 - val_dense_6_acc: 0.8154 - val_dense_6_acc_1: 0.8154 - val_dense_6_acc_2: 0.8154\n",
      "Epoch 27/30\n",
      "120/120 [==============================] - 15s 127ms/step - loss: 0.0317 - dense_2_loss: 7.7082e-04 - dense_4_loss: 0.0021 - dense_6_loss: 0.0288 - dense_2_acc: 0.9995 - dense_2_acc_1: 0.9995 - dense_2_acc_2: 0.9995 - dense_4_acc: 0.9996 - dense_4_acc_1: 0.9996 - dense_4_acc_2: 0.9996 - dense_6_acc: 0.9928 - dense_6_acc_1: 0.9928 - dense_6_acc_2: 0.9928 - val_loss: 2.0661 - val_dense_2_loss: 0.2041 - val_dense_4_loss: 0.2935 - val_dense_6_loss: 1.5685 - val_dense_2_acc: 0.9750 - val_dense_2_acc_1: 0.9750 - val_dense_2_acc_2: 0.9750 - val_dense_4_acc: 0.9686 - val_dense_4_acc_1: 0.9686 - val_dense_4_acc_2: 0.9686 - val_dense_6_acc: 0.8222 - val_dense_6_acc_1: 0.8222 - val_dense_6_acc_2: 0.8222\n",
      "Epoch 28/30\n",
      "120/120 [==============================] - 15s 125ms/step - loss: 0.0302 - dense_2_loss: 0.0042 - dense_4_loss: 0.0015 - dense_6_loss: 0.0245 - dense_2_acc: 0.9992 - dense_2_acc_1: 0.9992 - dense_2_acc_2: 0.9992 - dense_4_acc: 0.9993 - dense_4_acc_1: 0.9993 - dense_4_acc_2: 0.9993 - dense_6_acc: 0.9924 - dense_6_acc_1: 0.9924 - dense_6_acc_2: 0.9924 - val_loss: 2.0984 - val_dense_2_loss: 0.1942 - val_dense_4_loss: 0.3203 - val_dense_6_loss: 1.5839 - val_dense_2_acc: 0.9741 - val_dense_2_acc_1: 0.9741 - val_dense_2_acc_2: 0.9741 - val_dense_4_acc: 0.9695 - val_dense_4_acc_1: 0.9695 - val_dense_4_acc_2: 0.9695 - val_dense_6_acc: 0.8149 - val_dense_6_acc_1: 0.8149 - val_dense_6_acc_2: 0.8149\n",
      "Epoch 29/30\n",
      "120/120 [==============================] - 15s 125ms/step - loss: 0.0419 - dense_2_loss: 0.0017 - dense_4_loss: 0.0076 - dense_6_loss: 0.0326 - dense_2_acc: 0.9995 - dense_2_acc_1: 0.9995 - dense_2_acc_2: 0.9995 - dense_4_acc: 0.9987 - dense_4_acc_1: 0.9987 - dense_4_acc_2: 0.9987 - dense_6_acc: 0.9913 - dense_6_acc_1: 0.9913 - dense_6_acc_2: 0.9913 - val_loss: 2.2698 - val_dense_2_loss: 0.2750 - val_dense_4_loss: 0.3888 - val_dense_6_loss: 1.6060 - val_dense_2_acc: 0.9663 - val_dense_2_acc_1: 0.9663 - val_dense_2_acc_2: 0.9663 - val_dense_4_acc: 0.9645 - val_dense_4_acc_1: 0.9645 - val_dense_4_acc_2: 0.9645 - val_dense_6_acc: 0.8172 - val_dense_6_acc_1: 0.8172 - val_dense_6_acc_2: 0.8172\n",
      "Epoch 30/30\n",
      "120/120 [==============================] - 15s 125ms/step - loss: 0.0349 - dense_2_loss: 6.2375e-04 - dense_4_loss: 0.0087 - dense_6_loss: 0.0255 - dense_2_acc: 0.9996 - dense_2_acc_1: 0.9996 - dense_2_acc_2: 0.9996 - dense_4_acc: 0.9988 - dense_4_acc_1: 0.9988 - dense_4_acc_2: 0.9988 - dense_6_acc: 0.9932 - dense_6_acc_1: 0.9932 - dense_6_acc_2: 0.9932 - val_loss: 2.0541 - val_dense_2_loss: 0.2243 - val_dense_4_loss: 0.3184 - val_dense_6_loss: 1.5113 - val_dense_2_acc: 0.9727 - val_dense_2_acc_1: 0.9727 - val_dense_2_acc_2: 0.9727 - val_dense_4_acc: 0.9691 - val_dense_4_acc_1: 0.9691 - val_dense_4_acc_2: 0.9691 - val_dense_6_acc: 0.8345 - val_dense_6_acc_1: 0.8345 - val_dense_6_acc_2: 0.8345\n"
     ]
    }
   ],
   "source": [
    "history = model.fit_generator(inputgenerator,\n",
    "                             steps_per_epoch=120,\n",
    "                             epochs=30,\n",
    "                             validation_data=testgenerator,\n",
    "                             validation_steps=35)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "history_dict = history.history\n",
    "\n",
    "dense_2_loss = history_dict['dense_2_loss']\n",
    "val_dense_2_loss = history_dict['val_dense_2_loss']\n",
    "\n",
    "epochs = range(1, len(dense_2_loss) + 1)\n",
    "\n",
    "plt.plot(epochs, dense_2_loss, 'bo', label = 'MNIST Training loss')\n",
    "plt.plot(epochs, val_dense_2_loss, 'b', label = 'MNIST Validation loss')\n",
    "plt.title('MNIST Training and validation loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.clf()\n",
    "\n",
    "dense_2_acc = history_dict['dense_2_acc']\n",
    "val_dense_2_acc = history_dict['val_dense_2_acc']\n",
    "\n",
    "plt.plot(epochs, dense_2_acc, 'bo', label = 'MNIST Training acc')\n",
    "plt.plot(epochs, val_dense_2_acc, 'b', label = 'MNIST Validation acc')\n",
    "plt.title('MNIST Training and validation accuracy')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('acc')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.clf()\n",
    "\n",
    "dense_4_loss = history_dict['dense_4_loss']\n",
    "val_dense_4_loss = history_dict['val_dense_4_loss']\n",
    "\n",
    "plt.plot(epochs, dense_4_loss, 'bo', label = 'USPS Training loss')\n",
    "plt.plot(epochs, val_dense_4_loss, 'b', label = 'USPS Validation loss')\n",
    "plt.title('USPS Training and validation loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.clf()\n",
    "\n",
    "dense_4_acc = history_dict['dense_4_acc']\n",
    "val_dense_4_acc = history_dict['val_dense_4_acc']\n",
    "\n",
    "plt.plot(epochs, dense_4_acc, 'bo', label = 'USPS Training acc')\n",
    "plt.plot(epochs, val_dense_4_acc, 'b', label = 'USPS Validation acc')\n",
    "plt.title('USPS Training and validation acc')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('acc')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.clf()\n",
    "\n",
    "dense_6_loss = history_dict['dense_6_loss']\n",
    "val_dense_6_loss = history_dict['val_dense_6_loss']\n",
    "\n",
    "plt.plot(epochs, dense_6_loss, 'bo', label = 'SVHN Training loss')\n",
    "plt.plot(epochs, val_dense_6_loss, 'b', label = 'SVHN Validation loss')\n",
    "plt.title('SVHN Training and validation loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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czcIya9Vyf/TRil3O7po82f1HPwrx5eS4n3lmeD9xYmbi2b694uazZIn7iy+6jxrlPmeO+9atFTPvZB995H7JJe7Vq7vvs4/75Ze7f/JJxc0fyPc09rEZ38mX9ZVtScE9JIAWLcI/dIsW2ZcQ3MMPFNxffrn4MvPmuR96qPu++7o/99yei213bd3qPm2a+333uZ9/vnvTpjt2+DVquJ9wgvuVV7o/9lj4xy3cISxf7j5unPtvf+t+6qnutWvv+FyzZu4XXeT+8MMhSW7aVPa41q8PO/42bcI8GzZ0v/VW91mzduxwr7jCfcOGit0eZfXmm+6dO4d4Dj7Y/YEH3NeuDXEddZR7kybu3367Z2JZv979n/90b98+fB/HHOPes6f7737n/o9/hIOXr74qPmFs2uQ+Y4b7iBEhyZ9xRjjYST6oA/e6dd07dXK/7rpQ9qOP3LdsKV/M770XEn7hAeONN1b8gYV7+knBQtnskZeX5/n5+ZkOIzb/+Q+8/HK4qHT44eHVpElozpqu9eth7lz46COYPTv83b4d7r8f2rUre0z//CdcdhnceivceWfJZVesgB49QnXSQw/BDTeUfXlxcw9VQa+/Hl5vvQWrVoVprVpBhw47Xu3awb77pjffzZthxgx4770dr8JrR7VqQV4enHLKjlfjxqnn88UX8Je/wN/+Bt98A23bwvXXw8UXQ+3aocy2bfB//xeqF/Ly4Nln90x9cyH3sN3uuAMmT4Yf/CBUJf7v/+7cXUt+Ppx8coj9iSfii2fxYhg2DB59NGyzY44J1S6LFsG8eaHqc8uWHeXr1dvx/9WyJSxdCjNnwpw5O8rVrg1t2oTfQLt2oU5///1D1d306eE1Y8aOhiT77hvKtW8Pxx8fqldXrdr5tXp16nEHHRSq2n75S2jQIJ5tZGbT3T2v1HJKCpXDV1/Bb38LI0eGH1Py11KnDrRuveNHXPhq1SrshJN3/rNnhwt8hZ+vXTv8gxQUhAvBAweGf94aad7LPnNm+Kfu2BH+/W+oXr30z2zYEFokPfccXHddSA7pfC5OS5aEBPDGG+Hv8uVhfG4u/PjH4XXGGWHnVpG++GLnJDF9ekgehctOThJbt8Ijj8Azz4Sd/rnnhmRw+unhN5HKCy/Az38O++yz44J/WW3fDu+/D2vXhuRVs2Z4Fb4vOu6990IyePvtkNh+97vQzLq45Hn77TBoUPg99OxZ9viK4x6+yyFD4MUXw7jzzgv18Z0777zNtm0LiWP+/PCaN2/H+yVLoFGjnXf+7dqF/7nS/k+2bQsHGB98sCNRfPghrFu3c7l69aB+/fBKfl+/fvhfvvRS2G+/its2qaSbFDJeHVTWVzZWH5Vk61b3IUPc69UL9Yi33BJOvQsK3N94w33YsHA6ec457q1bh/rGoqey4F6tmvuRR7pfeKH7HXe4jx3rPn/+jqqOlSvde/cOZU86Kb26ylWr3Fu1cj/kkHDKXRbbtrn/6ldheeee675wYRgXp9WrQ9XK+PHujzwSln/++WEdCrfTwQe79+njPny4+2efxRtPKhs3huqCBx8M31WTJjt/jwccEL7vssQ2b16oJqlWzf3uu9PfzvPnh99b8vWRdF+HHBK2cTpVV5s3hyq4nJyy/45S+f778D9z5JE7qtX+8Af3xYvLN7/Nm3c/pmRbt7p/+qn755+7f/ddPNcfyoPKUH1kZmcDfwaqA4+6+z1FpjcHRgD1ozI3u/srJc2zKp0pvP9+aNo5fTqceWY44jniiJI/s2ULfP55OMJZsCCcarZpA0ceuaNqoSRPPx2WuWED3HNPOKpKVTXlDhdeGI5EJ08ON6qVx5Ah4Wh3+/ZwxnPkkXD00eF11FHh76GHlnxEtm1bqBL4+utwRlX4d8mSUD1Q+Pruu50/t+++O6rhOncOZwPHHlv8UXemLF0ajr7XrQvbfP/9yz6PdevgiitCS7Bzz4URI8IRaVGrV8OYMfD446HlVLVq0LUrXHIJNGsWzmI2bQp/k98njzv44ND6Kp3fW6E5c0K1Srdu4YyhPN/Bhg2hieajj8KaNaEV3C9/Cb16lS2WvVXGzxQIO/nPgEOBmsBM4OgiZYYDV0XvjwYWlTbfqnCm8M034cKlmXvjxu6jR1dcS4l0LFvm/pOfhKOsM85I3ZrowQfD9Acf3P3lzZ4djsxvuMG9a9dwETb5qLNmTfdjjw0XZq+/3v3ii8OF1DZt3Bs1CkfAqY5W69YNR8jnnON+9dXhQvGYMe7vv+/+9dd7dptWBtu3u//pT+HieKtWYbu7hyPVV18NZ0iFF8OPPtr93nvdv/hiz8V3//1h2U88UfbPfvGFe15e+J/52c/cp06t+PiqOjJ9pmBmpwAD3f2saPj3URL6Y1KZvwEL3f3eqPyD7t6xpPlm85mCeziC++1vw1HtddeFOv4DDshMLP/4B9x4Yzhq+/Ofwz0IZqGdeefO4Yjz2WfjObL+/vtQF/vxx+GieOHr668hJyfU8TZqFI5KC98XHa5fv/Id9VcGb78NF10UtvHPfgYvvQTLlsGBB4YLvpdeGi5O7+ltt21b+F3Nnh1ezZql97n8/PBb/P770D1Mjx6xhlllVYYzhQsJVUaFw5cAQ4qUaQzMBgqA74D2xcxrAJAP5Ddv3jyGHBq/WbNCs0Vw79gxNHurDBYudD/ttBDXT3/qPnNmqC9u1SpcU5DstGxZ+L1Vrx6+12efDdczMm3BgnCG16VLemdyTz8dmjjn5ob/ISk/Mn2fAtArRVL4f0XK/Ar4dfT+FGAuUK2k+VaW6qN07z3Yvt39L38JVSQNGoS20nFfcC2rbdvcH3oo3BAFoYqhsiQtKb/t2zN/D0Mqw4aF39nQocWX2bbN/fbbQ7lOnSrmAvXerjIkhVOAiUnDvwd+X6TMHKBZ0vBC4OCS5lsZkkK6dymvXever1+Y/pOfuK9YkZl40zV3bqiff/rpTEciVdn27eHaUp06oZVOUevWuffqFf5v+vevHGc4VUFlSAo1op18S3ZcaD6mSJkJQP/o/VHAMqJ7J4p7VYak0KLFzgmh8NWixY4yH38cLuaZud91V+U7OxDJpKVLQzPsTp12brK5dGlovmoW7oze2xoLxCndpBBbh3juvhW4FpgIfAyMcfc5ZjbIzAovFf0a+B8zmwk8FSWISn83XWk9n44ZE5rLrVgRbvi65Zay3ZEsUtU1bRqaK//3v6FjQQhNtDt0gE8/DTej/frXakiQCbqjuRxyc8PdkUU1bx7u2Pzzn8MdqmPGhB+/iOzKo3thXnop3PV8553hDukXXwx34UvFSrf1kY5fy2Hw4J37d4Fw80ytWiEhXH996BdGCUGkeGbw17+GpsW33BLOrqdOVULItDR7wJFk/fqFv7fcEqqMcnLC3ZbLl4c7hi+6KLPxiWSLnJxwh/Mbb4Q+lGrWzHREouqj3bB9e3gc5W23hS4bxo4N3TiIiFQ26VYf6UyhnNatC/3FjBsX7hL929/i7+VQRCRuSgrl8MUX8NOfhm6lH344XENQKwkRqQqUFMpo+vTQ98r338P48XDOOZmOSESk4qj1URk89xycdlp4oMm77yohiEjVo6SQBvfw7IELLgiPRpw6NTzDQESkqlH1USk2bw7PnX388fBgkcceS/+ZvSIi2UZnCiVYuTI88/bxx8NzD558UglBRKo2nSkU45NPoHv38MD7J5+Evn0zHZGISPyUFFJ4/fXQJ0vNmvDmm6EfIxGRvYGqj4rYsAHOOw+aNAkXlJUQRGRvoqRQxH/+A2vXwv33h95QRUT2JkoKRUyYEHo87dw505GIiOx5SgpFTJgAp5+uVkYisndSUkiycCHMnw/dumU6EhGRzFBSSPLqq+GvkoKI7K2UFJK8+iq0bAmtW2c6EhGRzIg1KZjZ2WY2z8wWmNnNKaY/bGYzotd8M1sVZzwl2bQpPP2pWzd1gy0ie6/Ybl4zs+rAUKALUABMM7Px7j63sIy735hU/pfA8XHFU5p33gkPzjn77ExFICKSeXGeKXQAFrj7QnffDIwGzi2hfF/gqRjjKdGECeEO5h/9KFMRiIhkXpxJoQmwNGm4IBq3CzNrAbQE3ihm+gAzyzez/BUrVlR4oBCSwmmnQd26scxeRCQrxJkUUtXMezFl+wDPuvu2VBPdfbi757l7Xk5OToUFWGjJEpg7N1QdjRoV7mSuVi38HTWqwhcnIlJpxdkhXgHQLGm4KbCsmLJ9gGtijKVEhU1Rt22DAQNg/fowvHhxGAbo1y8zsYmI7ElxnilMA1qbWUszq0nY8Y8vWsjMjgAOBN6LMZYSvfoqNGsGQ4fuSAiF1q+HW27JTFwiIntabEnB3bcC1wITgY+BMe4+x8wGmVmPpKJ9gdHuXlzVUqw2b4ZJk0JT1KVLU5dZsmTPxiQikimxPk/B3V8BXiky7rYiwwPjjKE0770Ha9aEpDBxYqgyKqp58z0fl4hIJuz1dzRPmAA1aoSmqIMHQ506O0+vUyeMFxHZGygpTIBTT4UDDggXk4cPhxYtwl3NLVqEYV1kFpG9xV79OM5ly2DWLLjnnh3j+vVTEhCRvVdaZwpm1tPM6iUN1zez8+ILa89Qr6giIjtLt/rodndfXTjg7quA2+MJac959VU45BBo0ybTkYiIVA7pJoVU5bK66mnrVnjttXAXs3pFFREJ0k0K+Wb2kJkdZmaHmtnDwPQ4A4vb1KmwapWqjkREkqWbFH4JbAaeBsYAG8hgtxQVYcIEqF4dzjwz05GIiFQeaVUBufs6YJeH5GSzCRPglFOgfv1MRyIiUnmk2/roNTOrnzR8oJlNjC+seH31FXzwgaqORESKSrf6qGHU4ggAd/8OODiekOI3MUpnesqaiMjO0k0K280s0QOQmeVS/LMRKr1XX4VGjaBdu0xHIiJSuaTbrPQW4B0zmxwNnwYMiCekeG3bFs4UuncPD9IREZEd0r3Q/KqZ5RESwQzgBUILpKyTnw/ffqvrCSIiqaSVFMzsCuB6wtPTZgAnEx6Kk3WPuZ8wIZwhdOmS6UhERCqfdCtQrgdOBBa7+xnA8cCK2KKK0YQJ0KEDNGiQ6UhERCqfdJPCRnffCGBmtdz9E+CI+MKKx8qVMG2aqo5ERIqT7oXmgug+heeB18zsO2BZfGHF49//Bnc1RRURKU66F5p7Rm8HmtmbQD3g1diiismrr0LDhpCXl+lIREQqpzL3dOruk0svVfls3x6SwllnqSmqiEhxYt09mtnZZjbPzBaYWcq+k8zsIjOba2ZzzOzJuGL58ENYsUJVRyIiJYntmQhmVh0YCnQBCoBpZjbe3ecmlWkN/B7o5O7fmVlsXWdMmBCem3DWWXEtQUQk+8X5oJwOwAJ3XwhgZqOBc4G5SWX+Bxga9aWEu38dVzBXXgnHHw85OXEtQUQk+8VZfdQEWJo0XBCNS3Y4cLiZ/dfMpphZysodMxtgZvlmlr9iRfluj2jYEM45p1wfFRHZa8SZFFI95LJoJ3o1gNZAZ6Av8GhyF92JD7kPd/c8d8/L0aG+iEhs4kwKBUCzpOGm7HpvQwHwgrtvcffPgXmEJCEiIhkQZ1KYBrQ2s5ZmVhPoA4wvUuZ54AwAM2tIqE5aGGNMIiJSgtiSgrtvBa4FJgIfA2PcfY6ZDTKzHlGxicA3ZjYXeBP4rbt/E1dMIiJSMnPPrmfl5OXleX5+fqbDEBHJKmY23d1L7c9B9/aKiEiCkoKIiCQoKYiISIKSgoiIJCgpiIhIgpKCiIgkKCmIiEiCkoKIiCQoKYiISIKSgoiIJCgpiIhIgpKCiIgkKCmIiEiCkoKIiCQoKYiISIKSgoiIJCgpiIhIgpKCiIgkKCmIiEiCkoKIiCTEmhTM7Gwzm2dmC8zs5hTT+5vZCjObEb2uiDMeEREpWY24Zmxm1YGhQBegAJhmZuPdfW6Rok+7+7VxxSEiIumL80yhA7DA3Re6+2ZgNHBujMsTEZHdFGdSaAIsTRouiMYVdYGZzTKzZ82sWaoZmdkAM8s3s/wVK1bEEauIiBBvUrAU47zI8ItArru3BSYBI1LNyN2Hu3ueu+fl5ORUcJgiIlIozqRQACQf+TcFliUXcPdv3H1TNPh3oH2M8YiISCniTArTgNZm1tLMagJ9gPFZlEYMAAAT+0lEQVTJBcyscdJgD+DjGOMREZFSxNb6yN23mtm1wESgOvCYu88xs0FAvruPB64zsx7AVuBboH9c8YiISOnMvWg1f+WWl5fn+fn5mQ5DRCSrmNl0d88rrZzuaBYRkQQlBRERSVBSEBGRBCUFERFJUFIQEZEEJQUREUlQUhARkQQlBRERSVBSEBGRBCUFERFJUFIQEZEEJQUREUlQUhARkQQlBRERSVBSEBGRBCUFERFJUFIQEZEEJQUREUlQUhARkYRYk4KZnW1m88xsgZndXEK5C83MzazU54eKiEh8YksKZlYdGAp0A44G+prZ0SnK7Q9cB0yNKxYREUlPnGcKHYAF7r7Q3TcDo4FzU5S7E7gP2BhjLCIikoYaMc67CbA0abgAOCm5gJkdDzRz95fM7DfFzcjMBgADAJo3bx5DqCLZYcuWLRQUFLBxo46hJLXatWvTtGlT9tlnn3J9Ps6kYCnGeWKiWTXgYaB/aTNy9+HAcIC8vDwvpbhIlVVQUMD+++9Pbm4uZqn+xWRv5u588803FBQU0LJly3LNI87qowKgWdJwU2BZ0vD+wLHAW2a2CDgZGK+LzSLF27hxIw0aNFBCkJTMjAYNGuzWmWScSWEa0NrMWppZTaAPML5woruvdveG7p7r7rnAFKCHu+fHGJNI1lNCkJLs7u8jtqTg7luBa4GJwMfAGHefY2aDzKxHXMsVEZHyi/U+BXd/xd0Pd/fD3H1wNO42dx+fomxnnSWIVKxRoyA3F6pVC39Hjdr9eQ4ePJhjjjmGtm3b0q5dO6ZOncrAgQP5/e9/v1O5GTNmcNRRRwGQm5vLypUrE9PeeustunfvDsDjjz9OtWrVmDVrVmL6sccey6JFi3aaX8+ePWnXrh2tWrWiXr16tGvXjnbt2vHuu++mHfvQoUMZVcpGmDp1KjfeeGPa86xq4rzQLCIZNGoUDBgA69eH4cWLwzBAv37lm+d7773HSy+9xAcffECtWrVYuXIlmzdvpm/fvnTr1o0//vGPibKjR4/m4osvTmu+TZs2ZfDgwTz99NPFlhk3bhwQEsoDDzzASy+9lLLc1q1bqVEj9a7tmmuuKTWWk046iZNOOqnUclWVurkQqaJuuWVHQii0fn0YX17Lly+nYcOG1KpVC4CGDRtyyCGHcMQRR1C/fn2mTt1xD+qYMWPo06dPWvPt3r07c+bMYd68eeWKq2nTptx555106tSJcePG8de//pUTTzyR4447jl69erFhwwYAbr31Vv70pz8BcOqpp3LzzTfToUMHjjjiiMQZx6RJkzjvvPMS5S+//HJOP/10Dj30UIYOHZpY5u23386RRx5Jly5d6N27d2K+yV544QVOOukkjj/+eLp27crXX38NwJo1a7j00ktp06YNbdu25fnnnwfg5Zdf5oQTTuC4446ja9eu5doWu0tJQaSKWrKkbOPT0bVrV5YuXcrhhx/O1VdfzeTJkxPT+vbty+jRowGYMmUKDRo0oHXr1onpZ5xxRqLK54orrthpvtWqVeOmm27i7rvvLndsdevW5b///S+9evWiV69eTJs2jZkzZ3LYYYfx+OOPp/yMu/P+++9z//33M2jQoJRl5s+fz2uvvcaUKVO47bbb2LZtG1OmTOGll15i5syZjB07lmnTpqX87GmnncaUKVP48MMPOf/883nwwQcBGDhwIDk5OcyePZuZM2dy+umn8+WXX3LVVVcxbtw4Zs6cmdiWe5qSgkgVVdx9nrtz/+d+++3H9OnTGT58ODk5OfTu3Tuxw+3Tpw/PPvss27dvZ/To0fTt23enz7755pvMmDGDGTNm8Oijj+4y74svvpgpU6bw+eeflyu23r17J97PmjWLH/7wh7Rp04bRo0czZ86clJ85//zzAWjfvv0u1zAKde/enZo1a3LwwQdz0EEHsWLFCt555x3OO+88atWqxQEHHJC4PlLUkiVL6Nq1K23atOGhhx5KxDFp0qREVZaZceCBB/Lee+9xxhln0KJFCwAOOuigcm2H3aWkIFJFDR4MdersPK5OnTB+d1SvXp3OnTtzxx13MGTIEMaOHQtAs2bNyM3NZfLkyYwdO5aLLrqoTPOtUaMGv/71r7n33nvLFVfdunUT73/+858zbNgwZs+eza233lpsu/3CarDq1auzdevWEsskl3NP7x7aa665hhtvvJHZs2fzl7/8JRGHu+/SdDTVuExQUhCpovr1g+HDoUULMAt/hw8v/0VmgHnz5vHpp58mhmfMmJE4soVQhXTjjTdy2GGH0bRp0zLPv3///kyaNIkVK1aUP0hg3bp1/OAHP2DLli08+eSTuzWvVE499VTGjx/Ppk2bWLNmDa+88krKcqtXr6ZJkya4OyNGjEiM79q1K0OGDAFCMvjuu+/o1KkTb7zxBosXLwbg22+/rfC406GkIFKF9esHixbB9u3h7+4kBIC1a9dy6aWXcvTRR9O2bVvmzp3LwIEDE9N79erFnDlz0r7AXFTNmjW57rrrEhdky2vQoEF06NCBLl26cPTRu3TOvNtOOeUUzj77bNq2bcuFF17IiSeeSL169XYpN3DgQHr27Mnpp59Oo0aNEuNvv/12vvrqK4499ljatWvH22+/TaNGjRg2bBjnnnsuxx13HP1298sqJ0v3NKiyyMvL8/x83c4ge6ePP/440fZfMmvt2rXst99+rFu3jlNPPZURI0bQtm3bTIcFpP6dmNl0dy+1GyHdpyAiUg6XX3458+bNY+PGjVx22WWVJiHsLiUFEZFyKOlGu2ymawoiIpKgpCAiIglKCiIikqCkICIiCUoKIlImmeo6u7RlFKdz584UNmP/yU9+wqpVq3YpM3DgQB544IES5/P8888zd+7cxPBtt93GpEmTSvxMNlJSEJG0JXedPWvWLCZNmkSzZs3o27fvLq1xytN1dkl2dxkAr7zyCvXr10+7fLKiSWHQoEGceeaZ5ZpXZaYmqSJZ6oYbYMaMip1nu3aQogfohFRdZxcq7Dq78FkEY8aMYeLEiWktt3v37vznP/9h3rx5HHHEESnLJHfPnWoZV111FdOmTWPDhg1ceOGF3HHHHbvMIzc3l/z8fBo2bMjgwYN54oknaNasGTk5ObRv3x6Av//97wwfPpzNmzfTqlUrRo4cyYwZMxg/fjyTJ0/mrrvuYuzYsdx55510796dCy+8kNdff53f/OY3bN26lRNPPJFhw4ZRq1YtcnNzufTSS3nxxRfZsmULzzzzDEceeeROMS1atIhLLrmEdevWATBkyBA6duwIwH333cfIkSOpVq0a3bp145577mHBggVceeWVrFixgurVq/PMM89w2GGHpbWd06EzBRFJW6a7zi5pGYMHDyY/P59Zs2YxefLknaqjipo+fTqjR4/mww8/5Lnnntup6+vzzz8/0e32UUcdxT/+8Q86duxIjx49uP/++5kxY8ZOO+GNGzfSv39/nn76aWbPns3WrVsZNmxYYnrDhg354IMPuOqqq1JWUR188MG89tprfPDBBzz99NNcd911AEyYMIHnn3+eqVOnMnPmTG666SYA+vXrxzXXXMPMmTN59913ady4cYnbrKx0piCSpUo6oo9LYdfZb7/9Nm+++Sa9e/fmnnvuoX///vTp04eOHTvy4IMPFtt1duGZReHT05JdfPHFDB48uMSus0taxpgxYxg+fDhbt25l+fLlzJ07t9i7jN9++2169uxJnagb2R49djw2/qOPPuLWW29l1apVrF27lrPOOqvEbTJv3jxatmzJ4YcfDsCll17K0KFDueGGG4Cdu+d+7rnndvn8li1buPbaa5kxYwbVq1dn/vz5QOhe+xe/+EUixoMOOog1a9bwxRdf0LNnTwBq165dYmzlsVecKcTxnFqRvVUmu84ubhmff/45DzzwAK+//jqzZs3inHPOKba77ELFdVPdv39/hgwZwuzZs7n99ttLnU9p/ceV1j33ww8/TKNGjZg5cyb5+fls3rw5Md9U3WvHLdakYGZnm9k8M1tgZjenmH6lmc02sxlm9o6ZVXh3hoXPqV28GNx3PKdWiUGk7CpD19mplvH9999Tt25d6tWrx1dffcWECRNKXM5pp53GuHHj2LBhA2vWrOHFF19MTFuzZg2NGzdmy5YtjEraUey///6sWbNml3kdeeSRLFq0iAULFgAwcuRITj/99LTXefXq1TRu3Jhq1aoxcuRItm3bBoSquscee4z10TNVv/32Ww444ACaNm2aeHznpk2bEtMrSmxJwcyqA0OBbsDRQN8UO/0n3b2Nu7cD7gMequg44nhOrcjeqjJ0nZ1qGccddxzHH388xxxzDJdddhmdOnUqcTknnHACvXv3pl27dlxwwQX88Ic/TEy78847Oemkk+jSpctOF4X79OnD/fffz/HHH89nn32WGF+7dm3++c9/0qtXL9q0aUO1atW48sor017nq6++mhEjRnDyySczf/78xMOCzj77bHr06EFeXh7t2rVLVLeNHDmSRx55hLZt29KxY0e+/PLLtJeVjti6zjazU4CB7n5WNPx7AHf/YzHl+wI/d/duJc23rF1nV6sWzhB2XV7oY14km6jrbEnH7nSdHWf1URNgadJwQTRuJ2Z2jZl9RjhTuC7VjMxsgJnlm1l+WZ/IFMdzakVEqqo4k0Kqqzi7HLO7+1B3Pwz4HXBrqhm5+3B3z3P3vJycnDIFEddzakVEqqI4k0IB0CxpuCmwrITyo4HzKjqIOJ5TK5JJ2fa0RNmzdvf3Eed9CtOA1mbWEvgC6APsdD+6mbV298KmDOcAnxKDfv2UBKRqqF27Nt988w0NGjQotkml7L3cnW+++Wa37l+ILSm4+1YzuxaYCFQHHnP3OWY2CMh39/HAtWZ2JrAF+A64NK54RKqCpk2bUlBQUGKTTdm71a5du1zNgQvF1vooLmVtfSQiIpWj9ZGIiGQZJQUREUlQUhARkYSsu6ZgZiuAxUVGNwRWpiierara+kDVW6eqtj5Q9dapqq0P7N46tXD3Um/0yrqkkIqZ5adzASVbVLX1gaq3TlVtfaDqrVNVWx/YM+uk6iMREUlQUhARkYSqkhSGZzqAClbV1geq3jpVtfWBqrdOVW19YA+sU5W4piAiIhWjqpwpiIhIBVBSEBGRhKxOCqU9AzobmdmipOdWZ2UnT2b2mJl9bWYfJY07yMxeM7NPo78HZjLGsihmfQaa2RfR9zTDzH6SyRjLwsyamdmbZvaxmc0xs+uj8dn8HRW3Tln5PZlZbTN738xmRutzRzS+pZlNjb6jp82sZoUvO1uvKUTPgJ4PdCE8u2Ea0Nfd52Y0sN1kZouAPHfP2ptuzOw0YC3whLsfG427D/jW3e+JEviB7v67TMaZrmLWZyCw1t0fyGRs5WFmjYHG7v6Bme0PTCc8y6Q/2fsdFbdOF5GF35OFftHruvtaM9sHeAe4HvgV8Jy7jzazvwIz3X1YRS47m88UOgAL3H2hu28mPKTn3AzHJIC7/wf4tsjoc4ER0fsRxPBApbgUsz5Zy92Xu/sH0fs1wMeER+Vm83dU3DplJQ/WRoP7RC8HfgQ8G42P5TvK5qSQ1jOgs5AD/zaz6WY2INPBVKBG7r4cwj8wcHCG46kI15rZrKh6KWuqWpKZWS5wPDCVKvIdFVknyNLvycyqm9kM4GvgNeAzYJW7b42KxLLPy+akkNYzoLNQJ3c/AegGXBNVXUjlMww4DGgHLAcezGw4ZWdm+wFjgRvc/ftMx1MRUqxT1n5P7r7N3dsRHmXcATgqVbGKXm42J4WyPgM6K7j7sujv18A4wo+hKvgqqvctrP/9OsPx7BZ3/yr6p90O/J0s+56ieuqxwCh3fy4andXfUap1yvbvCcDdVwFvAScD9c2s8ImZsezzsjkpJJ4BHV2B7wOMz3BMu8XM6kYXyTCzukBX4KOSP5U1xrPjcauXAi9kMJbdVrjzjPQki76n6CLmP4CP3f2hpElZ+x0Vt07Z+j2ZWY6Z1Y/e7wucSbhO8iZwYVQslu8oa1sfAUTNy/7EjmdAD85wSLvFzA4lnB1AeH72k9m4Tmb2FNCZ0M3vV8DtwPPAGKA5sATo5e5ZcfG2mPXpTKiScGAR8L+F9fGVnZmdCrwNzAa2R6P/QKiDz9bvqLh16ksWfk9m1pZwIbk64eB9jLsPivYRo4GDgA+Bn7n7pgpddjYnBRERqVjZXH0kIiIVTElBREQSlBRERCRBSUFERBKUFEREJEFJQSRiZtuSetOcUZE975pZbnIvqyKVVY3Si4jsNTZE3QqI7LV0piBSiugZF/dG/du/b2atovEtzOz1qLO1182seTS+kZmNi/rCn2lmHaNZVTezv0f94/87ulMVM7vOzOZG8xmdodUUAZQURJLtW6T6qHfStO/dvQMwhHAXPdH7J9y9LTAKeCQa/wgw2d2PA04A5kTjWwND3f0YYBVwQTT+ZuD4aD5XxrVyIunQHc0iETNb6+77pRi/CPiRuy+MOl370t0bmNlKwoNdtkTjl7t7QzNbATRN7n4g6s75NXdvHQ3/DtjH3e8ys1cJD/F5Hng+qR99kT1OZwoi6fFi3hdXJpXkPmq2seOa3jnAUKA9MD2pF0yRPU5JQSQ9vZP+vhe9f5fQOy9AP8IjEwFeB66CxINSDihupmZWDWjm7m8CNwH1gV3OVkT2FB2RiOywb/Skq0Kvunths9RaZjaVcCDVNxp3HfCYmf0WWAH8Ihp/PTDczC4nnBFcRXjASyrVgX+ZWT3Cg6MejvrPF8kIXVMQKUV0TSHP3VdmOhaRuKn6SEREEnSmICIiCTpTEBGRBCUFERFJUFIQEZEEJQUREUlQUhARkYT/DwU6xqW4DIj8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.clf()\n",
    "\n",
    "dense_6_acc = history_dict['dense_6_acc']\n",
    "val_dense_6_acc = history_dict['val_dense_6_acc']\n",
    "\n",
    "plt.plot(epochs, dense_6_acc, 'bo', label = 'SVHN Training acc')\n",
    "plt.plot(epochs, val_dense_6_acc, 'b', label = 'SVHN Validation acc')\n",
    "plt.title('SVHN Training and validation acc')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('acc')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loss = history_dict['val_loss']\n",
    "loss.index(min(loss))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MNIST Val acc\n",
      "0.9758981355161437\n",
      "\n",
      "USPS Val acc\n",
      "0.965893587994543\n",
      "\n",
      "SVHN Val acc\n",
      "0.815825375089216\n"
     ]
    }
   ],
   "source": [
    "print('MNIST Val acc')\n",
    "print(val_dense_2_acc[4])\n",
    "print()\n",
    "\n",
    "print('USPS Val acc')\n",
    "print(val_dense_4_acc[4])\n",
    "print()\n",
    "\n",
    "print('SVHN Val acc')\n",
    "print(val_dense_6_acc[4])"
   ]
  }
 ],
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