12. More Advanced CNN Techniques.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# More Advanced Techniques (and Architectures)\n",
    "We have thus far studied a number of convolutional networks for performing classification, specifically using the Fashion MNIST dataset. However, as the classification problem becomes more complicated, it will require more complicated architectures and more involved methods for regularizing the data. For instance, object recognition requires more knowledge than simple handwriting digits (as in the case of recognizing shirts in Fashion MNIST). The complexities of the picture are more varied, color has an influence, and much more. Therefore, the simple convolutions that we used before might not be expressive enough without a number of additional layers. \n",
    "\n",
    "Let's try some of these techniques with Fashion MNIST data. Some of these concepts we have already talked about, such as different initializations and different regularizers. Namely, we will cover these concepts:\n",
    "\n",
    "- Expansion\n",
    "- Initialization\n",
    "- Parallel Pathways\n",
    "- Building Blocks from More Advanced Networks (bypass, branching, etc.)\n",
    "\n",
    "![resnet](PDF_Slides/CNN_Summary.png)\n",
    "\n",
    "After going through this tutorial, you will have an understanding of how to use some of the more advanced techniques--however we are limited here by the hardware and time in lecture. Many modern architectures can take days/weeks to train and are extremely deep (look at the 100+ layers of ResNet-112). However, some of the architectures from ~2013 and ~2014 are manageable to investigate and take only a few hours to train. We will cover a few concepts for getting models up and running, and maybe even use more some complicated architectures. Let's get started! \n",
    "\n",
    "This is also an excellent tutorial with even more information than I am showing for the fine tuning techniques:\n",
    "- https://cambridgespark.com/content/tutorials/neural-networks-tuning-techniques/index.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.9.0'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import tensorflow.keras as keras\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Reshape, Input\n",
    "from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten\n",
    "from tensorflow.keras.layers import Conv2D, MaxPooling2D\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras.layers import RandomFlip, RandomRotation\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "from tensorflow.keras.regularizers import l2\n",
    "from tensorflow.keras.layers import average \n",
    "from tensorflow.keras.models import  Model\n",
    "\n",
    "keras.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.config.list_physical_devices('GPU')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics as mt\n",
    "from matplotlib import pyplot as plt\n",
    "from skimage.io import imshow\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "labels = [\"T-shirt/top\",\"Trouser\",\"Pullover\",\"Dress\",\"Coat\",\"Sandal\",\"Shirt\",\"Sneaker\",\"Bag\",\"Ankle boot\"]\n",
    "def summarize_net(net, X_test, y_test, title_text=''):\n",
    "    plt.figure(figsize=(15,5))\n",
    "    yhat = np.argmax(net.predict(X_test), axis=1)\n",
    "    acc = mt.accuracy_score(y_test,yhat)\n",
    "    cm = mt.confusion_matrix(y_test,yhat)\n",
    "    cm = cm/np.sum(cm,axis=1)[:,np.newaxis]\n",
    "    sns.heatmap(cm, annot=True, fmt='.2f', xticklabels=labels,yticklabels=labels)\n",
    "    plt.title(title_text+'{:.4f}'.format(acc))\n",
    "    \n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's load in a more diverse, harder to classify dataset: Fashion MNIST\n",
    "https://www.kaggle.com/zalando-research/fashionmnist\n",
    "\n",
    "**Labels**\n",
    "\n",
    "Each training and test example is assigned to one of the following labels:\n",
    "\n",
    "- 0 T-shirt/top\n",
    "- 1 Trouser\n",
    "- 2 Pullover\n",
    "- 3 Dress\n",
    "- 4 Coat\n",
    "- 5 Sandal\n",
    "- 6 Shirt\n",
    "- 7 Sneaker\n",
    "- 8 Bag\n",
    "- 9 Ankle boot "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows: 60000, columns: 784\n",
      "Rows: 10000, columns: 784\n",
      "New Shape: Rows: 60000, image size: (28,28,1)\n"
     ]
    }
   ],
   "source": [
    "# more data for handwriting recognition?\n",
    "# Let's use Raschka's implementation for using the mnist dataset:\n",
    "# https://github.com/rasbt/python-machine-learning-book/blob/master/code/ch12/ch12.ipynb\n",
    "import os\n",
    "import struct\n",
    "import numpy as np\n",
    " \n",
    "def load_mnist(path, kind='fashion_train'):\n",
    "    \"\"\"Load MNIST data from `path`\"\"\"\n",
    "    labels_path = os.path.join(path, '%s-labels-idx1-ubyte' % kind)\n",
    "    images_path = os.path.join(path, '%s-images-idx3-ubyte' % kind)\n",
    "        \n",
    "    with open(labels_path, 'rb') as lbpath:\n",
    "        magic, n = struct.unpack('>II', lbpath.read(8))\n",
    "        labels = np.fromfile(lbpath, dtype=np.uint8)\n",
    "\n",
    "    with open(images_path, 'rb') as imgpath:\n",
    "        magic, num, rows, cols = struct.unpack(\">IIII\", imgpath.read(16))\n",
    "        images = np.fromfile(imgpath, dtype=np.uint8).reshape(len(labels), 784)\n",
    " \n",
    "    return images, labels\n",
    "\n",
    "X_train, y_train = load_mnist('data/', kind='fashion_train')\n",
    "print('Rows: %d, columns: %d' % (X_train.shape[0], X_train.shape[1]))\n",
    "\n",
    "X_test, y_test = load_mnist('data/', kind='fashion_t10k')\n",
    "print('Rows: %d, columns: %d' % (X_test.shape[0], X_test.shape[1]))\n",
    "\n",
    "X_train = X_train/255.0 - 0.5\n",
    "X_test = X_test/255.0 - 0.5\n",
    "\n",
    "NUM_CLASSES = 10\n",
    "img_wh = 28\n",
    "\n",
    "# Let's start by fixing the sizes, channels last\n",
    "X_train = np.expand_dims(X_train.reshape((-1,img_wh,img_wh)), axis=3)\n",
    "X_test = np.expand_dims(X_test.reshape((-1,img_wh,img_wh)), axis=3)\n",
    "# the image data has been resized to (samples,image_rows,image_cols,image_channels)\n",
    "\n",
    "# and one hot encoding the output values\n",
    "y_train_ohe = keras.utils.to_categorical(y_train, NUM_CLASSES)\n",
    "y_test_ohe = keras.utils.to_categorical(y_test, NUM_CLASSES)\n",
    "\n",
    "print('New Shape: Rows: %d, image size: (%d,%d,%d)' % (X_train.shape[0], X_train.shape[1], X_train.shape[2], X_train.shape[3] ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 28, 28, 1)\n"
     ]
    },
    {
     "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": [
    "print(X_train.shape)\n",
    "plt.subplot(1,1,1)\n",
    "plt.imshow(X_train[1].squeeze(),cmap='bone')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Starting simple\n",
    "Let's first start by using data expansion on the Fashion MNIST dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# datagen = ImageDataGenerator(\n",
    "#     featurewise_center=False,\n",
    "#     samplewise_center=False,\n",
    "#     featurewise_std_normalization=False,\n",
    "#     samplewise_std_normalization=False,\n",
    "#     zca_whitening=False,\n",
    "#     rotation_range=5, # used, Int. Degree range for random rotations.\n",
    "#     width_shift_range=0.1, # used, Float (fraction of total width). Range for random horizontal shifts.\n",
    "#     height_shift_range=0.1, # used,  Float (fraction of total height). Range for random vertical shifts.\n",
    "#     shear_range=0., # Float. Shear Intensity (Shear angle in counter-clockwise direction as radians)\n",
    "#     zoom_range=0.,\n",
    "#     channel_shift_range=0.,\n",
    "#     fill_mode='nearest',\n",
    "#     cval=0.,\n",
    "#     horizontal_flip=True,\n",
    "#     vertical_flip=False,\n",
    "#     rescale=None)\n",
    "\n",
    "# datagen.fit(X_train)\n",
    "\n",
    "# idx = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# tmps = datagen.flow(X_train, y_train_ohe, batch_size=1)\n",
    "\n",
    "# for tmp in tmps:\n",
    "#     imshow(tmp[0].squeeze(),cmap='bone')\n",
    "#     plt.title(labels[np.argmax(tmp[1])])\n",
    "#     break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 28.7 ms, sys: 5.01 ms, total: 33.7 ms\n",
      "Wall time: 31.4 ms\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-01-14 09:40:33.239301: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    }
   ],
   "source": [
    "%%time \n",
    "\n",
    "cnn = Sequential()\n",
    "\n",
    "# add in augmentations directly\n",
    "cnn.add(RandomFlip(\"horizontal\"))\n",
    "cnn.add(RandomRotation(0.2))\n",
    "\n",
    "# let's start with an AlexNet style convolutional phase\n",
    "cnn.add(Conv2D(filters=32,\n",
    "                input_shape = (img_wh,img_wh,1),\n",
    "                kernel_size=(3,3), \n",
    "                padding='same', \n",
    "                activation='relu')) # more compact syntax\n",
    "\n",
    "# no max pool before next conv layer!!\n",
    "cnn.add(Conv2D(filters=64,\n",
    "                kernel_size=(3,3), \n",
    "                padding='same', \n",
    "                activation='relu')) # more compact syntax\n",
    "cnn.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "    \n",
    "\n",
    "# add one layer on flattened output\n",
    "cnn.add(Dropout(0.25)) # add some dropout for regularization after conv layers\n",
    "cnn.add(Flatten())\n",
    "cnn.add(Dense(128, activation='relu'))\n",
    "cnn.add(Dropout(0.5)) # add some dropout for regularization, again!\n",
    "cnn.add(Dense(NUM_CLASSES, activation='softmax'))\n",
    "\n",
    "# Let's train the model \n",
    "cnn.compile(loss='categorical_crossentropy', # 'categorical_crossentropy' 'mean_squared_error'\n",
    "              optimizer='rmsprop', # 'adadelta' 'rmsprop'\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "WARNING:tensorflow:AutoGraph could not transform <function Model.make_train_function.<locals>.train_function at 0x7fcf212c6ef0> 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: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function Model.make_train_function.<locals>.train_function at 0x7fcf212c6ef0> 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: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "1770/1875 [===========================>..] - ETA: 4s - loss: 0.9334 - accuracy: 0.6732"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/td/w_hszl_j1cj49sh8g524zmbw0000gn/T/ipykernel_12594/97993711.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m cnn.fit(X_train, y_train_ohe, batch_size=32, \n\u001b[1;32m      3\u001b[0m                    \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m                    \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_test_ohe\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m                   )\n",
      "\u001b[0;32m~/opt/anaconda3/envs/mlenv2022/lib/python3.7/site-packages/keras/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 64\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     65\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=broad-except\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/mlenv2022/lib/python3.7/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1407\u001b[0m                 _r=1):\n\u001b[1;32m   1408\u001b[0m               \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1409\u001b[0;31m               \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1410\u001b[0m               \u001b[0;32mif\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_sync\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1411\u001b[0m                 \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masync_wait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/mlenv2022/lib/python3.7/site-packages/tensorflow/python/util/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    148\u001b[0m     \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    151\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/mlenv2022/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    913\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    914\u001b[0m       \u001b[0;32mwith\u001b[0m \u001b[0mOptionalXlaContext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jit_compile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 915\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    916\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    917\u001b[0m       \u001b[0mnew_tracing_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/mlenv2022/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    945\u001b[0m       \u001b[0;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    946\u001b[0m       \u001b[0;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 947\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateless_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=not-callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    948\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    949\u001b[0m       \u001b[0;31m# Release the lock early so that multiple threads can perform the call\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/mlenv2022/lib/python3.7/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2452\u001b[0m        filtered_flat_args) = self._maybe_define_function(args, kwargs)\n\u001b[1;32m   2453\u001b[0m     return graph_function._call_flat(\n\u001b[0;32m-> 2454\u001b[0;31m         filtered_flat_args, captured_inputs=graph_function.captured_inputs)  # pylint: disable=protected-access\n\u001b[0m\u001b[1;32m   2455\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2456\u001b[0m   \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/mlenv2022/lib/python3.7/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1859\u001b[0m       \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1860\u001b[0m       return self._build_call_outputs(self._inference_function.call(\n\u001b[0;32m-> 1861\u001b[0;31m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[0m\u001b[1;32m   1862\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n\u001b[1;32m   1863\u001b[0m         \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/mlenv2022/lib/python3.7/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m    500\u001b[0m               \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    501\u001b[0m               \u001b[0mattrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattrs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 502\u001b[0;31m               ctx=ctx)\n\u001b[0m\u001b[1;32m    503\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    504\u001b[0m           outputs = execute.execute_with_cancellation(\n",
      "\u001b[0;32m~/opt/anaconda3/envs/mlenv2022/lib/python3.7/site-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     53\u001b[0m     \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0;32m---> 55\u001b[0;31m                                         inputs, attrs, num_outputs)\n\u001b[0m\u001b[1;32m     56\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# the flow method yields batches of images indefinitely, with the given transformations\n",
    "cnn.fit(X_train, y_train_ohe, batch_size=32, \n",
    "                   epochs=5, verbose=1,\n",
    "                   validation_data=(X_test,y_test_ohe)\n",
    "                  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "summarize_net(cnn, X_test, y_test, title_text='Using Expansion:')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "468/468 [==============================] - 297s 635ms/step - loss: 0.8621 - accuracy: 0.7249 - val_loss: 0.5601 - val_accuracy: 0.8299\n",
      "Epoch 2/50\n",
      "468/468 [==============================] - 296s 632ms/step - loss: 0.5665 - accuracy: 0.8365 - val_loss: 0.4695 - val_accuracy: 0.8718\n",
      "Epoch 3/50\n",
      "468/468 [==============================] - 313s 668ms/step - loss: 0.5077 - accuracy: 0.8593 - val_loss: 0.4272 - val_accuracy: 0.8878\n",
      "Epoch 4/50\n",
      "468/468 [==============================] - 358s 764ms/step - loss: 0.4738 - accuracy: 0.8715 - val_loss: 0.4196 - val_accuracy: 0.8912\n",
      "Epoch 5/50\n",
      "468/468 [==============================] - 371s 793ms/step - loss: 0.4492 - accuracy: 0.8804 - val_loss: 0.4103 - val_accuracy: 0.8929\n",
      "Epoch 6/50\n",
      "468/468 [==============================] - 373s 798ms/step - loss: 0.4324 - accuracy: 0.8873 - val_loss: 0.3670 - val_accuracy: 0.9080\n",
      "Epoch 7/50\n",
      "468/468 [==============================] - 372s 794ms/step - loss: 0.4189 - accuracy: 0.8922 - val_loss: 0.3889 - val_accuracy: 0.8975\n",
      "Epoch 8/50\n",
      "468/468 [==============================] - 376s 804ms/step - loss: 0.4067 - accuracy: 0.8954 - val_loss: 0.4351 - val_accuracy: 0.8903\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7fcc48f58e10>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# what if we just want to use the validation data??\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "from tensorflow.keras.regularizers import l2 \n",
    "l2_lambda = 0.0001\n",
    "\n",
    "# Use Kaiming He to regularize ReLU layers: https://arxiv.org/pdf/1502.01852.pdf\n",
    "# Use Glorot/Bengio for linear/sigmoid/softmax: http://proceedings.mlr.press/v9/glorot10a/glorot10a.pdf \n",
    "cnn = Sequential()\n",
    "\n",
    "cnn.add(Conv2D(filters=32,\n",
    "               input_shape = (img_wh,img_wh,1),\n",
    "               kernel_size=(3,3),\n",
    "               kernel_initializer='he_uniform', \n",
    "               kernel_regularizer=l2(l2_lambda),\n",
    "               padding='same', \n",
    "               activation='relu',\n",
    "               data_format=\"channels_last\")) # more compact syntax\n",
    "\n",
    "cnn.add(Conv2D(filters=32,\n",
    "               kernel_size=(3,3),\n",
    "               kernel_initializer='he_uniform', \n",
    "               kernel_regularizer=l2(l2_lambda),\n",
    "               padding='same', \n",
    "               activation='relu',data_format=\"channels_last\"))\n",
    "cnn.add(MaxPooling2D(pool_size=(2, 2), data_format=\"channels_last\"))\n",
    "\n",
    "cnn.add(Conv2D(filters=64,\n",
    "               input_shape = (img_wh,img_wh,1),\n",
    "               kernel_size=(3,3),\n",
    "               kernel_initializer='he_uniform', \n",
    "               kernel_regularizer=l2(l2_lambda),\n",
    "               padding='same', \n",
    "               activation='relu',data_format=\"channels_last\")) # more compact syntax\n",
    "\n",
    "cnn.add(Conv2D(filters=64,\n",
    "               kernel_size=(3,3),\n",
    "               kernel_initializer='he_uniform', \n",
    "               kernel_regularizer=l2(l2_lambda),\n",
    "               padding='same', \n",
    "               activation='relu'))\n",
    "cnn.add(MaxPooling2D(pool_size=(2, 2), data_format=\"channels_last\"))\n",
    "\n",
    "cnn.add(Conv2D(filters=128,\n",
    "               input_shape = (img_wh,img_wh,1),\n",
    "               kernel_size=(3,3),\n",
    "               kernel_initializer='he_uniform', \n",
    "               kernel_regularizer=l2(l2_lambda),\n",
    "               padding='same', \n",
    "               activation='relu',data_format=\"channels_last\")) # more compact syntax\n",
    "\n",
    "cnn.add(Conv2D(filters=128,\n",
    "               kernel_size=(3,3),\n",
    "               kernel_initializer='he_uniform', \n",
    "               kernel_regularizer=l2(l2_lambda),\n",
    "               padding='same', \n",
    "               activation='relu',data_format=\"channels_last\"))\n",
    "    \n",
    "\n",
    "# add one layer on flattened output\n",
    "\n",
    "cnn.add(Flatten())\n",
    "cnn.add(Dropout(0.25)) # add some dropout for regularization after conv layers\n",
    "cnn.add(Dense(128, \n",
    "              activation='relu',\n",
    "              kernel_initializer='he_uniform',\n",
    "              kernel_regularizer=l2(l2_lambda)\n",
    "       ))\n",
    "cnn.add(Dropout(0.5)) # add some dropout for regularization, again!\n",
    "cnn.add(Dense(NUM_CLASSES, \n",
    "              activation='softmax', \n",
    "              kernel_initializer='glorot_uniform',\n",
    "              kernel_regularizer=l2(l2_lambda)\n",
    "             ))\n",
    "\n",
    "# Let's train the model \n",
    "cnn.compile(loss='categorical_crossentropy', # 'categorical_crossentropy' 'mean_squared_error'\n",
    "              optimizer='rmsprop', # 'adadelta' 'rmsprop'\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "# the flow method yields batches of images indefinitely, with the given transofmrations\n",
    "cnn.fit_generator(datagen.flow(X_train, y_train_ohe, batch_size=128), \n",
    "                  steps_per_epoch=int(len(X_train)/128), # how many generators to go through per epoch\n",
    "                  epochs=50, verbose=1,\n",
    "                  validation_data=(X_test,y_test_ohe),\n",
    "                  callbacks=[EarlyStopping(monitor='val_loss', patience=2)]\n",
    "                 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "summarize_net(cnn, X_test, y_test, title_text='Using Exp.+Reg.+Init.:')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ensemble Nets\n",
    "Now lets do the exact same thing, but with an average of three separate networks. \n",
    "\n",
    "To get this running, we are going to use the keras `Model API` which is different than the `Sequential API`. It requires that we explicitly define the inputs and outputs, and gives us a bit more control over the network topology.\n",
    "\n",
    "- https://keras.io/models/model/\n",
    "\n",
    "Reminder of Differences from Sequential and Functional APIs:\n",
    "- You will need to specify the input and output nodes explicitly.\n",
    " - `input_layer = Input(shape=(**insert shape here**))`\n",
    "- You no longer `add` to a `Sequential`, you feed layer by layer:\n",
    " - `model.add(Dense())` becomes `layer2 = Dense()(input_layer)`\n",
    " - subsequent layers use temporary variables like `layer1` and `input_layer` above\n",
    "- Tell the Model what the input and output temporary variables are:\n",
    " - `model = Model(inputs=input_layer,outputs=layer1)`\n",
    " - then you can call `model.compile()`\n",
    "- A great example that uses the Model API with MNIST:\n",
    " - https://cambridgespark.com/content/tutorials/neural-networks-tuning-techniques/index.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            [(None, 28, 28, 1)]  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_8 (Conv2D)               (None, 28, 28, 32)   320         input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_9 (Conv2D)               (None, 28, 28, 32)   9248        conv2d_8[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2D)  (None, 14, 14, 32)   0           conv2d_9[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_10 (Conv2D)              (None, 14, 14, 32)   9248        max_pooling2d_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_14 (Conv2D)              (None, 14, 14, 32)   9248        max_pooling2d_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_18 (Conv2D)              (None, 14, 14, 32)   9248        max_pooling2d_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_11 (Conv2D)              (None, 14, 14, 32)   1056        conv2d_10[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_15 (Conv2D)              (None, 14, 14, 32)   1056        conv2d_14[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_19 (Conv2D)              (None, 14, 14, 32)   1056        conv2d_18[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2D)  (None, 7, 7, 32)     0           conv2d_11[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_6 (MaxPooling2D)  (None, 7, 7, 32)     0           conv2d_15[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_8 (MaxPooling2D)  (None, 7, 7, 32)     0           conv2d_19[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_12 (Conv2D)              (None, 7, 7, 64)     18496       max_pooling2d_4[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_16 (Conv2D)              (None, 7, 7, 64)     18496       max_pooling2d_6[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_20 (Conv2D)              (None, 7, 7, 64)     18496       max_pooling2d_8[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_13 (Conv2D)              (None, 7, 7, 64)     4160        conv2d_12[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_17 (Conv2D)              (None, 7, 7, 64)     4160        conv2d_16[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_21 (Conv2D)              (None, 7, 7, 64)     4160        conv2d_20[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_5 (MaxPooling2D)  (None, 3, 3, 64)     0           conv2d_13[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_7 (MaxPooling2D)  (None, 3, 3, 64)     0           conv2d_17[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_9 (MaxPooling2D)  (None, 3, 3, 64)     0           conv2d_21[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "flatten_2 (Flatten)             (None, 576)          0           max_pooling2d_5[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "flatten_3 (Flatten)             (None, 576)          0           max_pooling2d_7[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "flatten_4 (Flatten)             (None, 576)          0           max_pooling2d_9[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "dropout_4 (Dropout)             (None, 576)          0           flatten_2[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dropout_5 (Dropout)             (None, 576)          0           flatten_3[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dropout_6 (Dropout)             (None, 576)          0           flatten_4[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dense_4 (Dense)                 (None, 64)           36928       dropout_4[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dense_6 (Dense)                 (None, 64)           36928       dropout_5[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dense_8 (Dense)                 (None, 64)           36928       dropout_6[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dense_5 (Dense)                 (None, 10)           650         dense_4[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_7 (Dense)                 (None, 10)           650         dense_6[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_9 (Dense)                 (None, 10)           650         dense_8[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "concatenate (Concatenate)       (None, 30)           0           dense_5[0][0]                    \n",
      "                                                                 dense_7[0][0]                    \n",
      "                                                                 dense_9[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_10 (Dense)                (None, 10)           310         concatenate[0][0]                \n",
      "==================================================================================================\n",
      "Total params: 221,492\n",
      "Trainable params: 221,492\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n",
      "CPU times: user 339 ms, sys: 5.59 ms, total: 344 ms\n",
      "Wall time: 335 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "from tensorflow.keras.layers import Input, average, concatenate\n",
    "from tensorflow.keras.models import Model\n",
    "\n",
    "num_ensembles = 3\n",
    "l2_lambda = 0.000001\n",
    "\n",
    "input_holder = Input(shape=(img_wh, img_wh, 1))\n",
    "\n",
    "# start with a conv layer\n",
    "x = Conv2D(filters=32,\n",
    "               input_shape = (img_wh,img_wh,1),\n",
    "               kernel_size=(3,3),\n",
    "               kernel_initializer='he_uniform', \n",
    "               kernel_regularizer=l2(l2_lambda),\n",
    "               padding='same', \n",
    "               activation='relu', data_format=\"channels_last\")(input_holder)\n",
    "\n",
    "x = Conv2D(filters=32,\n",
    "               kernel_size=(3,3),\n",
    "               kernel_initializer='he_uniform', \n",
    "               kernel_regularizer=l2(l2_lambda),\n",
    "               padding='same', \n",
    "               activation='relu')(x)\n",
    "input_conv = MaxPooling2D(pool_size=(2, 2), data_format=\"channels_last\")(x)\n",
    "\n",
    "branches = []\n",
    "for _ in range(num_ensembles):\n",
    "    \n",
    "    # start using NiN (MLPConv)\n",
    "    x = Conv2D(filters=32,\n",
    "                   input_shape = (img_wh,img_wh,1),\n",
    "                   kernel_size=(3,3),\n",
    "                   kernel_initializer='he_uniform', \n",
    "                   kernel_regularizer=l2(l2_lambda),\n",
    "                   padding='same', \n",
    "                   activation='linear', data_format=\"channels_last\")(input_conv)\n",
    "\n",
    "    x = Conv2D(filters=32,\n",
    "                   kernel_size=(1,1),\n",
    "                   kernel_initializer='he_uniform', \n",
    "                   kernel_regularizer=l2(l2_lambda),\n",
    "                   padding='same', \n",
    "                   activation='relu', data_format=\"channels_last\")(x)\n",
    "    \n",
    "    x = MaxPooling2D(pool_size=(2, 2), data_format=\"channels_last\")(x)\n",
    "    \n",
    "    x = Conv2D(filters=64,\n",
    "                   input_shape = (img_wh,img_wh,1),\n",
    "                   kernel_size=(3,3),\n",
    "                   kernel_initializer='he_uniform', \n",
    "                   kernel_regularizer=l2(l2_lambda),\n",
    "                   padding='same', \n",
    "                   activation='linear', data_format=\"channels_last\")(x)\n",
    "\n",
    "    x = Conv2D(filters=64,\n",
    "                   kernel_size=(1,1),\n",
    "                   kernel_initializer='he_uniform', \n",
    "                   kernel_regularizer=l2(l2_lambda),\n",
    "                   padding='same', \n",
    "                   activation='linear', data_format=\"channels_last\")(x)\n",
    "    \n",
    "    x = MaxPooling2D(pool_size=(2, 2), data_format=\"channels_last\")(x)\n",
    "\n",
    "    # add one layer on flattened output\n",
    "    x = Flatten()(x)\n",
    "    x = Dropout(0.50)(x) # add some dropout for regularization after conv layers\n",
    "    x = Dense(64, \n",
    "              activation='relu',\n",
    "              kernel_initializer='he_uniform',\n",
    "              kernel_regularizer=l2(l2_lambda)\n",
    "            )(x)\n",
    "    \n",
    "    x = Dense(NUM_CLASSES, \n",
    "              activation='relu',\n",
    "              kernel_initializer='he_uniform',\n",
    "              kernel_regularizer=l2(l2_lambda)\n",
    "             )(x)\n",
    "    \n",
    "    # now add this branch onto the master list\n",
    "    branches.append(x)\n",
    "\n",
    "# that's it, we just need to average the results\n",
    "x = concatenate(branches)\n",
    "\n",
    "x = Dense(NUM_CLASSES, \n",
    "          activation='softmax', \n",
    "          kernel_initializer='glorot_uniform',\n",
    "          kernel_regularizer=l2(l2_lambda)\n",
    "         )(x)\n",
    "\n",
    "# here is the secret sauce for setting the network using the \n",
    "#   Functional API:\n",
    "cnn_ens = Model(inputs=input_holder,outputs=x)\n",
    "\n",
    "cnn_ens.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "# Let's train the model \n",
    "cnn_ens.compile(loss='categorical_crossentropy', # 'categorical_crossentropy' 'mean_squared_error'\n",
    "                optimizer='adam', # 'adadelta' 'rmsprop'\n",
    "                metrics=['accuracy'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "468/468 [==============================] - 342s 730ms/step - loss: 0.9119 - accuracy: 0.6610 - val_loss: 0.5361 - val_accuracy: 0.7870\n",
      "Epoch 2/50\n",
      "468/468 [==============================] - 342s 731ms/step - loss: 0.5525 - accuracy: 0.7896 - val_loss: 0.4381 - val_accuracy: 0.8308\n",
      "Epoch 3/50\n",
      "468/468 [==============================] - 380s 812ms/step - loss: 0.4759 - accuracy: 0.8223 - val_loss: 0.3652 - val_accuracy: 0.8619\n",
      "Epoch 4/50\n",
      "468/468 [==============================] - 375s 802ms/step - loss: 0.4217 - accuracy: 0.8422 - val_loss: 0.3563 - val_accuracy: 0.8699\n",
      "Epoch 5/50\n",
      "468/468 [==============================] - 381s 814ms/step - loss: 0.3861 - accuracy: 0.8572 - val_loss: 0.3231 - val_accuracy: 0.8843\n",
      "Epoch 6/50\n",
      "468/468 [==============================] - 370s 790ms/step - loss: 0.3543 - accuracy: 0.8694 - val_loss: 0.2994 - val_accuracy: 0.8896\n",
      "Epoch 7/50\n",
      "468/468 [==============================] - 357s 764ms/step - loss: 0.3363 - accuracy: 0.8773 - val_loss: 0.2964 - val_accuracy: 0.8939\n",
      "Epoch 8/50\n",
      "468/468 [==============================] - 358s 764ms/step - loss: 0.3217 - accuracy: 0.8813 - val_loss: 0.2840 - val_accuracy: 0.8957\n",
      "Epoch 9/50\n",
      "468/468 [==============================] - 327s 699ms/step - loss: 0.3163 - accuracy: 0.8835 - val_loss: 0.2773 - val_accuracy: 0.8990\n",
      "Epoch 10/50\n",
      "468/468 [==============================] - 309s 660ms/step - loss: 0.3037 - accuracy: 0.8895 - val_loss: 0.2661 - val_accuracy: 0.9044\n",
      "Epoch 11/50\n",
      "468/468 [==============================] - 306s 653ms/step - loss: 0.2940 - accuracy: 0.8921 - val_loss: 0.2713 - val_accuracy: 0.9015\n",
      "Epoch 12/50\n",
      "468/468 [==============================] - 298s 636ms/step - loss: 0.2878 - accuracy: 0.8950 - val_loss: 0.2670 - val_accuracy: 0.9046\n",
      "Epoch 13/50\n",
      "468/468 [==============================] - 303s 647ms/step - loss: 0.2857 - accuracy: 0.8971 - val_loss: 0.2602 - val_accuracy: 0.9059\n",
      "Epoch 14/50\n",
      "468/468 [==============================] - 306s 654ms/step - loss: 0.2835 - accuracy: 0.8967 - val_loss: 0.2549 - val_accuracy: 0.9097\n",
      "Epoch 15/50\n",
      "468/468 [==============================] - 304s 650ms/step - loss: 0.2769 - accuracy: 0.9000 - val_loss: 0.2847 - val_accuracy: 0.8985\n",
      "Epoch 16/50\n",
      "468/468 [==============================] - 306s 655ms/step - loss: 0.2725 - accuracy: 0.9013 - val_loss: 0.2484 - val_accuracy: 0.9095\n",
      "Epoch 17/50\n",
      "468/468 [==============================] - 309s 661ms/step - loss: 0.2661 - accuracy: 0.9034 - val_loss: 0.2759 - val_accuracy: 0.8984\n",
      "Epoch 18/50\n",
      "468/468 [==============================] - 290s 621ms/step - loss: 0.2641 - accuracy: 0.9041 - val_loss: 0.2481 - val_accuracy: 0.9082\n",
      "Epoch 19/50\n",
      "468/468 [==============================] - 301s 644ms/step - loss: 0.2600 - accuracy: 0.9050 - val_loss: 0.2399 - val_accuracy: 0.9155\n",
      "Epoch 20/50\n",
      "468/468 [==============================] - 309s 661ms/step - loss: 0.2596 - accuracy: 0.9055 - val_loss: 0.2474 - val_accuracy: 0.9089\n",
      "Epoch 21/50\n",
      "468/468 [==============================] - 309s 660ms/step - loss: 0.2517 - accuracy: 0.9084 - val_loss: 0.2535 - val_accuracy: 0.9063\n",
      "Epoch 22/50\n",
      "468/468 [==============================] - 294s 628ms/step - loss: 0.2536 - accuracy: 0.9089 - val_loss: 0.2614 - val_accuracy: 0.9040\n",
      "Epoch 23/50\n",
      "468/468 [==============================] - 294s 628ms/step - loss: 0.2497 - accuracy: 0.9092 - val_loss: 0.2597 - val_accuracy: 0.9042\n",
      "CPU times: user 6h 58min 48s, sys: 24min 10s, total: 7h 22min 58s\n",
      "Wall time: 2h 4min 31s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7fcc2e48f650>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "# the flow method yields batches of images indefinitely, with the given transofmrations\n",
    "cnn_ens.fit_generator(datagen.flow(X_train, y_train_ohe, batch_size=128), \n",
    "                      steps_per_epoch=int(len(X_train)/128), # how many generators to go through per epoch\n",
    "                      epochs=50, verbose=1,\n",
    "                      validation_data=(X_test,y_test_ohe),\n",
    "                      callbacks=[EarlyStopping(monitor='val_loss', patience=4)]\n",
    "                     )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "summarize_net(cnn_ens, X_test, y_test, title_text='CNN Ensemble:')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "# Adding ResNet Style Blocks\n",
    "Okay, so now lets try a trick from 2016---ancient in terms of deep learning years (that's a joke 🤷, ¯\\\\_(ツ)_/¯). \n",
    "\n",
    "The ResNet-Style Bypass is described in the following:\n",
    "![resnet](https://cdn-images-1.medium.com/max/1600/0*0r0vS8myiqyOb79L.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_2 (InputLayer)            [(None, 28, 28, 1)]  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_22 (Conv2D)              (None, 28, 28, 32)   320         input_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_10 (MaxPooling2D) (None, 14, 14, 32)   0           conv2d_22[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_23 (Conv2D)              (None, 14, 14, 32)   9248        max_pooling2d_10[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_11 (MaxPooling2D) (None, 7, 7, 32)     0           conv2d_23[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_24 (Conv2D)              (None, 7, 7, 64)     2112        max_pooling2d_11[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_25 (Conv2D)              (None, 7, 7, 64)     36928       conv2d_24[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_26 (Conv2D)              (None, 7, 7, 32)     2080        conv2d_25[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add (Add)                       (None, 7, 7, 32)     0           conv2d_26[0][0]                  \n",
      "                                                                 max_pooling2d_11[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "activation (Activation)         (None, 7, 7, 32)     0           add[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_12 (MaxPooling2D) (None, 3, 3, 32)     0           activation[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "flatten_5 (Flatten)             (None, 288)          0           max_pooling2d_12[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "dropout_7 (Dropout)             (None, 288)          0           flatten_5[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dense_11 (Dense)                (None, 256)          73984       dropout_7[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_1 (Activation)       (None, 256)          0           dense_11[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dropout_8 (Dropout)             (None, 256)          0           activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "dense_12 (Dense)                (None, 10)           2570        dropout_8[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_2 (Activation)       (None, 10)           0           dense_12[0][0]                   \n",
      "==================================================================================================\n",
      "Total params: 127,242\n",
      "Trainable params: 127,242\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n",
      "CPU times: user 152 ms, sys: 3.94 ms, total: 156 ms\n",
      "Wall time: 147 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# now lets use the LeNet architecture with batch norm\n",
    "# We will also use ReLU where approriate and drop out \n",
    "from tensorflow.keras.layers import Add, Input\n",
    "from tensorflow.keras.layers import average, concatenate\n",
    "from tensorflow.keras.models import Model\n",
    "\n",
    "input_holder = Input(shape=(img_wh, img_wh, 1))\n",
    "\n",
    "# start with a conv layer\n",
    "x = Conv2D(filters=32,\n",
    "               input_shape = (img_wh,img_wh,1),\n",
    "               kernel_size=(3,3),\n",
    "               kernel_initializer='he_uniform', \n",
    "               kernel_regularizer=l2(l2_lambda),\n",
    "               padding='same', \n",
    "               activation='relu', \n",
    "               data_format=\"channels_last\")(input_holder)\n",
    "\n",
    "x = MaxPooling2D(pool_size=(2, 2), data_format=\"channels_last\")(x)\n",
    "\n",
    "x = Conv2D(filters=32,\n",
    "               kernel_size=(3,3),\n",
    "               kernel_initializer='he_uniform', \n",
    "               kernel_regularizer=l2(l2_lambda),\n",
    "               padding='same', \n",
    "               activation='relu', \n",
    "               data_format=\"channels_last\")(x)\n",
    "\n",
    "x_split = MaxPooling2D(pool_size=(2, 2), data_format=\"channels_last\")(x)\n",
    "\n",
    "x = Conv2D(filters=64,\n",
    "               kernel_size=(1,1),\n",
    "               kernel_initializer='he_uniform', \n",
    "               kernel_regularizer=l2(l2_lambda),\n",
    "               padding='same', \n",
    "               activation='relu', \n",
    "               data_format=\"channels_last\")(x_split)\n",
    "\n",
    "x = Conv2D(filters=64,\n",
    "               kernel_size=(3,3),\n",
    "               kernel_initializer='he_uniform', \n",
    "               kernel_regularizer=l2(l2_lambda),\n",
    "               padding='same', \n",
    "               activation='relu', \n",
    "               data_format=\"channels_last\")(x)\n",
    "\n",
    "x = Conv2D(filters=32,\n",
    "               kernel_size=(1,1),\n",
    "               kernel_initializer='he_uniform', \n",
    "               kernel_regularizer=l2(l2_lambda),\n",
    "               padding='same', \n",
    "               activation='relu', \n",
    "               data_format=\"channels_last\")(x)\n",
    "\n",
    "# now add back in the split layer, x_split (residual added in)\n",
    "x = Add()([x, x_split])\n",
    "x = Activation(\"relu\")(x)\n",
    "\n",
    "x = MaxPooling2D(pool_size=(2, 2), data_format=\"channels_last\")(x)\n",
    "\n",
    "x = Flatten()(x)\n",
    "x = Dropout(0.25)(x)\n",
    "x = Dense(256)(x)\n",
    "x = Activation(\"relu\")(x)\n",
    "x = Dropout(0.5)(x)\n",
    "x = Dense(NUM_CLASSES)(x)\n",
    "x = Activation('softmax')(x)\n",
    "\n",
    "resnet = Model(inputs=input_holder,outputs=x)\n",
    "\n",
    "resnet.summary()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/50\n",
      "60000/60000 [==============================] - 20s 341us/sample - loss: 0.7022 - accuracy: 0.7458 - val_loss: 0.3966 - val_accuracy: 0.8520\n",
      "Epoch 2/50\n",
      "60000/60000 [==============================] - 20s 338us/sample - loss: 0.4224 - accuracy: 0.8456 - val_loss: 0.3564 - val_accuracy: 0.8677\n",
      "Epoch 3/50\n",
      "60000/60000 [==============================] - 21s 350us/sample - loss: 0.3619 - accuracy: 0.8674 - val_loss: 0.3062 - val_accuracy: 0.8858\n",
      "Epoch 4/50\n",
      "60000/60000 [==============================] - 21s 345us/sample - loss: 0.3277 - accuracy: 0.8809 - val_loss: 0.2875 - val_accuracy: 0.8940\n",
      "Epoch 5/50\n",
      "60000/60000 [==============================] - 20s 335us/sample - loss: 0.3057 - accuracy: 0.8898 - val_loss: 0.2741 - val_accuracy: 0.9004\n",
      "Epoch 6/50\n",
      "60000/60000 [==============================] - 20s 327us/sample - loss: 0.2863 - accuracy: 0.8960 - val_loss: 0.2691 - val_accuracy: 0.9010\n",
      "Epoch 7/50\n",
      "60000/60000 [==============================] - 20s 332us/sample - loss: 0.2737 - accuracy: 0.9012 - val_loss: 0.2631 - val_accuracy: 0.9036\n",
      "Epoch 8/50\n",
      "60000/60000 [==============================] - 20s 328us/sample - loss: 0.2642 - accuracy: 0.9024 - val_loss: 0.2613 - val_accuracy: 0.9027\n",
      "Epoch 9/50\n",
      "60000/60000 [==============================] - 21s 344us/sample - loss: 0.2500 - accuracy: 0.9084 - val_loss: 0.2508 - val_accuracy: 0.9087\n",
      "Epoch 10/50\n",
      "60000/60000 [==============================] - 20s 331us/sample - loss: 0.2433 - accuracy: 0.9102 - val_loss: 0.2451 - val_accuracy: 0.9087\n",
      "Epoch 11/50\n",
      "60000/60000 [==============================] - 21s 349us/sample - loss: 0.2370 - accuracy: 0.9124 - val_loss: 0.2431 - val_accuracy: 0.9099\n",
      "Epoch 12/50\n",
      "60000/60000 [==============================] - 20s 338us/sample - loss: 0.2306 - accuracy: 0.9144 - val_loss: 0.2384 - val_accuracy: 0.9132\n",
      "Epoch 13/50\n",
      "60000/60000 [==============================] - 21s 354us/sample - loss: 0.2242 - accuracy: 0.9168 - val_loss: 0.2381 - val_accuracy: 0.9142\n",
      "Epoch 14/50\n",
      "60000/60000 [==============================] - 20s 341us/sample - loss: 0.2180 - accuracy: 0.9186 - val_loss: 0.2430 - val_accuracy: 0.9124\n",
      "Epoch 15/50\n",
      "60000/60000 [==============================] - 20s 335us/sample - loss: 0.2124 - accuracy: 0.9214 - val_loss: 0.2465 - val_accuracy: 0.9096\n",
      "Epoch 16/50\n",
      "60000/60000 [==============================] - 20s 336us/sample - loss: 0.2100 - accuracy: 0.9228 - val_loss: 0.2381 - val_accuracy: 0.9150\n",
      "Epoch 17/50\n",
      "60000/60000 [==============================] - 20s 340us/sample - loss: 0.2029 - accuracy: 0.9253 - val_loss: 0.2401 - val_accuracy: 0.9126\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7fcc18820810>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resnet.compile(loss='categorical_crossentropy', # 'categorical_crossentropy' 'mean_squared_error'\n",
    "                optimizer='adam', # 'adadelta' 'rmsprop'\n",
    "                metrics=['accuracy'])\n",
    "\n",
    "# the flow method yields batches of images indefinitely, with the given transofmrations\n",
    "# resnet.fit_generator(datagen.flow(X_train, y_train_ohe, batch_size=128), \n",
    "#                       steps_per_epoch=int(len(X_train)/128), # how many generators to go through per epoch\n",
    "#                       epochs=50, verbose=1,\n",
    "#                       validation_data=(X_test,y_test_ohe),\n",
    "#                       callbacks=[EarlyStopping(monitor='val_loss', patience=4)]\n",
    "#                      )\n",
    "\n",
    "resnet.fit(X_train, y_train_ohe, batch_size=128, \n",
    "                      epochs=50, verbose=1,\n",
    "                      validation_data=(X_test,y_test_ohe),\n",
    "                      callbacks=[EarlyStopping(monitor='val_loss', patience=4)]\n",
    "                     )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "summarize_net(resnet, X_test, y_test, title_text='ResNet on Fashion MNIST:')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_2\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_3 (InputLayer)            [(None, 28, 28, 1)]  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_27 (Conv2D)              (None, 28, 28, 32)   320         input_3[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_13 (MaxPooling2D) (None, 14, 14, 32)   0           conv2d_27[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_28 (Conv2D)              (None, 14, 14, 32)   9248        max_pooling2d_13[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_14 (MaxPooling2D) (None, 7, 7, 32)     0           conv2d_28[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "separable_conv2d (SeparableConv (None, 7, 7, 32)     1344        max_pooling2d_14[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "add_1 (Add)                     (None, 7, 7, 32)     0           separable_conv2d[0][0]           \n",
      "                                                                 max_pooling2d_14[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "separable_conv2d_1 (SeparableCo (None, 7, 7, 32)     1344        add_1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "add_2 (Add)                     (None, 7, 7, 32)     0           separable_conv2d_1[0][0]         \n",
      "                                                                 add_1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_3 (Activation)       (None, 7, 7, 32)     0           add_2[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_15 (MaxPooling2D) (None, 3, 3, 32)     0           activation_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "flatten_6 (Flatten)             (None, 288)          0           max_pooling2d_15[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "dropout_9 (Dropout)             (None, 288)          0           flatten_6[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dense_13 (Dense)                (None, 256)          73984       dropout_9[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dropout_10 (Dropout)            (None, 256)          0           dense_13[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dense_14 (Dense)                (None, 10)           2570        dropout_10[0][0]                 \n",
      "==================================================================================================\n",
      "Total params: 88,810\n",
      "Trainable params: 88,810\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Xception style architecture\n",
    "from tensorflow.keras.layers import SeparableConv2D\n",
    "from tensorflow.keras.layers import BatchNormalization\n",
    "from tensorflow.keras.layers import Add, Input\n",
    "from tensorflow.keras.layers import average, concatenate\n",
    "from tensorflow.keras.models import Model\n",
    "\n",
    "l2_lambda = 0.000001\n",
    "\n",
    "\n",
    "\n",
    "input_holder = Input(shape=(img_wh, img_wh, 1))\n",
    "\n",
    "# start with a conv layer\n",
    "x = Conv2D(filters=32,\n",
    "               input_shape = (img_wh,img_wh,1),\n",
    "               kernel_size=(3,3),\n",
    "               kernel_initializer='he_uniform', \n",
    "               kernel_regularizer=l2(l2_lambda),\n",
    "               padding='same', \n",
    "               activation='relu', \n",
    "               data_format=\"channels_last\")(input_holder)\n",
    "\n",
    "x = MaxPooling2D(pool_size=(2, 2), data_format=\"channels_last\")(x)\n",
    "\n",
    "x = Conv2D(filters=32,\n",
    "               kernel_size=(3,3),\n",
    "               kernel_initializer='he_uniform', \n",
    "               kernel_regularizer=l2(l2_lambda),\n",
    "               padding='same', \n",
    "               activation='relu', \n",
    "               data_format=\"channels_last\")(x)\n",
    "\n",
    "\n",
    "x_split = MaxPooling2D(pool_size=(2, 2), data_format=\"channels_last\")(x)\n",
    "\n",
    "x = SeparableConv2D(filters=32,\n",
    "               input_shape = (img_wh,img_wh,1),\n",
    "               kernel_size=(3,3),\n",
    "               kernel_initializer='he_uniform', \n",
    "               kernel_regularizer=l2(l2_lambda),\n",
    "               padding='same', \n",
    "               activation='relu', \n",
    "               depth_multiplier = 1, # controls output channels\n",
    "               data_format=\"channels_last\")(x_split)\n",
    "\n",
    "\n",
    "x_split = Add()([x, x_split])\n",
    "\n",
    "x = SeparableConv2D(filters=32,\n",
    "               input_shape = (img_wh,img_wh,1),\n",
    "               kernel_size=(3,3),\n",
    "               kernel_initializer='he_uniform', \n",
    "               kernel_regularizer=l2(l2_lambda),\n",
    "               padding='same', \n",
    "               activation='relu', \n",
    "               depth_multiplier = 1, # controls output channels\n",
    "               data_format=\"channels_last\")(x_split)\n",
    "\n",
    "\n",
    "x_split = Add()([x, x_split])\n",
    "\n",
    "\n",
    "x = Activation(\"relu\")(x_split)\n",
    "\n",
    "x = MaxPooling2D(pool_size=(2, 2), data_format=\"channels_last\")(x)\n",
    "\n",
    "x = Flatten()(x)\n",
    "x = Dropout(0.25)(x)\n",
    "x = Dense(256, activation=\"relu\")(x)\n",
    "x = Dropout(0.5)(x)\n",
    "x = Dense(NUM_CLASSES,activation=\"softmax\")(x)\n",
    "\n",
    "xception = Model(inputs=input_holder,outputs=x)\n",
    "\n",
    "xception.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/50\n",
      "60000/60000 [==============================] - 24s 402us/sample - loss: 0.6121 - accuracy: 0.7750 - val_loss: 0.3892 - val_accuracy: 0.8573\n",
      "Epoch 2/50\n",
      "60000/60000 [==============================] - 24s 407us/sample - loss: 0.4081 - accuracy: 0.8497 - val_loss: 0.3341 - val_accuracy: 0.8746\n",
      "Epoch 3/50\n",
      "60000/60000 [==============================] - 25s 417us/sample - loss: 0.3598 - accuracy: 0.8676 - val_loss: 0.3188 - val_accuracy: 0.8817\n",
      "Epoch 4/50\n",
      "60000/60000 [==============================] - 25s 410us/sample - loss: 0.3302 - accuracy: 0.8788 - val_loss: 0.2903 - val_accuracy: 0.8908\n",
      "Epoch 5/50\n",
      "60000/60000 [==============================] - 24s 405us/sample - loss: 0.3101 - accuracy: 0.8874 - val_loss: 0.2878 - val_accuracy: 0.8950\n",
      "Epoch 6/50\n",
      "60000/60000 [==============================] - 25s 412us/sample - loss: 0.2982 - accuracy: 0.8908 - val_loss: 0.2702 - val_accuracy: 0.8992\n",
      "Epoch 7/50\n",
      "60000/60000 [==============================] - 24s 406us/sample - loss: 0.2844 - accuracy: 0.8960 - val_loss: 0.2773 - val_accuracy: 0.8952\n",
      "Epoch 8/50\n",
      "60000/60000 [==============================] - 24s 405us/sample - loss: 0.2769 - accuracy: 0.8979 - val_loss: 0.2696 - val_accuracy: 0.9016\n",
      "Epoch 9/50\n",
      "60000/60000 [==============================] - 25s 410us/sample - loss: 0.2742 - accuracy: 0.8993 - val_loss: 0.2623 - val_accuracy: 0.9026\n",
      "Epoch 10/50\n",
      "60000/60000 [==============================] - 26s 430us/sample - loss: 0.2638 - accuracy: 0.9031 - val_loss: 0.2539 - val_accuracy: 0.9076\n",
      "Epoch 11/50\n",
      "60000/60000 [==============================] - 25s 410us/sample - loss: 0.2567 - accuracy: 0.9062 - val_loss: 0.2570 - val_accuracy: 0.9013\n",
      "Epoch 12/50\n",
      "60000/60000 [==============================] - 24s 403us/sample - loss: 0.2550 - accuracy: 0.9049 - val_loss: 0.2596 - val_accuracy: 0.9033\n",
      "Epoch 13/50\n",
      "60000/60000 [==============================] - 24s 407us/sample - loss: 0.2477 - accuracy: 0.9092 - val_loss: 0.2595 - val_accuracy: 0.9082\n",
      "Epoch 14/50\n",
      "60000/60000 [==============================] - 25s 412us/sample - loss: 0.2427 - accuracy: 0.9095 - val_loss: 0.2465 - val_accuracy: 0.9116\n",
      "Epoch 15/50\n",
      "60000/60000 [==============================] - 25s 415us/sample - loss: 0.2381 - accuracy: 0.9118 - val_loss: 0.2464 - val_accuracy: 0.9090\n",
      "Epoch 16/50\n",
      "60000/60000 [==============================] - 25s 412us/sample - loss: 0.2377 - accuracy: 0.9122 - val_loss: 0.2436 - val_accuracy: 0.9103\n",
      "Epoch 17/50\n",
      "60000/60000 [==============================] - 25s 417us/sample - loss: 0.2336 - accuracy: 0.9134 - val_loss: 0.2449 - val_accuracy: 0.9122\n",
      "Epoch 18/50\n",
      "60000/60000 [==============================] - 25s 411us/sample - loss: 0.2336 - accuracy: 0.9139 - val_loss: 0.2425 - val_accuracy: 0.9130\n",
      "Epoch 19/50\n",
      "60000/60000 [==============================] - 24s 407us/sample - loss: 0.2293 - accuracy: 0.9156 - val_loss: 0.2448 - val_accuracy: 0.9130\n",
      "Epoch 20/50\n",
      "60000/60000 [==============================] - 25s 415us/sample - loss: 0.2254 - accuracy: 0.9168 - val_loss: 0.2394 - val_accuracy: 0.9151\n",
      "Epoch 21/50\n",
      "60000/60000 [==============================] - 25s 414us/sample - loss: 0.2247 - accuracy: 0.9171 - val_loss: 0.2484 - val_accuracy: 0.9093\n",
      "Epoch 22/50\n",
      "60000/60000 [==============================] - 25s 409us/sample - loss: 0.2230 - accuracy: 0.9190 - val_loss: 0.2454 - val_accuracy: 0.9106\n",
      "Epoch 23/50\n",
      "60000/60000 [==============================] - 25s 411us/sample - loss: 0.2208 - accuracy: 0.9176 - val_loss: 0.2444 - val_accuracy: 0.9110\n",
      "Epoch 24/50\n",
      "60000/60000 [==============================] - 24s 406us/sample - loss: 0.2169 - accuracy: 0.9194 - val_loss: 0.2519 - val_accuracy: 0.9128\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7fcc498b1550>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# speed up by training by not using augmentation, perhaps there are faster ways??\n",
    "xception.compile(loss='categorical_crossentropy', # 'categorical_crossentropy' 'mean_squared_error'\n",
    "                optimizer='adam', # 'adadelta' 'rmsprop'\n",
    "                metrics=['accuracy'])\n",
    "\n",
    "xception.fit(X_train, y_train_ohe, batch_size=32,\n",
    "            epochs=50, verbose=1,\n",
    "            validation_data=(X_test,y_test_ohe),\n",
    "            callbacks=[EarlyStopping(monitor='val_loss', patience=4)]\n",
    "            )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "summarize_net(xception, X_test, y_test, title_text='Xception on Fashion MNIST:')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using Pre-trained Models\n",
    "You also have fantastic access to different pre-trained models such as Xception, DenseNet, ResNest, VGG, and many other. For an updated key of available models see:\n",
    "- https://keras.io/applications/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# workaround for multiple OpenMP on Mac\n",
    "import os\n",
    "os.environ['KMP_DUPLICATE_LIB_OK']='True'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"resnet50\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            [(None, 224, 224, 3) 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv1_pad (ZeroPadding2D)       (None, 230, 230, 3)  0           input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv1_conv (Conv2D)             (None, 112, 112, 64) 9472        conv1_pad[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1_bn (BatchNormalization)   (None, 112, 112, 64) 256         conv1_conv[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv1_relu (Activation)         (None, 112, 112, 64) 0           conv1_bn[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "pool1_pad (ZeroPadding2D)       (None, 114, 114, 64) 0           conv1_relu[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "pool1_pool (MaxPooling2D)       (None, 56, 56, 64)   0           pool1_pad[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_1_conv (Conv2D)    (None, 56, 56, 64)   4160        pool1_pool[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_1_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block1_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_1_relu (Activation (None, 56, 56, 64)   0           conv2_block1_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_2_conv (Conv2D)    (None, 56, 56, 64)   36928       conv2_block1_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_2_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block1_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_2_relu (Activation (None, 56, 56, 64)   0           conv2_block1_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_0_conv (Conv2D)    (None, 56, 56, 256)  16640       pool1_pool[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_3_conv (Conv2D)    (None, 56, 56, 256)  16640       conv2_block1_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_0_bn (BatchNormali (None, 56, 56, 256)  1024        conv2_block1_0_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_3_bn (BatchNormali (None, 56, 56, 256)  1024        conv2_block1_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_add (Add)          (None, 56, 56, 256)  0           conv2_block1_0_bn[0][0]          \n",
      "                                                                 conv2_block1_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_out (Activation)   (None, 56, 56, 256)  0           conv2_block1_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_1_conv (Conv2D)    (None, 56, 56, 64)   16448       conv2_block1_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_1_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block2_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_1_relu (Activation (None, 56, 56, 64)   0           conv2_block2_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_2_conv (Conv2D)    (None, 56, 56, 64)   36928       conv2_block2_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_2_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block2_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_2_relu (Activation (None, 56, 56, 64)   0           conv2_block2_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_3_conv (Conv2D)    (None, 56, 56, 256)  16640       conv2_block2_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_3_bn (BatchNormali (None, 56, 56, 256)  1024        conv2_block2_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_add (Add)          (None, 56, 56, 256)  0           conv2_block1_out[0][0]           \n",
      "                                                                 conv2_block2_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_out (Activation)   (None, 56, 56, 256)  0           conv2_block2_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_1_conv (Conv2D)    (None, 56, 56, 64)   16448       conv2_block2_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_1_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block3_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_1_relu (Activation (None, 56, 56, 64)   0           conv2_block3_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_2_conv (Conv2D)    (None, 56, 56, 64)   36928       conv2_block3_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_2_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block3_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_2_relu (Activation (None, 56, 56, 64)   0           conv2_block3_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_3_conv (Conv2D)    (None, 56, 56, 256)  16640       conv2_block3_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_3_bn (BatchNormali (None, 56, 56, 256)  1024        conv2_block3_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_add (Add)          (None, 56, 56, 256)  0           conv2_block2_out[0][0]           \n",
      "                                                                 conv2_block3_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_out (Activation)   (None, 56, 56, 256)  0           conv2_block3_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_1_conv (Conv2D)    (None, 28, 28, 128)  32896       conv2_block3_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_1_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block1_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_1_relu (Activation (None, 28, 28, 128)  0           conv3_block1_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_2_conv (Conv2D)    (None, 28, 28, 128)  147584      conv3_block1_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_2_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block1_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_2_relu (Activation (None, 28, 28, 128)  0           conv3_block1_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_0_conv (Conv2D)    (None, 28, 28, 512)  131584      conv2_block3_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_3_conv (Conv2D)    (None, 28, 28, 512)  66048       conv3_block1_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_0_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block1_0_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_3_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block1_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_add (Add)          (None, 28, 28, 512)  0           conv3_block1_0_bn[0][0]          \n",
      "                                                                 conv3_block1_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_out (Activation)   (None, 28, 28, 512)  0           conv3_block1_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_1_conv (Conv2D)    (None, 28, 28, 128)  65664       conv3_block1_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_1_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block2_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_1_relu (Activation (None, 28, 28, 128)  0           conv3_block2_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_2_conv (Conv2D)    (None, 28, 28, 128)  147584      conv3_block2_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_2_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block2_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_2_relu (Activation (None, 28, 28, 128)  0           conv3_block2_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_3_conv (Conv2D)    (None, 28, 28, 512)  66048       conv3_block2_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_3_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block2_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_add (Add)          (None, 28, 28, 512)  0           conv3_block1_out[0][0]           \n",
      "                                                                 conv3_block2_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_out (Activation)   (None, 28, 28, 512)  0           conv3_block2_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_1_conv (Conv2D)    (None, 28, 28, 128)  65664       conv3_block2_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_1_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block3_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_1_relu (Activation (None, 28, 28, 128)  0           conv3_block3_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_2_conv (Conv2D)    (None, 28, 28, 128)  147584      conv3_block3_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_2_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block3_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_2_relu (Activation (None, 28, 28, 128)  0           conv3_block3_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_3_conv (Conv2D)    (None, 28, 28, 512)  66048       conv3_block3_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_3_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block3_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_add (Add)          (None, 28, 28, 512)  0           conv3_block2_out[0][0]           \n",
      "                                                                 conv3_block3_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_out (Activation)   (None, 28, 28, 512)  0           conv3_block3_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_1_conv (Conv2D)    (None, 28, 28, 128)  65664       conv3_block3_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_1_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block4_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_1_relu (Activation (None, 28, 28, 128)  0           conv3_block4_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_2_conv (Conv2D)    (None, 28, 28, 128)  147584      conv3_block4_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_2_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block4_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_2_relu (Activation (None, 28, 28, 128)  0           conv3_block4_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_3_conv (Conv2D)    (None, 28, 28, 512)  66048       conv3_block4_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_3_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block4_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_add (Add)          (None, 28, 28, 512)  0           conv3_block3_out[0][0]           \n",
      "                                                                 conv3_block4_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_out (Activation)   (None, 28, 28, 512)  0           conv3_block4_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_1_conv (Conv2D)    (None, 14, 14, 256)  131328      conv3_block4_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block1_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_1_relu (Activation (None, 14, 14, 256)  0           conv4_block1_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block1_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block1_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_2_relu (Activation (None, 14, 14, 256)  0           conv4_block1_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_0_conv (Conv2D)    (None, 14, 14, 1024) 525312      conv3_block4_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block1_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_0_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block1_0_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block1_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_add (Add)          (None, 14, 14, 1024) 0           conv4_block1_0_bn[0][0]          \n",
      "                                                                 conv4_block1_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_out (Activation)   (None, 14, 14, 1024) 0           conv4_block1_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block1_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block2_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_1_relu (Activation (None, 14, 14, 256)  0           conv4_block2_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block2_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block2_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_2_relu (Activation (None, 14, 14, 256)  0           conv4_block2_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block2_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block2_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_add (Add)          (None, 14, 14, 1024) 0           conv4_block1_out[0][0]           \n",
      "                                                                 conv4_block2_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_out (Activation)   (None, 14, 14, 1024) 0           conv4_block2_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block2_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block3_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_1_relu (Activation (None, 14, 14, 256)  0           conv4_block3_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block3_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block3_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_2_relu (Activation (None, 14, 14, 256)  0           conv4_block3_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block3_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block3_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_add (Add)          (None, 14, 14, 1024) 0           conv4_block2_out[0][0]           \n",
      "                                                                 conv4_block3_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_out (Activation)   (None, 14, 14, 1024) 0           conv4_block3_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block3_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block4_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_1_relu (Activation (None, 14, 14, 256)  0           conv4_block4_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block4_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block4_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_2_relu (Activation (None, 14, 14, 256)  0           conv4_block4_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block4_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block4_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_add (Add)          (None, 14, 14, 1024) 0           conv4_block3_out[0][0]           \n",
      "                                                                 conv4_block4_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_out (Activation)   (None, 14, 14, 1024) 0           conv4_block4_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block4_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block5_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_1_relu (Activation (None, 14, 14, 256)  0           conv4_block5_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block5_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block5_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_2_relu (Activation (None, 14, 14, 256)  0           conv4_block5_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block5_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block5_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_add (Add)          (None, 14, 14, 1024) 0           conv4_block4_out[0][0]           \n",
      "                                                                 conv4_block5_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_out (Activation)   (None, 14, 14, 1024) 0           conv4_block5_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block5_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block6_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_1_relu (Activation (None, 14, 14, 256)  0           conv4_block6_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block6_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block6_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_2_relu (Activation (None, 14, 14, 256)  0           conv4_block6_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block6_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block6_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_add (Add)          (None, 14, 14, 1024) 0           conv4_block5_out[0][0]           \n",
      "                                                                 conv4_block6_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_out (Activation)   (None, 14, 14, 1024) 0           conv4_block6_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_1_conv (Conv2D)    (None, 7, 7, 512)    524800      conv4_block6_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_1_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block1_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_1_relu (Activation (None, 7, 7, 512)    0           conv5_block1_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_2_conv (Conv2D)    (None, 7, 7, 512)    2359808     conv5_block1_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_2_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block1_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_2_relu (Activation (None, 7, 7, 512)    0           conv5_block1_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_0_conv (Conv2D)    (None, 7, 7, 2048)   2099200     conv4_block6_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_3_conv (Conv2D)    (None, 7, 7, 2048)   1050624     conv5_block1_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_0_bn (BatchNormali (None, 7, 7, 2048)   8192        conv5_block1_0_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_3_bn (BatchNormali (None, 7, 7, 2048)   8192        conv5_block1_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_add (Add)          (None, 7, 7, 2048)   0           conv5_block1_0_bn[0][0]          \n",
      "                                                                 conv5_block1_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_out (Activation)   (None, 7, 7, 2048)   0           conv5_block1_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_1_conv (Conv2D)    (None, 7, 7, 512)    1049088     conv5_block1_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_1_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block2_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_1_relu (Activation (None, 7, 7, 512)    0           conv5_block2_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_2_conv (Conv2D)    (None, 7, 7, 512)    2359808     conv5_block2_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_2_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block2_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_2_relu (Activation (None, 7, 7, 512)    0           conv5_block2_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_3_conv (Conv2D)    (None, 7, 7, 2048)   1050624     conv5_block2_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_3_bn (BatchNormali (None, 7, 7, 2048)   8192        conv5_block2_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_add (Add)          (None, 7, 7, 2048)   0           conv5_block1_out[0][0]           \n",
      "                                                                 conv5_block2_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_out (Activation)   (None, 7, 7, 2048)   0           conv5_block2_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_1_conv (Conv2D)    (None, 7, 7, 512)    1049088     conv5_block2_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_1_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block3_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_1_relu (Activation (None, 7, 7, 512)    0           conv5_block3_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_2_conv (Conv2D)    (None, 7, 7, 512)    2359808     conv5_block3_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_2_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block3_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_2_relu (Activation (None, 7, 7, 512)    0           conv5_block3_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_3_conv (Conv2D)    (None, 7, 7, 2048)   1050624     conv5_block3_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_3_bn (BatchNormali (None, 7, 7, 2048)   8192        conv5_block3_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_add (Add)          (None, 7, 7, 2048)   0           conv5_block2_out[0][0]           \n",
      "                                                                 conv5_block3_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_out (Activation)   (None, 7, 7, 2048)   0           conv5_block3_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "avg_pool (GlobalAveragePooling2 (None, 2048)         0           conv5_block3_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "probs (Dense)                   (None, 1000)         2049000     avg_pool[0][0]                   \n",
      "==================================================================================================\n",
      "Total params: 25,636,712\n",
      "Trainable params: 25,583,592\n",
      "Non-trainable params: 53,120\n",
      "__________________________________________________________________________________________________\n",
      "CPU times: user 7.87 s, sys: 1.15 s, total: 9.01 s\n",
      "Wall time: 15.6 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# manipulated from Keras Documentation\n",
    "#  https://keras.io/applications/\n",
    "from tensorflow.keras.applications.resnet50 import ResNet50\n",
    "from tensorflow.keras.preprocessing import image\n",
    "from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions\n",
    "import numpy as np\n",
    "\n",
    "res_model = ResNet50(weights='imagenet')\n",
    "res_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": [
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "from skimage.io import imshow\n",
    "\n",
    "def get_image(img_path):\n",
    "    \n",
    "    img = image.load_img(img_path, target_size=(224, 224))\n",
    "    imshow(image.img_to_array(img)/255)\n",
    "    plt.grid(False)\n",
    "    return image.img_to_array(img)\n",
    "\n",
    "img_path = 'data/Simba.jpg'\n",
    "x = get_image(img_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted: [('n02129165', 'lion', 0.9956474), ('n02130308', 'cheetah', 0.0013233849), ('n07802026', 'hay', 0.0010097753)]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "x = np.expand_dims(x, axis=0)\n",
    "x = preprocess_input(x)\n",
    "\n",
    "preds = res_model.predict(x)\n",
    "# # decode the results into a list of tuples (class, description, probability)\n",
    "print('Predicted:', decode_predictions(preds, top=3)[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted: [('n04285008', 'sports_car', 0.81929064), ('n03100240', 'convertible', 0.07494792), ('n02974003', 'car_wheel', 0.06959674), ('n03459775', 'grille', 0.026538586), ('n04037443', 'racer', 0.0042379284), ('n03770679', 'minivan', 0.0017388012), ('n02814533', 'beach_wagon', 0.0015881446), ('n04483307', 'trimaran', 0.00055104715), ('n03930630', 'pickup', 0.00035391172), ('n03670208', 'limousine', 0.00027896723)]\n"
     ]
    },
    {
     "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": [
    "img_path = 'data/MyCar.jpg'\n",
    "x = get_image(img_path)\n",
    "\n",
    "x = np.expand_dims(x, axis=0)\n",
    "x = preprocess_input(x)\n",
    "\n",
    "preds = res_model.predict(x)\n",
    "# decode the results into a list of tuples (class, description, probability)\n",
    "print('Predicted:', decode_predictions(preds, top=10)[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_____\n",
    "_____\n",
    "**Self Test: Should we do this Live?**\n",
    "\n",
    "- A. Yes.\n",
    "- B. No.\n",
    "- C. It Depends\n",
    "- D. We're gonna do it anyway\n",
    "\n",
    "If you have OpenCV installed, you can try this.\n",
    "\n",
    "# Imposter Syndrome:\n",
    "\n",
    "<img src=\"https://i.imgflip.com/29pmyu.jpg\" width=500>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "vc opened, getting first frame\n",
      "releasing...\n",
      "Done\n"
     ]
    }
   ],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "import time\n",
    "import itertools\n",
    "%matplotlib inline\n",
    "\n",
    "from tensorflow.keras.applications.resnet50 import ResNet50\n",
    "from tensorflow.keras.preprocessing import image\n",
    "from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions\n",
    "import numpy as np\n",
    "\n",
    "import os\n",
    "os.environ['KMP_DUPLICATE_LIB_OK']='True'\n",
    "\n",
    "if 'res_model' not in locals():\n",
    "    res_model = ResNet50(weights='imagenet')\n",
    "\n",
    "ESC_KEY = 27\n",
    "\n",
    "# setup some windows for viewing\n",
    "cv2.namedWindow(\"demowin1\")\n",
    "cv2.startWindowThread()\n",
    "\n",
    "# open the video card for capture\n",
    "vc = cv2.VideoCapture(0)\n",
    "\n",
    "if vc.isOpened():  # try to get the first frame\n",
    "    print (\"vc opened, getting first frame\")\n",
    "    rval, frame = vc.read()\n",
    "    # this will likely fail the first time\n",
    "    # the webcam often needs some time to open fully\n",
    "    key = 0\n",
    "else:\n",
    "    print (\"vc not open, exiting\")\n",
    "    key = ESC_KEY\n",
    "\n",
    "while key != ESC_KEY and vc.isOpened():  # the escape key and the capture device is open\n",
    "    rval, frame = vc.read()\n",
    "    key = cv2.waitKey(10)\n",
    "\n",
    "    if rval and frame is not None:\n",
    "        \n",
    "        frame_to_show = cv2.pyrDown(frame)  # make smaller immediately\n",
    "        # grab the ROI in top left (will show later on)\n",
    "        frame_down = frame_to_show[0:224,0:224]\n",
    "        \n",
    "        # increase contrast\n",
    "        img_yuv = cv2.cvtColor(frame_down, cv2.COLOR_BGR2YUV)\n",
    "        img_yuv[:,:,0] = cv2.equalizeHist(img_yuv[:,:,0])# equalize the histogram of the Y channel\n",
    "        frame_down = cv2.cvtColor(img_yuv, cv2.COLOR_YUV2BGR)# convert the YUV image back to RGB format\n",
    "\n",
    "        # and then remove noise\n",
    "        frame_down = cv2.fastNlMeansDenoisingColored(frame_down,None,7,10,7,21)\n",
    "# h (Optional): filter strength (luminance). Larger h value removes noise but alsoß image details\n",
    "# hColor (Optional):  The same as h but for color components. \n",
    "#    For most images value equals 10 will be enough to remove noise and not distort colors\n",
    "# templateWindowSize (Optional): Size in pixels of the template patch that is used to compute weights. Should be odd. \n",
    "#    Recommended value 7 pixels\n",
    "# searchWindowSize (Optional): Size in pixels of the window that is used to compute weighted average for given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater denoising time. \n",
    "#    Recommended value 21 pixels\n",
    "        \n",
    "        img = frame_down.copy() # make a copy for numpy\n",
    "\n",
    "        x = image.img_to_array(img[:,:,::-1]) # convert to numpy\n",
    "        x = np.expand_dims(x, axis=0) # add batch dimension\n",
    "        x = preprocess_input(x) # apply resnet presets\n",
    "\n",
    "        preds = res_model.predict(x) # get the predictions\n",
    "        \n",
    "        # decode the results into a list of tuples (class, description, probability)\n",
    "        txt = str(decode_predictions(preds, top=3)[0])\n",
    "\n",
    "        cv2.putText(frame_to_show, txt, (10, 50), cv2.FONT_HERSHEY_SIMPLEX, 0.5, [255, 255, 255])\n",
    "        cv2.rectangle(frame_to_show, (0,0), (224,224), (255,255,255), 1)\n",
    "        cv2.imshow(\"demowin1\", frame_to_show)\n",
    "    \n",
    "print('releasing...')\n",
    "cv2.waitKey(1)\n",
    "cv2.destroyAllWindows()\n",
    "cv2.waitKey(1)\n",
    "print('Done')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Transfer Learning With ResNet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/eclarson/anaconda3/envs/mlenv/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(50000, 32, 32, 3)\n",
      "(50000, 1)\n",
      "(10000, 32, 32, 3)\n",
      "(10000, 1)\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.datasets import cifar10\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "\n",
    "NUM_CLASSES = 10\n",
    "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
    "\n",
    "y_train_ohe = to_categorical(y_train, NUM_CLASSES)\n",
    "y_test_ohe = to_categorical(y_test, NUM_CLASSES)\n",
    "\n",
    "print(x_train.shape)\n",
    "print(y_train.shape)\n",
    "print(x_test.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/eclarson/anaconda3/envs/mlenv/lib/python3.6/site-packages/ipykernel_launcher.py:6: DeprecationWarning: `imresize` is deprecated!\n",
      "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n",
      "Use ``skimage.transform.resize`` instead.\n",
      "  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(50000, 64, 64, 3)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/eclarson/anaconda3/envs/mlenv/lib/python3.6/site-packages/ipykernel_launcher.py:10: DeprecationWarning: `imresize` is deprecated!\n",
      "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n",
      "Use ``skimage.transform.resize`` instead.\n",
      "  # Remove the CWD from sys.path while we load stuff.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10000, 64, 64, 3)\n",
      "CPU times: user 7.59 s, sys: 973 ms, total: 8.57 s\n",
      "Wall time: 8.68 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# from skimage.transform import resize # stupid slow function\n",
    "from scipy.misc import imresize\n",
    "import numpy as np\n",
    "\n",
    "# CIFAR is really small, so let's increase the size of it so its not so small\n",
    "# once we put it through all the downsampling layers\n",
    "x_train_up = [imresize(x, size=(64,64,3), interp='nearest') for x in x_train]\n",
    "x_train_up = np.stack(x_train_up, axis=0)\n",
    "print(x_train_up.shape)\n",
    "\n",
    "x_test_up = [imresize(x, size=(64,64,3), interp='nearest') for x in x_test]\n",
    "x_test_up = np.stack(x_test_up, axis=0)\n",
    "print(x_test_up.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "# from skimage.io import imshow\n",
    "idx = 5\n",
    "plt.imshow((x_train[idx]-x_train[idx].min())/(x_train[idx].max()-x_train[idx].min()))\n",
    "plt.grid(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "136.22101"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train_up[0].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/eclarson/anaconda3/envs/mlenv/lib/python3.6/site-packages/keras_applications/resnet50.py:263: UserWarning: The output shape of `ResNet50(include_top=False)` has been changed since Keras 2.2.0.\n",
      "  warnings.warn('The output shape of `ResNet50(include_top=False)` '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2.88 s, sys: 28.6 ms, total: 2.9 s\n",
      "Wall time: 1.56 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(1, 2, 2, 2048)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# connect new layers to the output\n",
    "from keras.applications.resnet50 import ResNet50\n",
    "from keras.applications.resnet50 import preprocess_input, decode_predictions\n",
    "\n",
    "# load only convolutional layers of resnet:\n",
    "if 'res_no_top' not in locals():\n",
    "    res_no_top = ResNet50(weights='imagenet', include_top=False)\n",
    "\n",
    "x = x_train_up[0]\n",
    "x = np.expand_dims(x, axis=0)\n",
    "x = preprocess_input(x)\n",
    "\n",
    "%time preds = res_no_top.predict(x)\n",
    "preds.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_up = preprocess_input(x_train_up)\n",
    "x_test_up = preprocess_input(x_test_up)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(50000, 2, 2, 2048)\n",
      "CPU times: user 1d 4h 52min 22s, sys: 6min 57s, total: 1d 4h 59min 20s\n",
      "Wall time: 7h 46min 7s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# train on half the data, to save a few hours\n",
    "x_train_resnet = res_no_top.predict(x_train_up)\n",
    "x_test_resnet = res_no_top.predict(x_test_up)\n",
    "print(x_train_resnet.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_2 (InputLayer)         (None, 2, 2, 2048)        0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 8192)              0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 200)               1638600   \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 10)                2010      \n",
      "=================================================================\n",
      "Total params: 1,640,610\n",
      "Trainable params: 1,640,610\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from keras.layers import SeparableConv2D\n",
    "from keras.layers.normalization import BatchNormalization\n",
    "from keras.layers import Add, Flatten, Dense\n",
    "from keras.layers import average, concatenate\n",
    "from keras.models import Input, Model\n",
    "\n",
    "\n",
    "# let's add a fully-connected layer\n",
    "input_x = Input(shape=x_train_resnet[0].shape)\n",
    "x = Flatten()(input_x)\n",
    "x = Dense(200, activation='relu',kernel_initializer='he_uniform')(x)\n",
    "# and a fully connected layer \n",
    "predictions = Dense(NUM_CLASSES, activation='softmax', kernel_initializer='glorot_uniform')(x)\n",
    "\n",
    "model = Model(inputs=input_x, outputs=predictions)\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 50000 samples, validate on 10000 samples\n",
      "Epoch 1/5\n",
      "50000/50000 [==============================] - 232s 5ms/step - loss: 1.0178 - acc: 0.7244 - val_loss: 0.7912 - val_acc: 0.7600\n",
      "Epoch 2/5\n",
      "50000/50000 [==============================] - 225s 4ms/step - loss: 0.6051 - acc: 0.8122 - val_loss: 0.7327 - val_acc: 0.7786\n",
      "Epoch 3/5\n",
      "50000/50000 [==============================] - 220s 4ms/step - loss: 0.4722 - acc: 0.8522 - val_loss: 0.7996 - val_acc: 0.7807\n",
      "Epoch 4/5\n",
      "50000/50000 [==============================] - 221s 4ms/step - loss: 0.3650 - acc: 0.8873 - val_loss: 0.9270 - val_acc: 0.7770\n",
      "Epoch 5/5\n",
      "50000/50000 [==============================] - 223s 4ms/step - loss: 0.2813 - acc: 0.9145 - val_loss: 1.1049 - val_acc: 0.7830\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0xb20dc0c18>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_ohe_resnet = y_train_ohe[:x_train_resnet.shape[0]]\n",
    "\n",
    "model.compile(optimizer='rmsprop', loss='categorical_crossentropy',metrics=['accuracy'])\n",
    "\n",
    "model.fit(x_train_resnet,y_train_ohe_resnet,epochs=5,\n",
    "          batch_size=64,verbose=1,\n",
    "         validation_data=(x_test_resnet, y_test_ohe[:x_test_resnet.shape[0]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summarize_net(model, x_test_resnet, y_test[:x_test_resnet.shape[0]], title_text='Transfer Learning, ResNet on CIFAR:')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 50000 samples, validate on 10000 samples\n",
      "Epoch 1/5\n",
      "50000/50000 [==============================] - 252s 5ms/step - loss: 0.2184 - acc: 0.9357 - val_loss: 1.1913 - val_acc: 0.7839\n",
      "Epoch 2/5\n",
      "50000/50000 [==============================] - 259s 5ms/step - loss: 0.1689 - acc: 0.9529 - val_loss: 1.3205 - val_acc: 0.7889\n",
      "Epoch 3/5\n",
      "50000/50000 [==============================] - 225s 4ms/step - loss: 0.1373 - acc: 0.9625 - val_loss: 1.5595 - val_acc: 0.7811\n",
      "Epoch 4/5\n",
      "50000/50000 [==============================] - 226s 5ms/step - loss: 0.1126 - acc: 0.9692 - val_loss: 1.5395 - val_acc: 0.7917\n",
      "Epoch 5/5\n",
      "50000/50000 [==============================] - 228s 5ms/step - loss: 0.0891 - acc: 0.9762 - val_loss: 1.6964 - val_acc: 0.7901\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0xb6d099ba8>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train_resnet,y_train_ohe_resnet,epochs=5,\n",
    "          batch_size=64,verbose=1,\n",
    "         validation_data=(x_test_resnet, y_test_ohe[:x_test_resnet.shape[0]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summarize_net(model, x_test_resnet, y_test[:x_test_resnet.shape[0]], title_text='Extended Training, ResNet on CIFAR:')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 50000 samples, validate on 10000 samples\n",
      "Epoch 1/5\n",
      "50000/50000 [==============================] - 224s 4ms/step - loss: 0.0909 - acc: 0.9782 - val_loss: 1.7258 - val_acc: 0.7903\n",
      "Epoch 2/5\n",
      "50000/50000 [==============================] - 226s 5ms/step - loss: 0.0769 - acc: 0.9812 - val_loss: 1.7928 - val_acc: 0.7953\n",
      "Epoch 3/5\n",
      "50000/50000 [==============================] - 228s 5ms/step - loss: 0.0651 - acc: 0.9852 - val_loss: 1.9714 - val_acc: 0.7861\n",
      "Epoch 4/5\n",
      "50000/50000 [==============================] - 223s 4ms/step - loss: 0.0687 - acc: 0.9849 - val_loss: 1.9146 - val_acc: 0.7958\n",
      "Epoch 5/5\n",
      "50000/50000 [==============================] - 237s 5ms/step - loss: 0.0589 - acc: 0.9872 - val_loss: 1.9639 - val_acc: 0.7911\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0xb6d095710>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train_resnet,y_train_ohe_resnet,epochs=5,\n",
    "          batch_size=64,verbose=1,\n",
    "         validation_data=(x_test_resnet, y_test_ohe[:x_test_resnet.shape[0]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}