lec3_keras_dense_visualization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction to Deep Learning \n",
    "# \n",
    "# Visualization\n",
    "#\n",
    "# Multilayer Perceptron with Keras - Dense Layer\n",
    "### Build and visualize Simple feed forward dense network with Mnist data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "import numpy as np\n",
    "#import utils_plot as uplot\n",
    "import matplotlib.gridspec as gridspec\n",
    "from sklearn.metrics import accuracy_score, log_loss\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "%matplotlib inline  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def drow_history(history, par='loss'):\n",
    "    \n",
    "    # list all data in history\n",
    "    print(history.history.keys())\n",
    "    # summarize history for loss\n",
    "    plt.figure(figsize=(5,4))\n",
    "    plt.ylim(0,1)\n",
    "    plt.plot(history.history[par])\n",
    "    plt.plot(history.history['val_'+par])\n",
    "    plt.title('model '+par)\n",
    "    plt.ylabel('loss')\n",
    "    plt.xlabel('epoch')\n",
    "    plt.legend(['train', 'test'], loc='upper left')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.gridspec as gridspec\n",
    "import matplotlib.pyplot as plt\n",
    "# cb - cotrast_brightness\n",
    "def show_n_images(imgs, cmap='gray', titles = None, enlarge = 4, mtitle=None,\n",
    "                  cut = 0, axis_off = False, fontsize=40, cb = 1):\n",
    "    \n",
    "    plt.set_cmap(cmap);\n",
    "    \n",
    "    n = len(imgs);\n",
    "    gs1 = gridspec.GridSpec(1, n);\n",
    "    \n",
    "    fig1 = plt.figure(figsize=(8*len(imgs),8)); \n",
    "    for i in range(n):\n",
    "\n",
    "        ax1 = fig1.add_subplot(gs1[i]);\n",
    "        if (cb):\n",
    "            if len(np.unique(imgs[i])<=5):\n",
    "                 img = imgs[i]\n",
    "            else:\n",
    "                \n",
    "                img = cont_br(imgs[i])\n",
    "        else:\n",
    "            img = imgs[i]\n",
    "        if cut:\n",
    "            ax1.imshow(img[50:290, 75:450] , interpolation='none');\n",
    "        else:\n",
    "            \n",
    "            ax1.imshow(img, interpolation='none');\n",
    "        if (titles is not None):\n",
    "            ax1.set_title(titles[i], fontsize=fontsize);  #, fontweight=\"bold\");\n",
    "        if (axis_off):\n",
    "            plt.axis('off')\n",
    "    if mtitle:\n",
    "        plt.title(mtitle)\n",
    "    plt.tight_layout()\n",
    "    plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lets start Keras\n",
    "In the Mnist classification example, we saw that the net basically learn templates.<br>\n",
    "This is not robust enough for generalization. <br>\n",
    "To generalize, we want the neural network to learn **features**. <br>\n",
    "For that we will need deeper and more complexed networks.<br>\n",
    "Lets build our first neural network with Keras.\n",
    "\n",
    "We will use:\n",
    "\n",
    "### Sequential Model\n",
    "https://keras.io/models/sequential/\n",
    "\n",
    "### Dense layer\n",
    "https://keras.io/layers/core/\n",
    "\n",
    "### Dropout layer\n",
    "https://keras.io/layers/core/\n",
    "\n",
    "### Optimizer Adam\n",
    "https://keras.io/optimizers/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import keras\n",
    "# Keras has many build in data sets\n",
    "from keras.datasets import mnist\n",
    "# Sequential is the basic feed forward neural network (FFN)\n",
    "from keras.models import Sequential\n",
    "# Dense is fully connected layer\n",
    "# Dropout is a \"noising\" layer, to prevent over feet.\n",
    "from keras.layers import Dense, Dropout\n",
    "# Read about optimizers in keras documentation\n",
    "#from keras.optimizers import Adam\n",
    "from tensorflow.keras.optimizers import Adam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_moons, make_circles, make_classification\n",
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from matplotlib.colors import ListedColormap\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mnist example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60000 train samples\n",
      "10000 test samples\n"
     ]
    }
   ],
   "source": [
    "batch_size = 128\n",
    "num_classes = 10\n",
    "\n",
    "\n",
    "# the data, split between train and test sets\n",
    "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
    "\n",
    "x_train = x_train.reshape(60000, 784)\n",
    "x_test = x_test.reshape(10000, 784)\n",
    "x_train = x_train.astype('float32')\n",
    "x_test = x_test.astype('float32')\n",
    "x_train /= 255\n",
    "x_test /= 255\n",
    "print(x_train.shape[0], 'train samples')\n",
    "print(x_test.shape[0], 'test samples')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 10)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelBinarizer\n",
    "le = LabelBinarizer()\n",
    "\n",
    "y_train = le.fit(y_train).transform(y_train)\n",
    "y_test = le.transform(y_test)\n",
    "y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create the model\n",
    "In previous chapter,I used the word network,in practical world, it called model.<br>\n",
    "Model is from keras examples: https://github.com/keras-team/keras/blob/master/examples/mnist_mlp.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_3\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_9 (Dense)              (None, 512)               401920    \n",
      "_________________________________________________________________\n",
      "dense_10 (Dense)             (None, 512)               262656    \n",
      "_________________________________________________________________\n",
      "dense_11 (Dense)             (None, 10)                5130      \n",
      "=================================================================\n",
      "Total params: 669,706\n",
      "Trainable params: 669,706\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "\n",
    "model = Sequential()\n",
    "model.add(Dense(512, activation='relu', input_shape=(784,)))\n",
    "model.add(Dense(512, activation='relu'))\n",
    "model.add(Dense(num_classes, activation='softmax'))\n",
    "\n",
    "model.summary()\n",
    "\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer=Adam(),\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/4\n",
      "469/469 [==============================] - 3s 6ms/step - loss: 0.2174 - accuracy: 0.9342 - val_loss: 0.1021 - val_accuracy: 0.9673\n",
      "Epoch 2/4\n",
      "469/469 [==============================] - 3s 6ms/step - loss: 0.0814 - accuracy: 0.9753 - val_loss: 0.0835 - val_accuracy: 0.9740\n",
      "Epoch 3/4\n",
      "469/469 [==============================] - 2s 5ms/step - loss: 0.0502 - accuracy: 0.9839 - val_loss: 0.1078 - val_accuracy: 0.9661\n",
      "Epoch 4/4\n",
      "469/469 [==============================] - 2s 5ms/step - loss: 0.0368 - accuracy: 0.9883 - val_loss: 0.0665 - val_accuracy: 0.9799\n",
      "Test loss: 0.06650950014591217\n",
      "Test accuracy: 0.9799000024795532\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(x_train, y_train,\n",
    "                    batch_size=128,\n",
    "                    epochs=4,\n",
    "                    verbose=1,\n",
    "                    validation_data=(x_test, y_test))\n",
    "score = model.evaluate(x_test, y_test, verbose=0)\n",
    "print('Test loss:', score[0])\n",
    "print('Test accuracy:', score[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "drow_history(history, 'loss')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss: 0.09009279310703278\n",
      "Test accuracy: 0.9697999954223633\n"
     ]
    }
   ],
   "source": [
    "score = model.evaluate(x_test, y_test, verbose=0)\n",
    "print('Test loss:', score[0])\n",
    "print('Test accuracy:', score[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualize Model layers\n",
    "While training the model, lets view the layers, meaning the incoming weights, to each neuron in the layer. Lets train for 2 epoch's, and examine the results. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_7\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_21 (Dense)             (None, 512)               401920    \n",
      "_________________________________________________________________\n",
      "dense_22 (Dense)             (None, 512)               262656    \n",
      "_________________________________________________________________\n",
      "dense_23 (Dense)             (None, 10)                5130      \n",
      "=================================================================\n",
      "Total params: 669,706\n",
      "Trainable params: 669,706\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Epoch 1/4\n",
      "469/469 [==============================] - 3s 6ms/step - loss: 0.2189 - accuracy: 0.9359 - val_loss: 0.1013 - val_accuracy: 0.9699\n",
      "Epoch 2/4\n",
      "469/469 [==============================] - 2s 5ms/step - loss: 0.0801 - accuracy: 0.9752 - val_loss: 0.0818 - val_accuracy: 0.9745\n",
      "Epoch 3/4\n",
      "469/469 [==============================] - 3s 5ms/step - loss: 0.0503 - accuracy: 0.9843 - val_loss: 0.0741 - val_accuracy: 0.9770\n",
      "Epoch 4/4\n",
      "469/469 [==============================] - 2s 5ms/step - loss: 0.0364 - accuracy: 0.9880 - val_loss: 0.0683 - val_accuracy: 0.9791\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(512, activation='relu', input_shape=(784,)))\n",
    "model.add(Dense(512, activation='relu'))\n",
    "model.add(Dense(num_classes, activation='softmax'))\n",
    "\n",
    "model.summary()\n",
    "\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer=Adam(),\n",
    "              metrics=['accuracy'])\n",
    "history = model.fit(x_train, y_train,\n",
    "                    batch_size=batch_size,\n",
    "                    epochs=4,\n",
    "                    verbose=1,\n",
    "                    validation_data=(x_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### First Hidden Layer\n",
    "First layer is a dense layer of size 512, meaning that each input pixel intensity, from the 764 inputs, is connected to each neuron in the hidden layer.<br>\n",
    "So we get $784*512$ incoming weights to the first hidden layer. Lets look at them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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": [
    "W0 = model.get_layer(name=model.layers[0].name).get_weights()[0]\n",
    "W0a=W0.reshape((784,512)).copy()\n",
    "plt.imshow(W0a.T);\n",
    "plt.set_cmap('gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each row in the above matrix,is multiplication by the input layer pixels intensities,or the input vector, and produce $512$ input vector to the next layer.<br>\n",
    "To demonstrate the weights, we will reshape each row(of 784 weights) back to the (28,28) original shape, so we can see what patterns are causing activation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 5760x576 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "dense_list0 = [W0a.T[i, :].reshape(28,28) for i in range(10)]\n",
    "show_n_images(dense_list0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Activation Example of the First Hidden Layer\n",
    "Lets check the network \"response\" or activation, to one of the digit images from the test set, and follow the weighs and activation flow down to the last output layer.<br>\n",
    "To do that, we multiply $W^T*X$,where $X$ is an input image. <br>\n",
    "The image:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x154832e5550>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "W70=W0a.T*x_test[90]\n",
    "plt.imshow(x_test[90].reshape(28,28))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets view the activation of the image, the result of the multiplication above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x154952910d0>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(W70)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again,to understand the activation patterns,we reshape them back to the(28,28) original shape."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 5760x576 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dense_list70 = [W70[i, :].reshape(28,28) for i in range(10)]\n",
    "show_n_images(dense_list70)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Second hodden layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x154857f0400>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "W1 = model.get_layer(name=model.layers[1].name).get_weights()[0]\n",
    "Wa1=W1.reshape((512,512)).copy()\n",
    "plt.imshow(Wa1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 5760x576 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dense_list1= [Wa1[i,:].reshape(32,16) for i in range(10)]\n",
    "show_n_images(dense_list1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1548590ad90>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "W17 = np.dot((Wa1.T),W70)\n",
    "plt.imshow(W17)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 5760x576 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dense_list17= [W17[i,:].reshape(28,28) for i in range(10)]\n",
    "show_n_images(dense_list17)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Last Dense Layer\n",
    "Last dense layer size is 10, as the number of classes. Following is a view of this Layer, as a matrix, and with each row resized to a rectangle."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x15482bc05e0>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "W2 = model.get_layer(name=model.layers[2].name).get_weights()[0]\n",
    "W2a=W2.reshape((10,512)).copy()\n",
    "plt.imshow(W2a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 5760x576 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "dense_list2= [W2a[i, :].reshape(32,16) for i in range(10)]\n",
    "show_n_images(dense_list2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hard to see any patterns in this layer visualization. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1548398bf70>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "W27=np.dot(W2.T,W17)\n",
    "plt.imshow(W27)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 5760x576 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dense_list27= [W27[i, :].reshape(28,28) for i in range(10)]\n",
    "show_n_images(dense_list27)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Apply Softmax on the last hidden layer.\n",
    "We can see from the weights image, that the \"template\" or row for the digit 3, is standing out in the activation row, and that is reflected in the numbers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def softmax(x):\n",
    "    \"\"\"Compute softmax values for each sets of scores in x.\"\"\"\n",
    "    e_x = np.exp(x - np.max(x))\n",
    "    return e_x / e_x.sum(axis=0) # only difference\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4.3171024, -12.391281, -42.54796, 43.192223, -8.390995, -19.481857, -55.822113, -21.099525, 27.462807, 19.185263]\n",
      "0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 "
     ]
    }
   ],
   "source": [
    "print([W27[i].sum() for i in range(10)])\n",
    "x = [W27[i].sum() for i in range(10)]\n",
    "\n",
    "pred = softmax(x)#np.exp(x)/np.sum(x)\n",
    "\n",
    "for i in range(10):\n",
    "    print(\"{0:.2f}\".format(pred[i]), end=' ')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classification Exercise Fashion Mnist\n",
    "https://keras.io/datasets/\n",
    "Dataset of 60,000 28x28 grayscale images of 10 fashion categories, along with a test set of 10,000 images. This dataset can be used as a drop-in replacement for MNIST. The class labels are:\n",
    "```\n",
    "* Label\tDescription\n",
    "0\tT-shirt/top\n",
    "1\tTrouser\n",
    "2\tPullover\n",
    "3\tDress\n",
    "4\tCoat\n",
    "5\tSandal\n",
    "6\tShirt\n",
    "7\tSneaker\n",
    "8\tBag\n",
    "9\tAnkle boot\n",
    "```\n",
    "\n",
    "Build the best model for this data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.datasets import fashion_mnist\n",
    "\n",
    "(x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((28, 28), (60000, 28, 28), (10000, 28, 28))"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train[0].shape, x_train.shape,x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['lable 9', 'lable 0', 'lable 0', 'lable 3', 'lable 0', 'lable 2', 'lable 7', 'lable 2', 'lable 5', 'lable 5', 'lable 0', 'lable 9', 'lable 5', 'lable 5', 'lable 7', 'lable 9', 'lable 1', 'lable 0', 'lable 6', 'lable 4']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 5760x576 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "imgs = [x_train[i].reshape(28,28) for i in range(100,110)]\n",
    "titles = [(\"lable {}\").format(y_train[i]) for i in range(20)]\n",
    "print(titles)\n",
    "show_n_images(imgs, titles=titles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60000 train samples\n",
      "10000 test samples\n"
     ]
    }
   ],
   "source": [
    "x_train = x_train.reshape(60000, 784)\n",
    "x_test = x_test.reshape(10000, 784)\n",
    "x_train = x_train.astype('float32')\n",
    "x_test = x_test.astype('float32')\n",
    "x_train /= 255\n",
    "x_test /= 255\n",
    "print(x_train.shape[0], 'train samples')\n",
    "print(x_test.shape[0], 'test samples')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 10)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelBinarizer\n",
    "num_classes=10\n",
    "le = LabelBinarizer()\n",
    "\n",
    "y_train = le.fit(y_train).transform(y_train)\n",
    "y_test = le.transform(y_test)\n",
    "y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_7\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_21 (Dense)             (None, 512)               401920    \n",
      "_________________________________________________________________\n",
      "dense_22 (Dense)             (None, 512)               262656    \n",
      "_________________________________________________________________\n",
      "dense_23 (Dense)             (None, 10)                5130      \n",
      "_________________________________________________________________\n",
      "dense_24 (Dense)             (None, 512)               5632      \n",
      "_________________________________________________________________\n",
      "dense_25 (Dense)             (None, 512)               262656    \n",
      "_________________________________________________________________\n",
      "dense_26 (Dense)             (None, 10)                5130      \n",
      "=================================================================\n",
      "Total params: 943,124\n",
      "Trainable params: 943,124\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Epoch 1/2\n",
      "469/469 [==============================] - 4s 8ms/step - loss: 1.0114 - accuracy: 0.5319 - val_loss: 0.5022 - val_accuracy: 0.8244\n",
      "Epoch 2/2\n",
      "469/469 [==============================] - 4s 8ms/step - loss: 0.4196 - accuracy: 0.8503 - val_loss: 0.4093 - val_accuracy: 0.8536\n"
     ]
    }
   ],
   "source": [
    "## model = Sequential()\n",
    "model.add(Dense(512, activation='relu', input_shape=(784,)))\n",
    "model.add(Dense(512, activation='relu'))\n",
    "model.add(Dense(num_classes, activation='softmax'))\n",
    "\n",
    "model.summary()\n",
    "\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer=Adam(),\n",
    "              metrics=['accuracy'])\n",
    "history = model.fit(x_train, y_train,\n",
    "                    batch_size=batch_size,\n",
    "                    epochs=2,\n",
    "                    verbose=1,\n",
    "                    validation_data=(x_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "drow_history(history, 'loss')\n",
    "drow_history(history, 'accuracy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss: 0.4092945158481598\n",
      "Test accuracy: 0.853600025177002\n"
     ]
    }
   ],
   "source": [
    "score = model.evaluate(x_test, y_test, verbose=0)\n",
    "print('Test loss:', score[0])\n",
    "print('Test accuracy:', score[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.2357342e-05, 1.0145005e-04, 7.1863840e-05, 4.6150148e-05,\n",
       "        2.7198093e-05, 2.0681327e-02, 8.0056025e-06, 5.4682499e-01,\n",
       "        1.9077031e-03, 4.3031898e-01],\n",
       "       [1.0853126e-03, 4.3485445e-05, 9.8235798e-01, 1.9894067e-04,\n",
       "        8.1256591e-03, 2.0268157e-05, 7.2991257e-03, 6.8995672e-05,\n",
       "        7.9826982e-04, 1.9874724e-06],\n",
       "       [1.1200455e-03, 9.9839056e-01, 1.4872539e-08, 4.5259760e-04,\n",
       "        6.1165062e-10, 6.8977841e-09, 3.6843485e-05, 2.0621721e-11,\n",
       "        2.8299070e-08, 9.0032343e-10],\n",
       "       [1.1354522e-03, 9.9836355e-01, 1.5530462e-08, 4.6317719e-04,\n",
       "        6.4287742e-10, 7.2360140e-09, 3.7746951e-05, 2.1995034e-11,\n",
       "        2.9625896e-08, 9.4879471e-10],\n",
       "       [1.0027370e-01, 1.9134879e-03, 1.2284215e-01, 9.7609153e-03,\n",
       "        2.0619383e-02, 4.4970209e-05, 7.4020272e-01, 1.1795855e-05,\n",
       "        4.3238373e-03, 7.0523711e-06]], dtype=float32)"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_test = model.predict(x_test)\n",
    "pred_test[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([7, 2, 1, 1, 6, 1, 4, 6, 5, 7], dtype=int64),\n",
       " array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 1],\n",
       "        [0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n",
       "        [0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       "        [0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       "        [0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n",
       "        [0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       "        [0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n",
       "        [0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n",
       "        [0, 0, 0, 0, 0, 1, 0, 0, 0, 0],\n",
       "        [0, 0, 0, 0, 0, 0, 0, 1, 0, 0]]))"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_test=np.argmax(pred_test, axis=1)\n",
    "y_pred_test[0:10], y_test[0:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the prediction are very accurate, but we can see as well that the network is limited in its ability to understand digits in different locations and orientations. <br>\n",
    "Lets think about the following idea: We have an image, with a digit inside. if we can \"move\" our network around, and position it on the part containing the digit, in the right orientation, we can  classify the digit correctly. This is, in a very simplified form, the idea of **convolutional neural  networks**. They work with sets of filters, slided over the image, to capture specific features, without being bind to specific location."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Learn more:\n",
    "* https://keras.io/\n",
    "* https://www.coursera.org/learn/neural-networks/lecture/kOQiH/why-object-recognition-is-difficult-5-min"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Articles:\n",
    "* Zhe Li1, Boqing Gong, Tianbao Yang. **Improved Dropout for Shallow and Deep Learning**, \n",
    "The University of Iowa, Iowa city, IA 52245. University of Central Florida, Orlando, FL 32816. https://arxiv.org/pdf/1602.02220.pdf\n",
    "* Nitish Srivastava,Geoffrey Hinton, Alex Krizhevsky, Ruslan Salakhutdinov, Ilya Sutskever. **Dropout: A Simple Way to Prevent Neural Networks from Overfitting**,  June 2014Journal of Machine Learning Research 15(1):1929-1958\n",
    "* Ilya Sutskever, James Martens, George Dahl, Geoffrey Hinton. **On the importance of initialization and momentum in deep learning**.  ICML'13 Proceedings of the 30th International Conference on International Conference on Machine Learning - Volume 28 Pages III-1139-III-1147. Atlanta, GA, USA — June 16 - 21, 2013 http://proceedings.mlr.press/v28/sutskever13.pdf\n",
    "\n"
   ]
  }
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