9.convolutional_neural_networks.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convolutional networks\n",
    "\n",
    "This is an annotated version of Keras's [example MNIST CNN code](https://github.com/fchollet/keras/blob/master/examples/mnist_cnn.py) for how to implement convolutional networks.\n",
    "\n",
    "The CIFAR-10 classification task is a classic machine learning benchmark. The data includes 50,000 images belonging to 10 classes, and the task is to identify them. Along with MNIST, CIFAR-10 classification is a sort of like a \"hello world\" for computer vision and convolutional networks, so a solution can be implemented quickly with an off-the-shelf machine learning library.\n",
    "\n",
    "Since convolutional neural networks have thus far proven to be the best at computer vision tasks, we'll use the Keras library to implement a convolutional networks as our solution. Keras provides a well-designed and readable API on top of TensorFlow's backend, so we'll be done in a surprisingly short amount of steps!\n",
    "\n",
    "Note, if you have been running these notebooks on a regular laptop without GPU until now, it's going to become more and more difficult to do so. The neural networks we will be training, starting with convolutional networks, will become increasingly memory and processing-intensive and may slow down laptops without good graphics processing. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import random\n",
    "\n",
    "import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout\n",
    "from keras.layers import Conv2D, MaxPooling2D, Flatten\n",
    "\n",
    "from keras.layers import Activation\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Recall that a basic neural network in Keras can be set up like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_35 (Dense)             (None, 100)               307300    \n",
      "_________________________________________________________________\n",
      "dense_36 (Dense)             (None, 100)               10100     \n",
      "_________________________________________________________________\n",
      "dense_37 (Dense)             (None, 10)                1010      \n",
      "=================================================================\n",
      "Total params: 318,410\n",
      "Trainable params: 318,410\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(100, activation='sigmoid', input_dim=3072))\n",
    "model.add(Dense(100, activation='sigmoid'))\n",
    "model.add(Dense(10, activation='softmax'))\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We load CIFAR-10 dataset and reshape them as individual vectors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50000 train samples, 10000 test samples\n",
      "training data shape:  (50000, 3072) (50000, 10)\n",
      "test data shape:  (10000, 3072) (10000, 10)\n"
     ]
    }
   ],
   "source": [
    "from keras.datasets import cifar10\n",
    "\n",
    "# load CIFAR\n",
    "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
    "num_classes = 10\n",
    "\n",
    "# reshape CIFAR\n",
    "x_train = x_train.reshape(50000, 32*32*3)\n",
    "x_test = x_test.reshape(10000, 32*32*3)\n",
    "\n",
    "# make float32\n",
    "x_train = x_train.astype('float32')\n",
    "x_test = x_test.astype('float32')\n",
    "\n",
    "# normalize to (0-1)\n",
    "x_train /= 255\n",
    "x_test /= 255\n",
    "\n",
    "# convert class vectors to binary class matrices\n",
    "y_train = keras.utils.to_categorical(y_train, num_classes)\n",
    "y_test = keras.utils.to_categorical(y_test, num_classes)\n",
    "\n",
    "print('%d train samples, %d test samples'%(x_train.shape[0], x_test.shape[0]))\n",
    "print(\"training data shape: \", x_train.shape, y_train.shape)\n",
    "print(\"test data shape: \", x_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see some of our samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f282998b6d8>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "samples = np.concatenate([np.concatenate([x_train[i].reshape((32,32,3)) for i in [int(random.random() * len(x_train)) for i in range(16)]], axis=1) for i in range(6)], axis=0)\n",
    "plt.figure(figsize=(16,6))\n",
    "plt.imshow(samples, cmap='gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can compile the model using categorical-cross-entropy loss, and train it for 30 epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 50000 samples, validate on 10000 samples\n",
      "Epoch 1/30\n",
      "50000/50000 [==============================] - 4s 85us/step - loss: 2.2977 - acc: 0.1434 - val_loss: 2.2823 - val_acc: 0.1897\n",
      "Epoch 2/30\n",
      "50000/50000 [==============================] - 1s 30us/step - loss: 2.2731 - acc: 0.2025 - val_loss: 2.2625 - val_acc: 0.2174\n",
      "Epoch 3/30\n",
      "50000/50000 [==============================] - 1s 29us/step - loss: 2.2520 - acc: 0.2323 - val_loss: 2.2396 - val_acc: 0.2420\n",
      "Epoch 4/30\n",
      "50000/50000 [==============================] - 1s 29us/step - loss: 2.2259 - acc: 0.2490 - val_loss: 2.2111 - val_acc: 0.2650\n",
      "Epoch 5/30\n",
      "50000/50000 [==============================] - 1s 29us/step - loss: 2.1940 - acc: 0.2626 - val_loss: 2.1765 - val_acc: 0.2698\n",
      "Epoch 6/30\n",
      "50000/50000 [==============================] - 1s 28us/step - loss: 2.1575 - acc: 0.2688 - val_loss: 2.1393 - val_acc: 0.2762\n",
      "Epoch 7/30\n",
      "50000/50000 [==============================] - 1s 28us/step - loss: 2.1207 - acc: 0.2751 - val_loss: 2.1037 - val_acc: 0.2716\n",
      "Epoch 8/30\n",
      "50000/50000 [==============================] - 1s 28us/step - loss: 2.0869 - acc: 0.2803 - val_loss: 2.0723 - val_acc: 0.2858\n",
      "Epoch 9/30\n",
      "50000/50000 [==============================] - 1s 29us/step - loss: 2.0577 - acc: 0.2879 - val_loss: 2.0454 - val_acc: 0.2865\n",
      "Epoch 10/30\n",
      "50000/50000 [==============================] - 1s 28us/step - loss: 2.0327 - acc: 0.2904 - val_loss: 2.0231 - val_acc: 0.2861\n",
      "Epoch 11/30\n",
      "50000/50000 [==============================] - 1s 28us/step - loss: 2.0114 - acc: 0.2952 - val_loss: 2.0026 - val_acc: 0.3020\n",
      "Epoch 12/30\n",
      "50000/50000 [==============================] - 1s 28us/step - loss: 1.9929 - acc: 0.2994 - val_loss: 1.9854 - val_acc: 0.3008\n",
      "Epoch 13/30\n",
      "50000/50000 [==============================] - 1s 28us/step - loss: 1.9765 - acc: 0.3036 - val_loss: 1.9702 - val_acc: 0.3028\n",
      "Epoch 14/30\n",
      "50000/50000 [==============================] - 1s 28us/step - loss: 1.9621 - acc: 0.3069 - val_loss: 1.9568 - val_acc: 0.3047\n",
      "Epoch 15/30\n",
      "50000/50000 [==============================] - 1s 28us/step - loss: 1.9491 - acc: 0.3100 - val_loss: 1.9444 - val_acc: 0.3057\n",
      "Epoch 16/30\n",
      "50000/50000 [==============================] - 1s 28us/step - loss: 1.9375 - acc: 0.3147 - val_loss: 1.9331 - val_acc: 0.3147\n",
      "Epoch 17/30\n",
      "50000/50000 [==============================] - 1s 29us/step - loss: 1.9271 - acc: 0.3171 - val_loss: 1.9245 - val_acc: 0.3184\n",
      "Epoch 18/30\n",
      "50000/50000 [==============================] - 1s 29us/step - loss: 1.9178 - acc: 0.3201 - val_loss: 1.9155 - val_acc: 0.3193\n",
      "Epoch 19/30\n",
      "50000/50000 [==============================] - 1s 29us/step - loss: 1.9095 - acc: 0.3242 - val_loss: 1.9077 - val_acc: 0.3172\n",
      "Epoch 20/30\n",
      "50000/50000 [==============================] - 1s 30us/step - loss: 1.9019 - acc: 0.3250 - val_loss: 1.8996 - val_acc: 0.3274\n",
      "Epoch 21/30\n",
      "50000/50000 [==============================] - 1s 28us/step - loss: 1.8947 - acc: 0.3292 - val_loss: 1.8924 - val_acc: 0.3288\n",
      "Epoch 22/30\n",
      "50000/50000 [==============================] - 1s 28us/step - loss: 1.8879 - acc: 0.3321 - val_loss: 1.8861 - val_acc: 0.3298\n",
      "Epoch 23/30\n",
      "50000/50000 [==============================] - 1s 28us/step - loss: 1.8816 - acc: 0.3341 - val_loss: 1.8813 - val_acc: 0.3282\n",
      "Epoch 24/30\n",
      "50000/50000 [==============================] - 1s 29us/step - loss: 1.8756 - acc: 0.3367 - val_loss: 1.8754 - val_acc: 0.3364\n",
      "Epoch 25/30\n",
      "50000/50000 [==============================] - 1s 28us/step - loss: 1.8698 - acc: 0.3385 - val_loss: 1.8689 - val_acc: 0.3371\n",
      "Epoch 26/30\n",
      "50000/50000 [==============================] - 1s 29us/step - loss: 1.8639 - acc: 0.3420 - val_loss: 1.8633 - val_acc: 0.3353\n",
      "Epoch 27/30\n",
      "50000/50000 [==============================] - 1s 29us/step - loss: 1.8581 - acc: 0.3435 - val_loss: 1.8568 - val_acc: 0.3443\n",
      "Epoch 28/30\n",
      "50000/50000 [==============================] - 1s 28us/step - loss: 1.8528 - acc: 0.3453 - val_loss: 1.8517 - val_acc: 0.3444\n",
      "Epoch 29/30\n",
      "50000/50000 [==============================] - 1s 29us/step - loss: 1.8472 - acc: 0.3473 - val_loss: 1.8461 - val_acc: 0.3490\n",
      "Epoch 30/30\n",
      "50000/50000 [==============================] - 1s 29us/step - loss: 1.8419 - acc: 0.3484 - val_loss: 1.8402 - val_acc: 0.3497\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f2af7e04860>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer='sgd',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "model.fit(x_train, y_train,\n",
    "          batch_size=128,\n",
    "          epochs=30,\n",
    "          validation_data=(x_test, y_test))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we can evaluate the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss: 1.8402026081085205\n",
      "Test accuracy: 0.3497\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": [
    "Not very good! With more training time, perhaps 100 epochs, we might get 40% accuracy.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now onto convolutional networks... The general architecture of a convolutional neural network is:\n",
    "\n",
    "- convolution layers, followed by pooling layers\n",
    "- fully-connected layers\n",
    "- a final fully-connected softmax layer\n",
    "\n",
    "We'll follow this same basic structure and interweave some other components, such as [dropout](https://en.wikipedia.org/wiki/Dropout_(neural_networks), to improve performance.\n",
    "\n",
    "To begin, we start with our convolution layers. We first need to specify some architectural hyperparemeters:\n",
    "\n",
    "- _How many filters do we want for our convolution layers?_ Like most hyperparameters, this is chosen through a mix of intuition and tuning. A rough rule of thumb is: the more complex the task, the more filters. (Note that we don't need to have the same number of filters for each convolution layer, but we are doing so here for convenience.)\n",
    "- _What size should our convolution filters be?_ We don't want filters to be too large or the resulting matrix might not be very meaningful. For instance, a useless filter size in this task would be a 28x28 filter since it covers the whole image. We also don't want filters to be too small for a similar reason, e.g. a 1x1 filter just returns each pixel.\n",
    "- _What size should our pooling window be?_ Again, we don't want pooling windows to be too large or we'll be throwing away information. However, for larger images, a larger pooling window might be appropriate (same goes for convolution filters).\n",
    "\n",
    "We start by designing a neural network with two alternating convolutional and max-pooling layers, followed by a 100-neuron fully-connected layer and a 10-neuron output. We'll have 64 and 32 filters in the two convolutional layers, and make the input shape a full-sized image (32x32x3) instead of an unrolled vector (3072x1).  We also now use ReLU activation units instead of sigmoids, to avoid vanishing gradients."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 32, 32, 64)        1792      \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 32, 32, 64)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 16, 16, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 16, 16, 32)        18464     \n",
      "_________________________________________________________________\n",
      "activation_2 (Activation)    (None, 16, 16, 32)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 8, 8, 32)          0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 2048)              0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 100)               204900    \n",
      "_________________________________________________________________\n",
      "activation_3 (Activation)    (None, 100)               0         \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 10)                1010      \n",
      "_________________________________________________________________\n",
      "activation_4 (Activation)    (None, 10)                0         \n",
      "=================================================================\n",
      "Total params: 226,166\n",
      "Trainable params: 226,166\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(Conv2D(64, (3, 3), padding='same', input_shape=(32,32,3)))\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Conv2D(32, (3, 3), padding='same'))\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(100))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dense(num_classes))\n",
    "model.add(Activation('softmax'))\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to reload the CIFAR-10 dataset and this time do *not* reshape them into unrolled input vectors -- let them stay as images, although continue to normalize them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50000 train samples, 10000 test samples\n",
      "training data shape:  (50000, 32, 32, 3) (50000, 10)\n",
      "test data shape:  (10000, 32, 32, 3) (10000, 10)\n"
     ]
    }
   ],
   "source": [
    "from keras.datasets import cifar10\n",
    "\n",
    "# load CIFAR\n",
    "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
    "num_classes = 10\n",
    "\n",
    "# do not reshape CIFAR if you have a convolutional input!\n",
    "\n",
    "# make float32\n",
    "x_train = x_train.astype('float32')\n",
    "x_test = x_test.astype('float32')\n",
    "\n",
    "# normalize to (0-1)\n",
    "x_train /= 255\n",
    "x_test /= 255\n",
    "\n",
    "# convert class vectors to binary class matrices\n",
    "y_train = keras.utils.to_categorical(y_train, num_classes)\n",
    "y_test = keras.utils.to_categorical(y_test, num_classes)\n",
    "\n",
    "print('%d train samples, %d test samples'%(x_train.shape[0], x_test.shape[0]))\n",
    "print(\"training data shape: \", x_train.shape, y_train.shape)\n",
    "print(\"test data shape: \", x_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's compile the model and test it again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 50000 samples, validate on 10000 samples\n",
      "Epoch 1/30\n",
      "50000/50000 [==============================] - 6s 121us/step - loss: 2.1950 - acc: 0.1982 - val_loss: 2.0243 - val_acc: 0.2768\n",
      "Epoch 2/30\n",
      "50000/50000 [==============================] - 4s 76us/step - loss: 1.9498 - acc: 0.3072 - val_loss: 1.9031 - val_acc: 0.3381\n",
      "Epoch 3/30\n",
      "50000/50000 [==============================] - 4s 76us/step - loss: 1.8160 - acc: 0.3621 - val_loss: 1.7428 - val_acc: 0.3857\n",
      "Epoch 4/30\n",
      "50000/50000 [==============================] - 4s 75us/step - loss: 1.6909 - acc: 0.4049 - val_loss: 1.6034 - val_acc: 0.4345\n",
      "Epoch 5/30\n",
      "50000/50000 [==============================] - 4s 75us/step - loss: 1.6039 - acc: 0.4325 - val_loss: 1.5351 - val_acc: 0.4469\n",
      "Epoch 6/30\n",
      "50000/50000 [==============================] - 4s 76us/step - loss: 1.5284 - acc: 0.4569 - val_loss: 1.4748 - val_acc: 0.4711\n",
      "Epoch 7/30\n",
      "50000/50000 [==============================] - 4s 76us/step - loss: 1.4794 - acc: 0.4739 - val_loss: 1.4417 - val_acc: 0.4878\n",
      "Epoch 8/30\n",
      "50000/50000 [==============================] - 4s 76us/step - loss: 1.4300 - acc: 0.4891 - val_loss: 1.4253 - val_acc: 0.4888\n",
      "Epoch 9/30\n",
      "50000/50000 [==============================] - 4s 75us/step - loss: 1.3947 - acc: 0.5048 - val_loss: 1.4039 - val_acc: 0.4968\n",
      "Epoch 10/30\n",
      "50000/50000 [==============================] - 4s 76us/step - loss: 1.3616 - acc: 0.5176 - val_loss: 1.3843 - val_acc: 0.5038\n",
      "Epoch 11/30\n",
      "50000/50000 [==============================] - 4s 75us/step - loss: 1.3283 - acc: 0.5292 - val_loss: 1.3235 - val_acc: 0.5276\n",
      "Epoch 12/30\n",
      "50000/50000 [==============================] - 4s 75us/step - loss: 1.3000 - acc: 0.5399 - val_loss: 1.2925 - val_acc: 0.5408\n",
      "Epoch 13/30\n",
      "50000/50000 [==============================] - 4s 75us/step - loss: 1.2742 - acc: 0.5503 - val_loss: 1.2821 - val_acc: 0.5406\n",
      "Epoch 14/30\n",
      "50000/50000 [==============================] - 4s 75us/step - loss: 1.2468 - acc: 0.5611 - val_loss: 1.2522 - val_acc: 0.5507\n",
      "Epoch 15/30\n",
      "50000/50000 [==============================] - 4s 75us/step - loss: 1.2225 - acc: 0.5691 - val_loss: 1.2457 - val_acc: 0.5509\n",
      "Epoch 16/30\n",
      "50000/50000 [==============================] - 4s 76us/step - loss: 1.1988 - acc: 0.5774 - val_loss: 1.2455 - val_acc: 0.5593\n",
      "Epoch 17/30\n",
      "50000/50000 [==============================] - 4s 76us/step - loss: 1.1782 - acc: 0.5873 - val_loss: 1.1993 - val_acc: 0.5719\n",
      "Epoch 18/30\n",
      "50000/50000 [==============================] - 4s 76us/step - loss: 1.1599 - acc: 0.5933 - val_loss: 1.2084 - val_acc: 0.5674\n",
      "Epoch 19/30\n",
      "50000/50000 [==============================] - 4s 77us/step - loss: 1.1394 - acc: 0.6006 - val_loss: 1.2101 - val_acc: 0.5722\n",
      "Epoch 20/30\n",
      "50000/50000 [==============================] - 4s 76us/step - loss: 1.1241 - acc: 0.6070 - val_loss: 1.2216 - val_acc: 0.5784\n",
      "Epoch 21/30\n",
      "50000/50000 [==============================] - 4s 76us/step - loss: 1.1044 - acc: 0.6133 - val_loss: 1.1923 - val_acc: 0.5782\n",
      "Epoch 22/30\n",
      "50000/50000 [==============================] - 4s 76us/step - loss: 1.0889 - acc: 0.6185 - val_loss: 1.1599 - val_acc: 0.5865\n",
      "Epoch 23/30\n",
      "50000/50000 [==============================] - 4s 75us/step - loss: 1.0701 - acc: 0.6245 - val_loss: 1.1081 - val_acc: 0.6020\n",
      "Epoch 24/30\n",
      "50000/50000 [==============================] - 4s 76us/step - loss: 1.0533 - acc: 0.6325 - val_loss: 1.0949 - val_acc: 0.6094\n",
      "Epoch 25/30\n",
      "50000/50000 [==============================] - 4s 76us/step - loss: 1.0379 - acc: 0.6382 - val_loss: 1.0887 - val_acc: 0.6147\n",
      "Epoch 26/30\n",
      "50000/50000 [==============================] - 4s 76us/step - loss: 1.0258 - acc: 0.6417 - val_loss: 1.1304 - val_acc: 0.5994\n",
      "Epoch 27/30\n",
      "50000/50000 [==============================] - 4s 76us/step - loss: 1.0096 - acc: 0.6471 - val_loss: 1.0664 - val_acc: 0.6275\n",
      "Epoch 28/30\n",
      "50000/50000 [==============================] - 4s 76us/step - loss: 0.9945 - acc: 0.6532 - val_loss: 1.0837 - val_acc: 0.6152\n",
      "Epoch 29/30\n",
      "50000/50000 [==============================] - 4s 76us/step - loss: 0.9819 - acc: 0.6577 - val_loss: 1.0721 - val_acc: 0.6184\n",
      "Epoch 30/30\n",
      "50000/50000 [==============================] - 4s 76us/step - loss: 0.9696 - acc: 0.6640 - val_loss: 1.0799 - val_acc: 0.6180\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f2af7d21518>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer='sgd',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "model.fit(x_train, y_train,\n",
    "          batch_size=128,\n",
    "          epochs=30,\n",
    "          validation_data=(x_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's evaluate the model again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss: 1.0451206232070922\n",
      "Test accuracy: 0.6323\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": [
    "63% accuracy is a big improvement on 40%! All of that is accomplished in just 30 epochs using convolutional layers and ReLUs. \n",
    "\n",
    "Let's try to make the network bigger."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_40 (Conv2D)           (None, 32, 32, 128)       3584      \n",
      "_________________________________________________________________\n",
      "activation_71 (Activation)   (None, 32, 32, 128)       0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_32 (MaxPooling (None, 16, 16, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_41 (Conv2D)           (None, 16, 16, 64)        73792     \n",
      "_________________________________________________________________\n",
      "activation_72 (Activation)   (None, 16, 16, 64)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_33 (MaxPooling (None, 8, 8, 64)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_42 (Conv2D)           (None, 8, 8, 64)          36928     \n",
      "_________________________________________________________________\n",
      "activation_73 (Activation)   (None, 8, 8, 64)          0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_34 (MaxPooling (None, 4, 4, 64)          0         \n",
      "_________________________________________________________________\n",
      "flatten_13 (Flatten)         (None, 1024)              0         \n",
      "_________________________________________________________________\n",
      "dense_38 (Dense)             (None, 256)               262400    \n",
      "_________________________________________________________________\n",
      "activation_74 (Activation)   (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense_39 (Dense)             (None, 100)               25700     \n",
      "_________________________________________________________________\n",
      "activation_75 (Activation)   (None, 100)               0         \n",
      "_________________________________________________________________\n",
      "dense_40 (Dense)             (None, 10)                1010      \n",
      "_________________________________________________________________\n",
      "activation_76 (Activation)   (None, 10)                0         \n",
      "=================================================================\n",
      "Total params: 403,414\n",
      "Trainable params: 403,414\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(Conv2D(128, (3, 3), padding='same', input_shape=(32,32,3)))\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Conv2D(64, (3, 3), padding='same'))\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Conv2D(64, (3, 3), padding='same'))\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(256))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dense(100))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dense(num_classes))\n",
    "model.add(Activation('softmax'))\n",
    "model.summary()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compile and train again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_3 (Conv2D)            (None, 32, 32, 128)       3584      \n",
      "_________________________________________________________________\n",
      "activation_5 (Activation)    (None, 32, 32, 128)       0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 16, 16, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 16, 16, 64)        73792     \n",
      "_________________________________________________________________\n",
      "activation_6 (Activation)    (None, 16, 16, 64)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2 (None, 8, 8, 64)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 8, 8, 64)          36928     \n",
      "_________________________________________________________________\n",
      "activation_7 (Activation)    (None, 8, 8, 64)          0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_5 (MaxPooling2 (None, 4, 4, 64)          0         \n",
      "_________________________________________________________________\n",
      "flatten_2 (Flatten)          (None, 1024)              0         \n",
      "_________________________________________________________________\n",
      "dense_6 (Dense)              (None, 256)               262400    \n",
      "_________________________________________________________________\n",
      "activation_8 (Activation)    (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense_7 (Dense)              (None, 100)               25700     \n",
      "_________________________________________________________________\n",
      "activation_9 (Activation)    (None, 100)               0         \n",
      "_________________________________________________________________\n",
      "dense_8 (Dense)              (None, 10)                1010      \n",
      "_________________________________________________________________\n",
      "activation_10 (Activation)   (None, 10)                0         \n",
      "=================================================================\n",
      "Total params: 403,414\n",
      "Trainable params: 403,414\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Train on 50000 samples, validate on 10000 samples\n",
      "Epoch 1/50\n",
      "50000/50000 [==============================] - 6s 123us/step - loss: 2.2554 - acc: 0.1903 - val_loss: 2.1562 - val_acc: 0.1933\n",
      "Epoch 2/50\n",
      "50000/50000 [==============================] - 6s 116us/step - loss: 2.0300 - acc: 0.2712 - val_loss: 1.9791 - val_acc: 0.2843\n",
      "Epoch 3/50\n",
      "50000/50000 [==============================] - 6s 115us/step - loss: 1.9109 - acc: 0.3177 - val_loss: 1.8798 - val_acc: 0.3334\n",
      "Epoch 4/50\n",
      "50000/50000 [==============================] - 6s 117us/step - loss: 1.8111 - acc: 0.3543 - val_loss: 1.7440 - val_acc: 0.3758\n",
      "Epoch 5/50\n",
      "50000/50000 [==============================] - 6s 116us/step - loss: 1.7230 - acc: 0.3867 - val_loss: 1.6357 - val_acc: 0.4203\n",
      "Epoch 6/50\n",
      "50000/50000 [==============================] - 6s 115us/step - loss: 1.6345 - acc: 0.4163 - val_loss: 1.5607 - val_acc: 0.4485\n",
      "Epoch 7/50\n",
      "50000/50000 [==============================] - 6s 115us/step - loss: 1.5600 - acc: 0.4441 - val_loss: 1.5302 - val_acc: 0.4474\n",
      "Epoch 8/50\n",
      "50000/50000 [==============================] - 6s 116us/step - loss: 1.5005 - acc: 0.4640 - val_loss: 1.4898 - val_acc: 0.4716\n",
      "Epoch 9/50\n",
      "50000/50000 [==============================] - 6s 116us/step - loss: 1.4564 - acc: 0.4795 - val_loss: 1.4031 - val_acc: 0.4952\n",
      "Epoch 10/50\n",
      "50000/50000 [==============================] - 6s 115us/step - loss: 1.4113 - acc: 0.4965 - val_loss: 1.4292 - val_acc: 0.4817\n",
      "Epoch 11/50\n",
      "50000/50000 [==============================] - 6s 115us/step - loss: 1.3784 - acc: 0.5100 - val_loss: 1.3621 - val_acc: 0.5163\n",
      "Epoch 12/50\n",
      "50000/50000 [==============================] - 6s 115us/step - loss: 1.3484 - acc: 0.5213 - val_loss: 1.3342 - val_acc: 0.5182\n",
      "Epoch 13/50\n",
      "50000/50000 [==============================] - 6s 116us/step - loss: 1.3126 - acc: 0.5329 - val_loss: 1.2919 - val_acc: 0.5342\n",
      "Epoch 14/50\n",
      "50000/50000 [==============================] - 6s 115us/step - loss: 1.2877 - acc: 0.5430 - val_loss: 1.2874 - val_acc: 0.5312\n",
      "Epoch 15/50\n",
      "50000/50000 [==============================] - 6s 115us/step - loss: 1.2602 - acc: 0.5536 - val_loss: 1.4420 - val_acc: 0.4882\n",
      "Epoch 16/50\n",
      "50000/50000 [==============================] - 6s 115us/step - loss: 1.2314 - acc: 0.5638 - val_loss: 1.2218 - val_acc: 0.5669\n",
      "Epoch 17/50\n",
      "50000/50000 [==============================] - 6s 115us/step - loss: 1.2049 - acc: 0.5741 - val_loss: 1.2472 - val_acc: 0.5532\n",
      "Epoch 18/50\n",
      "50000/50000 [==============================] - 6s 115us/step - loss: 1.1755 - acc: 0.5865 - val_loss: 1.2906 - val_acc: 0.5367\n",
      "Epoch 19/50\n",
      "50000/50000 [==============================] - 6s 115us/step - loss: 1.1512 - acc: 0.5935 - val_loss: 1.1664 - val_acc: 0.5809\n",
      "Epoch 20/50\n",
      "50000/50000 [==============================] - 6s 115us/step - loss: 1.1242 - acc: 0.6059 - val_loss: 1.1497 - val_acc: 0.5885\n",
      "Epoch 21/50\n",
      "50000/50000 [==============================] - 6s 116us/step - loss: 1.1013 - acc: 0.6122 - val_loss: 1.1715 - val_acc: 0.5828\n",
      "Epoch 22/50\n",
      "50000/50000 [==============================] - 6s 115us/step - loss: 1.0763 - acc: 0.6217 - val_loss: 1.1545 - val_acc: 0.5913\n",
      "Epoch 23/50\n",
      "50000/50000 [==============================] - 6s 115us/step - loss: 1.0535 - acc: 0.6298 - val_loss: 1.0809 - val_acc: 0.6199\n",
      "Epoch 24/50\n",
      "50000/50000 [==============================] - 6s 116us/step - loss: 1.0325 - acc: 0.6393 - val_loss: 1.0858 - val_acc: 0.6134\n",
      "Epoch 25/50\n",
      "50000/50000 [==============================] - 6s 115us/step - loss: 1.0088 - acc: 0.6466 - val_loss: 1.0516 - val_acc: 0.6306\n",
      "Epoch 26/50\n",
      "50000/50000 [==============================] - 6s 115us/step - loss: 0.9883 - acc: 0.6542 - val_loss: 1.1746 - val_acc: 0.5982\n",
      "Epoch 27/50\n",
      "50000/50000 [==============================] - 6s 116us/step - loss: 0.9696 - acc: 0.6604 - val_loss: 1.0660 - val_acc: 0.6232\n",
      "Epoch 28/50\n",
      "50000/50000 [==============================] - 6s 115us/step - loss: 0.9477 - acc: 0.6709 - val_loss: 1.0299 - val_acc: 0.6393\n",
      "Epoch 29/50\n",
      "50000/50000 [==============================] - 6s 115us/step - loss: 0.9311 - acc: 0.6763 - val_loss: 1.0138 - val_acc: 0.6466\n",
      "Epoch 30/50\n",
      "50000/50000 [==============================] - 6s 115us/step - loss: 0.9144 - acc: 0.6808 - val_loss: 1.0106 - val_acc: 0.6470\n",
      "Epoch 31/50\n",
      "50000/50000 [==============================] - 6s 116us/step - loss: 0.8915 - acc: 0.6909 - val_loss: 1.0042 - val_acc: 0.6468\n",
      "Epoch 32/50\n",
      "50000/50000 [==============================] - 6s 115us/step - loss: 0.8750 - acc: 0.6943 - val_loss: 0.9766 - val_acc: 0.6586\n",
      "Epoch 33/50\n",
      "50000/50000 [==============================] - 6s 115us/step - loss: 0.8590 - acc: 0.7004 - val_loss: 0.9977 - val_acc: 0.6551\n",
      "Epoch 34/50\n",
      "50000/50000 [==============================] - 6s 115us/step - loss: 0.8440 - acc: 0.7083 - val_loss: 1.0536 - val_acc: 0.6438\n",
      "Epoch 35/50\n",
      "50000/50000 [==============================] - 6s 116us/step - loss: 0.8274 - acc: 0.7140 - val_loss: 1.1110 - val_acc: 0.6119\n",
      "Epoch 36/50\n",
      "50000/50000 [==============================] - 6s 116us/step - loss: 0.8134 - acc: 0.7173 - val_loss: 1.0435 - val_acc: 0.6477\n",
      "Epoch 37/50\n",
      "50000/50000 [==============================] - 6s 115us/step - loss: 0.7967 - acc: 0.7250 - val_loss: 1.0294 - val_acc: 0.6473\n",
      "Epoch 38/50\n",
      "50000/50000 [==============================] - 6s 115us/step - loss: 0.7831 - acc: 0.7286 - val_loss: 0.9457 - val_acc: 0.6736\n",
      "Epoch 39/50\n",
      "50000/50000 [==============================] - 6s 115us/step - loss: 0.7660 - acc: 0.7358 - val_loss: 0.9650 - val_acc: 0.6648\n",
      "Epoch 40/50\n",
      "50000/50000 [==============================] - 6s 116us/step - loss: 0.7551 - acc: 0.7392 - val_loss: 0.9824 - val_acc: 0.6653\n",
      "Epoch 41/50\n",
      "50000/50000 [==============================] - 6s 115us/step - loss: 0.7395 - acc: 0.7446 - val_loss: 0.9714 - val_acc: 0.6706\n",
      "Epoch 42/50\n",
      "50000/50000 [==============================] - 6s 115us/step - loss: 0.7264 - acc: 0.7486 - val_loss: 0.9794 - val_acc: 0.6655\n",
      "Epoch 43/50\n",
      "50000/50000 [==============================] - 6s 116us/step - loss: 0.7106 - acc: 0.7546 - val_loss: 1.0379 - val_acc: 0.6566\n",
      "Epoch 44/50\n",
      "50000/50000 [==============================] - 6s 116us/step - loss: 0.6938 - acc: 0.7597 - val_loss: 0.9049 - val_acc: 0.6903\n",
      "Epoch 45/50\n",
      "50000/50000 [==============================] - 6s 116us/step - loss: 0.6851 - acc: 0.7643 - val_loss: 0.9483 - val_acc: 0.6785\n",
      "Epoch 46/50\n",
      "50000/50000 [==============================] - 6s 116us/step - loss: 0.6696 - acc: 0.7692 - val_loss: 0.9494 - val_acc: 0.6856\n",
      "Epoch 47/50\n",
      "50000/50000 [==============================] - 6s 116us/step - loss: 0.6566 - acc: 0.7735 - val_loss: 0.9291 - val_acc: 0.6855\n",
      "Epoch 48/50\n",
      "50000/50000 [==============================] - 6s 115us/step - loss: 0.6446 - acc: 0.7762 - val_loss: 0.9010 - val_acc: 0.6947\n",
      "Epoch 49/50\n",
      "50000/50000 [==============================] - 6s 116us/step - loss: 0.6300 - acc: 0.7811 - val_loss: 0.9371 - val_acc: 0.6854\n",
      "Epoch 50/50\n",
      "50000/50000 [==============================] - 6s 115us/step - loss: 0.6197 - acc: 0.7854 - val_loss: 0.9821 - val_acc: 0.6781\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f2ae1f23160>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer='sgd',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "model.fit(x_train, y_train,\n",
    "          batch_size=128,\n",
    "          epochs=50,\n",
    "          validation_data=(x_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluate test accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss: 0.9820661101341247\n",
      "Test accuracy: 0.6781\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": [
    "One problem you might notice is that the accuracy of the model is much better on the training set than on the test set. You can see that by monitoring the progress at the end of each epoch above or by evaluating it directly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training loss: 0.647886616191864\n",
      "Training accuracy: 0.7712\n"
     ]
    }
   ],
   "source": [
    "score = model.evaluate(x_train, y_train, verbose=0)\n",
    "print('Training loss:', score[0])\n",
    "print('Training accuracy:', score[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "77% accuracy on the training set but only 68% on the test set. Looking at the monitored training, the validation accuracy and training accuracy began to diverge around epoch 10.\n",
    "\n",
    "Something must be wrong!  This is a symptom of \"overfitting\".  Our model has probably tried to bend itself a little too well towards predicting the training set but does not generalize very well to unseen data. This is a very common problem. \n",
    "\n",
    "It's normal for the training accuracy to be better than the testng accuracy to some degree, because it's hard to avoid for the network to be better at predicting the data it sees. But a 9% difference is too much.\n",
    "\n",
    "One way of helping this is by doing some regularization. We can add dropout to our model after a few layers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_21 (Conv2D)           (None, 32, 32, 128)       3584      \n",
      "_________________________________________________________________\n",
      "activation_41 (Activation)   (None, 32, 32, 128)       0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_21 (MaxPooling (None, 16, 16, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_22 (Conv2D)           (None, 16, 16, 64)        73792     \n",
      "_________________________________________________________________\n",
      "activation_42 (Activation)   (None, 16, 16, 64)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_22 (MaxPooling (None, 8, 8, 64)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_23 (Conv2D)           (None, 8, 8, 64)          36928     \n",
      "_________________________________________________________________\n",
      "activation_43 (Activation)   (None, 8, 8, 64)          0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_23 (MaxPooling (None, 4, 4, 64)          0         \n",
      "_________________________________________________________________\n",
      "dropout_13 (Dropout)         (None, 4, 4, 64)          0         \n",
      "_________________________________________________________________\n",
      "flatten_8 (Flatten)          (None, 1024)              0         \n",
      "_________________________________________________________________\n",
      "dense_24 (Dense)             (None, 256)               262400    \n",
      "_________________________________________________________________\n",
      "activation_44 (Activation)   (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense_25 (Dense)             (None, 100)               25700     \n",
      "_________________________________________________________________\n",
      "activation_45 (Activation)   (None, 100)               0         \n",
      "_________________________________________________________________\n",
      "dropout_14 (Dropout)         (None, 100)               0         \n",
      "_________________________________________________________________\n",
      "dense_26 (Dense)             (None, 10)                1010      \n",
      "_________________________________________________________________\n",
      "activation_46 (Activation)   (None, 10)                0         \n",
      "=================================================================\n",
      "Total params: 403,414\n",
      "Trainable params: 403,414\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(Conv2D(128, (3, 3), padding='same', input_shape=(32,32,3)))\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Conv2D(64, (3, 3), padding='same'))\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Conv2D(64, (3, 3), padding='same'))\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Dropout(0.25))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(256))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dense(100))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dropout(0.25))\n",
    "model.add(Dense(num_classes))\n",
    "model.add(Activation('softmax'))\n",
    "model.summary()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We compile and train again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 50000 samples, validate on 10000 samples\n",
      "Epoch 1/50\n",
      "50000/50000 [==============================] - 7s 132us/step - loss: 2.2915 - acc: 0.1272 - val_loss: 2.2643 - val_acc: 0.1780\n",
      "Epoch 2/50\n",
      "50000/50000 [==============================] - 6s 122us/step - loss: 2.1875 - acc: 0.1797 - val_loss: 2.0443 - val_acc: 0.2662\n",
      "Epoch 3/50\n",
      "50000/50000 [==============================] - 6s 121us/step - loss: 2.0504 - acc: 0.2390 - val_loss: 1.9592 - val_acc: 0.2933\n",
      "Epoch 4/50\n",
      "50000/50000 [==============================] - 6s 122us/step - loss: 1.9522 - acc: 0.2873 - val_loss: 1.8107 - val_acc: 0.3590\n",
      "Epoch 5/50\n",
      "50000/50000 [==============================] - 6s 122us/step - loss: 1.8516 - acc: 0.3258 - val_loss: 1.7262 - val_acc: 0.3914\n",
      "Epoch 6/50\n",
      "50000/50000 [==============================] - 6s 122us/step - loss: 1.7655 - acc: 0.3619 - val_loss: 1.6722 - val_acc: 0.4035\n",
      "Epoch 7/50\n",
      "50000/50000 [==============================] - 6s 122us/step - loss: 1.6940 - acc: 0.3835 - val_loss: 1.5555 - val_acc: 0.4370\n",
      "Epoch 8/50\n",
      "50000/50000 [==============================] - 6s 122us/step - loss: 1.6410 - acc: 0.4016 - val_loss: 1.5345 - val_acc: 0.4442\n",
      "Epoch 9/50\n",
      "50000/50000 [==============================] - 6s 122us/step - loss: 1.6012 - acc: 0.4150 - val_loss: 1.5140 - val_acc: 0.4560\n",
      "Epoch 10/50\n",
      "50000/50000 [==============================] - 6s 122us/step - loss: 1.5629 - acc: 0.4290 - val_loss: 1.4396 - val_acc: 0.4790\n",
      "Epoch 11/50\n",
      "50000/50000 [==============================] - 6s 123us/step - loss: 1.5281 - acc: 0.4436 - val_loss: 1.4515 - val_acc: 0.4725\n",
      "Epoch 12/50\n",
      "50000/50000 [==============================] - 6s 122us/step - loss: 1.5052 - acc: 0.4528 - val_loss: 1.3930 - val_acc: 0.4940\n",
      "Epoch 13/50\n",
      "50000/50000 [==============================] - 6s 122us/step - loss: 1.4687 - acc: 0.4650 - val_loss: 1.3571 - val_acc: 0.5054\n",
      "Epoch 14/50\n",
      "50000/50000 [==============================] - 6s 122us/step - loss: 1.4461 - acc: 0.4735 - val_loss: 1.3391 - val_acc: 0.5132\n",
      "Epoch 15/50\n",
      "50000/50000 [==============================] - 6s 123us/step - loss: 1.4252 - acc: 0.4852 - val_loss: 1.3287 - val_acc: 0.5178\n",
      "Epoch 16/50\n",
      "50000/50000 [==============================] - 6s 122us/step - loss: 1.4015 - acc: 0.4925 - val_loss: 1.3226 - val_acc: 0.5173\n",
      "Epoch 17/50\n",
      "50000/50000 [==============================] - 6s 123us/step - loss: 1.3816 - acc: 0.5021 - val_loss: 1.3070 - val_acc: 0.5310\n",
      "Epoch 18/50\n",
      "50000/50000 [==============================] - 6s 122us/step - loss: 1.3587 - acc: 0.5107 - val_loss: 1.3062 - val_acc: 0.5306\n",
      "Epoch 19/50\n",
      "50000/50000 [==============================] - 6s 122us/step - loss: 1.3396 - acc: 0.5159 - val_loss: 1.2485 - val_acc: 0.5544\n",
      "Epoch 20/50\n",
      "50000/50000 [==============================] - 6s 122us/step - loss: 1.3177 - acc: 0.5257 - val_loss: 1.2497 - val_acc: 0.5558\n",
      "Epoch 21/50\n",
      "50000/50000 [==============================] - 6s 121us/step - loss: 1.2973 - acc: 0.5338 - val_loss: 1.2119 - val_acc: 0.5656\n",
      "Epoch 22/50\n",
      "50000/50000 [==============================] - 6s 122us/step - loss: 1.2770 - acc: 0.5419 - val_loss: 1.1726 - val_acc: 0.5828\n",
      "Epoch 23/50\n",
      "50000/50000 [==============================] - 6s 121us/step - loss: 1.2585 - acc: 0.5517 - val_loss: 1.1617 - val_acc: 0.5834\n",
      "Epoch 24/50\n",
      "50000/50000 [==============================] - 6s 122us/step - loss: 1.2417 - acc: 0.5545 - val_loss: 1.1736 - val_acc: 0.5815\n",
      "Epoch 25/50\n",
      "50000/50000 [==============================] - 6s 122us/step - loss: 1.2255 - acc: 0.5621 - val_loss: 1.1333 - val_acc: 0.5968\n",
      "Epoch 26/50\n",
      "50000/50000 [==============================] - 6s 122us/step - loss: 1.2077 - acc: 0.5681 - val_loss: 1.1415 - val_acc: 0.5894\n",
      "Epoch 27/50\n",
      "50000/50000 [==============================] - 6s 122us/step - loss: 1.1894 - acc: 0.5766 - val_loss: 1.1133 - val_acc: 0.6033\n",
      "Epoch 28/50\n",
      "50000/50000 [==============================] - 6s 122us/step - loss: 1.1788 - acc: 0.5799 - val_loss: 1.0746 - val_acc: 0.6180\n",
      "Epoch 29/50\n",
      "50000/50000 [==============================] - 6s 122us/step - loss: 1.1583 - acc: 0.5873 - val_loss: 1.0697 - val_acc: 0.6180\n",
      "Epoch 30/50\n",
      "50000/50000 [==============================] - 6s 123us/step - loss: 1.1419 - acc: 0.5944 - val_loss: 1.0474 - val_acc: 0.6314\n",
      "Epoch 31/50\n",
      "50000/50000 [==============================] - 6s 122us/step - loss: 1.1284 - acc: 0.5990 - val_loss: 1.0345 - val_acc: 0.6350\n",
      "Epoch 32/50\n",
      "50000/50000 [==============================] - 6s 122us/step - loss: 1.1087 - acc: 0.6061 - val_loss: 1.0353 - val_acc: 0.6329\n",
      "Epoch 33/50\n",
      "50000/50000 [==============================] - 6s 123us/step - loss: 1.1019 - acc: 0.6089 - val_loss: 1.0311 - val_acc: 0.6331\n",
      "Epoch 34/50\n",
      "50000/50000 [==============================] - 6s 122us/step - loss: 1.0843 - acc: 0.6150 - val_loss: 1.0180 - val_acc: 0.6389\n",
      "Epoch 35/50\n",
      "50000/50000 [==============================] - 6s 122us/step - loss: 1.0710 - acc: 0.6232 - val_loss: 0.9908 - val_acc: 0.6507\n",
      "Epoch 36/50\n",
      "50000/50000 [==============================] - 6s 122us/step - loss: 1.0588 - acc: 0.6285 - val_loss: 1.0137 - val_acc: 0.6447\n",
      "Epoch 37/50\n",
      "50000/50000 [==============================] - 6s 123us/step - loss: 1.0508 - acc: 0.6278 - val_loss: 0.9988 - val_acc: 0.6464\n",
      "Epoch 38/50\n",
      "50000/50000 [==============================] - 6s 122us/step - loss: 1.0370 - acc: 0.6327 - val_loss: 0.9680 - val_acc: 0.6547\n",
      "Epoch 39/50\n",
      "50000/50000 [==============================] - 6s 122us/step - loss: 1.0229 - acc: 0.6401 - val_loss: 0.9509 - val_acc: 0.6677\n",
      "Epoch 40/50\n",
      "50000/50000 [==============================] - 6s 122us/step - loss: 1.0151 - acc: 0.6409 - val_loss: 0.9826 - val_acc: 0.6538\n",
      "Epoch 41/50\n",
      "50000/50000 [==============================] - 6s 122us/step - loss: 1.0019 - acc: 0.6465 - val_loss: 0.9400 - val_acc: 0.6728\n",
      "Epoch 42/50\n",
      "50000/50000 [==============================] - 6s 122us/step - loss: 0.9904 - acc: 0.6495 - val_loss: 0.9605 - val_acc: 0.6589\n",
      "Epoch 43/50\n",
      "50000/50000 [==============================] - 6s 122us/step - loss: 0.9832 - acc: 0.6535 - val_loss: 0.9385 - val_acc: 0.6720\n",
      "Epoch 44/50\n",
      "50000/50000 [==============================] - 6s 122us/step - loss: 0.9728 - acc: 0.6566 - val_loss: 0.9163 - val_acc: 0.6833\n",
      "Epoch 45/50\n",
      "50000/50000 [==============================] - 6s 123us/step - loss: 0.9583 - acc: 0.6620 - val_loss: 0.8996 - val_acc: 0.6861\n",
      "Epoch 46/50\n",
      "50000/50000 [==============================] - 6s 122us/step - loss: 0.9492 - acc: 0.6668 - val_loss: 0.9058 - val_acc: 0.6861\n",
      "Epoch 47/50\n",
      "50000/50000 [==============================] - 6s 122us/step - loss: 0.9393 - acc: 0.6710 - val_loss: 0.8956 - val_acc: 0.6874\n",
      "Epoch 48/50\n",
      "50000/50000 [==============================] - 6s 122us/step - loss: 0.9304 - acc: 0.6721 - val_loss: 0.8769 - val_acc: 0.6929\n",
      "Epoch 49/50\n",
      "50000/50000 [==============================] - 6s 122us/step - loss: 0.9194 - acc: 0.6757 - val_loss: 0.8853 - val_acc: 0.6887\n",
      "Epoch 50/50\n",
      "50000/50000 [==============================] - 6s 122us/step - loss: 0.9110 - acc: 0.6796 - val_loss: 0.8791 - val_acc: 0.6929\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f285c865fd0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer='sgd',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "model.fit(x_train, y_train,\n",
    "          batch_size=128,\n",
    "          epochs=50,\n",
    "          validation_data=(x_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We check our test loss and training loss again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss: 0.8791208669662476\n",
      "Test accuracy: 0.6929\n",
      "Training loss: 0.792264692363739\n",
      "Training accuracy: 0.72292\n"
     ]
    }
   ],
   "source": [
    "score = model.evaluate(x_test, y_test, verbose=0)\n",
    "print('Test loss:', score[0])\n",
    "print('Test accuracy:', score[1])\n",
    "\n",
    "score = model.evaluate(x_train, y_train, verbose=0)\n",
    "print('Training loss:', score[0])\n",
    "print('Training accuracy:', score[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now our training accuracy is lower (72%) but our test accuracy is higher (69%).  This is more like what we expect."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss: 0.8672016343116761\n",
      "Test accuracy: 0.6991\n",
      "Training loss: 0.7848990835762024\n",
      "Training accuracy: 0.73034\n"
     ]
    }
   ],
   "source": [
    "score = model.evaluate(x_test, y_test, verbose=0)\n",
    "print('Test loss:', score[0])\n",
    "print('Test accuracy:', score[1])\n",
    "\n",
    "score = model.evaluate(x_train, y_train, verbose=0)\n",
    "print('Training loss:', score[0])\n",
    "print('Training accuracy:', score[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another way of improving performance is to experiment with different optimizers beyond just standard sgd. Let's try to instantiate the same network but use ADAM instead of sgd."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_46 (Conv2D)           (None, 32, 32, 128)       3584      \n",
      "_________________________________________________________________\n",
      "activation_83 (Activation)   (None, 32, 32, 128)       0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_38 (MaxPooling (None, 16, 16, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_47 (Conv2D)           (None, 16, 16, 64)        73792     \n",
      "_________________________________________________________________\n",
      "activation_84 (Activation)   (None, 16, 16, 64)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_39 (MaxPooling (None, 8, 8, 64)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_48 (Conv2D)           (None, 8, 8, 64)          36928     \n",
      "_________________________________________________________________\n",
      "activation_85 (Activation)   (None, 8, 8, 64)          0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_40 (MaxPooling (None, 4, 4, 64)          0         \n",
      "_________________________________________________________________\n",
      "dropout_29 (Dropout)         (None, 4, 4, 64)          0         \n",
      "_________________________________________________________________\n",
      "flatten_15 (Flatten)         (None, 1024)              0         \n",
      "_________________________________________________________________\n",
      "dense_44 (Dense)             (None, 256)               262400    \n",
      "_________________________________________________________________\n",
      "activation_86 (Activation)   (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense_45 (Dense)             (None, 100)               25700     \n",
      "_________________________________________________________________\n",
      "activation_87 (Activation)   (None, 100)               0         \n",
      "_________________________________________________________________\n",
      "dropout_30 (Dropout)         (None, 100)               0         \n",
      "_________________________________________________________________\n",
      "dense_46 (Dense)             (None, 10)                1010      \n",
      "_________________________________________________________________\n",
      "activation_88 (Activation)   (None, 10)                0         \n",
      "=================================================================\n",
      "Total params: 403,414\n",
      "Trainable params: 403,414\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(Conv2D(128, (3, 3), padding='same', input_shape=(32,32,3)))\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Conv2D(64, (3, 3), padding='same'))\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Conv2D(64, (3, 3), padding='same'))\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Dropout(0.25))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(256))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dense(100))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dropout(0.25))\n",
    "model.add(Dense(num_classes))\n",
    "model.add(Activation('softmax'))\n",
    "model.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 50000 samples, validate on 10000 samples\n",
      "Epoch 1/50\n",
      "50000/50000 [==============================] - 7s 146us/step - loss: 1.6382 - acc: 0.4002 - val_loss: 1.2972 - val_acc: 0.5378\n",
      "Epoch 2/50\n",
      "50000/50000 [==============================] - 6s 129us/step - loss: 1.2317 - acc: 0.5640 - val_loss: 1.0376 - val_acc: 0.6280\n",
      "Epoch 3/50\n",
      "50000/50000 [==============================] - 6s 130us/step - loss: 1.0625 - acc: 0.6239 - val_loss: 0.9647 - val_acc: 0.6581\n",
      "Epoch 4/50\n",
      "50000/50000 [==============================] - 6s 129us/step - loss: 0.9416 - acc: 0.6708 - val_loss: 0.8799 - val_acc: 0.6979\n",
      "Epoch 5/50\n",
      "50000/50000 [==============================] - 6s 129us/step - loss: 0.8508 - acc: 0.7044 - val_loss: 0.8396 - val_acc: 0.7045\n",
      "Epoch 6/50\n",
      "50000/50000 [==============================] - 6s 129us/step - loss: 0.7853 - acc: 0.7277 - val_loss: 0.7940 - val_acc: 0.7231\n",
      "Epoch 7/50\n",
      "50000/50000 [==============================] - 6s 129us/step - loss: 0.7307 - acc: 0.7441 - val_loss: 0.7589 - val_acc: 0.7363\n",
      "Epoch 8/50\n",
      "50000/50000 [==============================] - 6s 128us/step - loss: 0.6834 - acc: 0.7608 - val_loss: 0.7449 - val_acc: 0.7410\n",
      "Epoch 9/50\n",
      "50000/50000 [==============================] - 6s 128us/step - loss: 0.6334 - acc: 0.7797 - val_loss: 0.7348 - val_acc: 0.7457\n",
      "Epoch 10/50\n",
      "50000/50000 [==============================] - 6s 128us/step - loss: 0.6021 - acc: 0.7906 - val_loss: 0.7106 - val_acc: 0.7592\n",
      "Epoch 11/50\n",
      "50000/50000 [==============================] - 6s 128us/step - loss: 0.5622 - acc: 0.8017 - val_loss: 0.6812 - val_acc: 0.7647\n",
      "Epoch 12/50\n",
      "50000/50000 [==============================] - 6s 129us/step - loss: 0.5279 - acc: 0.8141 - val_loss: 0.6972 - val_acc: 0.7663\n",
      "Epoch 13/50\n",
      "50000/50000 [==============================] - 6s 129us/step - loss: 0.4999 - acc: 0.8249 - val_loss: 0.6749 - val_acc: 0.7737\n",
      "Epoch 14/50\n",
      "50000/50000 [==============================] - 6s 128us/step - loss: 0.4752 - acc: 0.8325 - val_loss: 0.6941 - val_acc: 0.7669\n",
      "Epoch 15/50\n",
      "50000/50000 [==============================] - 6s 129us/step - loss: 0.4453 - acc: 0.8430 - val_loss: 0.7053 - val_acc: 0.7661\n",
      "Epoch 16/50\n",
      "50000/50000 [==============================] - 6s 128us/step - loss: 0.4271 - acc: 0.8489 - val_loss: 0.6851 - val_acc: 0.7784\n",
      "Epoch 17/50\n",
      "50000/50000 [==============================] - 6s 128us/step - loss: 0.4029 - acc: 0.8571 - val_loss: 0.6793 - val_acc: 0.7789\n",
      "Epoch 18/50\n",
      "50000/50000 [==============================] - 6s 129us/step - loss: 0.3794 - acc: 0.8664 - val_loss: 0.7009 - val_acc: 0.7728\n",
      "Epoch 19/50\n",
      "50000/50000 [==============================] - 6s 128us/step - loss: 0.3659 - acc: 0.8702 - val_loss: 0.7238 - val_acc: 0.7756\n",
      "Epoch 20/50\n",
      "50000/50000 [==============================] - 6s 129us/step - loss: 0.3431 - acc: 0.8768 - val_loss: 0.7185 - val_acc: 0.7758\n",
      "Epoch 21/50\n",
      "50000/50000 [==============================] - 6s 129us/step - loss: 0.3313 - acc: 0.8819 - val_loss: 0.7220 - val_acc: 0.7721\n",
      "Epoch 22/50\n",
      "50000/50000 [==============================] - 6s 129us/step - loss: 0.3134 - acc: 0.8892 - val_loss: 0.7411 - val_acc: 0.7751\n",
      "Epoch 23/50\n",
      "50000/50000 [==============================] - 6s 129us/step - loss: 0.3061 - acc: 0.8903 - val_loss: 0.7551 - val_acc: 0.7767\n",
      "Epoch 24/50\n",
      "50000/50000 [==============================] - 6s 128us/step - loss: 0.2812 - acc: 0.9010 - val_loss: 0.7759 - val_acc: 0.7695\n",
      "Epoch 25/50\n",
      "50000/50000 [==============================] - 6s 129us/step - loss: 0.2827 - acc: 0.8980 - val_loss: 0.7740 - val_acc: 0.7760\n",
      "Epoch 26/50\n",
      "50000/50000 [==============================] - 6s 129us/step - loss: 0.2768 - acc: 0.9020 - val_loss: 0.8171 - val_acc: 0.7692\n",
      "Epoch 27/50\n",
      "50000/50000 [==============================] - 6s 128us/step - loss: 0.2647 - acc: 0.9054 - val_loss: 0.7987 - val_acc: 0.7765\n",
      "Epoch 28/50\n",
      "50000/50000 [==============================] - 6s 129us/step - loss: 0.2533 - acc: 0.9103 - val_loss: 0.8112 - val_acc: 0.7726\n",
      "Epoch 29/50\n",
      "50000/50000 [==============================] - 6s 128us/step - loss: 0.2518 - acc: 0.9121 - val_loss: 0.8097 - val_acc: 0.7742\n",
      "Epoch 30/50\n",
      "50000/50000 [==============================] - 6s 129us/step - loss: 0.2426 - acc: 0.9147 - val_loss: 0.8517 - val_acc: 0.7734\n",
      "Epoch 31/50\n",
      "50000/50000 [==============================] - 6s 129us/step - loss: 0.2285 - acc: 0.9201 - val_loss: 0.8203 - val_acc: 0.7794\n",
      "Epoch 32/50\n",
      "50000/50000 [==============================] - 6s 128us/step - loss: 0.2251 - acc: 0.9214 - val_loss: 0.8824 - val_acc: 0.7651\n",
      "Epoch 33/50\n",
      "50000/50000 [==============================] - 6s 128us/step - loss: 0.2173 - acc: 0.9230 - val_loss: 0.8846 - val_acc: 0.7679\n",
      "Epoch 34/50\n",
      "50000/50000 [==============================] - 6s 128us/step - loss: 0.2139 - acc: 0.9251 - val_loss: 0.8547 - val_acc: 0.7750\n",
      "Epoch 35/50\n",
      "50000/50000 [==============================] - 6s 130us/step - loss: 0.2063 - acc: 0.9271 - val_loss: 0.8930 - val_acc: 0.7744\n",
      "Epoch 36/50\n",
      "50000/50000 [==============================] - 6s 128us/step - loss: 0.2109 - acc: 0.9251 - val_loss: 0.8623 - val_acc: 0.7755\n",
      "Epoch 37/50\n",
      "50000/50000 [==============================] - 6s 129us/step - loss: 0.2030 - acc: 0.9287 - val_loss: 0.9001 - val_acc: 0.7746\n",
      "Epoch 38/50\n",
      "50000/50000 [==============================] - 6s 128us/step - loss: 0.1962 - acc: 0.9316 - val_loss: 0.8962 - val_acc: 0.7719\n",
      "Epoch 39/50\n",
      "50000/50000 [==============================] - 6s 129us/step - loss: 0.1901 - acc: 0.9339 - val_loss: 0.9481 - val_acc: 0.7655\n",
      "Epoch 40/50\n",
      "50000/50000 [==============================] - 6s 128us/step - loss: 0.1851 - acc: 0.9347 - val_loss: 0.9045 - val_acc: 0.7719\n",
      "Epoch 41/50\n",
      "50000/50000 [==============================] - 6s 128us/step - loss: 0.1904 - acc: 0.9328 - val_loss: 0.9049 - val_acc: 0.7790\n",
      "Epoch 42/50\n",
      "50000/50000 [==============================] - 6s 128us/step - loss: 0.1760 - acc: 0.9387 - val_loss: 0.9005 - val_acc: 0.7784\n",
      "Epoch 43/50\n",
      "50000/50000 [==============================] - 6s 129us/step - loss: 0.1777 - acc: 0.9388 - val_loss: 0.9391 - val_acc: 0.7739\n",
      "Epoch 44/50\n",
      "50000/50000 [==============================] - 6s 129us/step - loss: 0.1772 - acc: 0.9376 - val_loss: 0.9207 - val_acc: 0.7781\n",
      "Epoch 45/50\n",
      "50000/50000 [==============================] - 6s 128us/step - loss: 0.1693 - acc: 0.9417 - val_loss: 0.9473 - val_acc: 0.7765\n",
      "Epoch 46/50\n",
      "50000/50000 [==============================] - 6s 128us/step - loss: 0.1616 - acc: 0.9437 - val_loss: 0.9907 - val_acc: 0.7734\n",
      "Epoch 47/50\n",
      "50000/50000 [==============================] - 6s 128us/step - loss: 0.1718 - acc: 0.9399 - val_loss: 0.9832 - val_acc: 0.7712\n",
      "Epoch 48/50\n",
      "50000/50000 [==============================] - 6s 128us/step - loss: 0.1582 - acc: 0.9462 - val_loss: 0.9661 - val_acc: 0.7747\n",
      "Epoch 49/50\n",
      "50000/50000 [==============================] - 6s 128us/step - loss: 0.1637 - acc: 0.9425 - val_loss: 0.9532 - val_acc: 0.7779\n",
      "Epoch 50/50\n",
      "50000/50000 [==============================] - 6s 129us/step - loss: 0.1530 - acc: 0.9471 - val_loss: 0.9661 - val_acc: 0.7790\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f2829a72a58>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.compile(\n",
    "    loss='categorical_crossentropy',\n",
    "    optimizer='adam',\n",
    "    metrics=['accuracy']\n",
    ")\n",
    "\n",
    "model.fit(x_train, y_train,\n",
    "          batch_size=128,\n",
    "          epochs=50,\n",
    "          validation_data=(x_test, y_test))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss: 0.9660877988576889\n",
      "Test accuracy: 0.779\n",
      "Training loss: 0.024635728995651005\n",
      "Training accuracy: 0.99504\n"
     ]
    }
   ],
   "source": [
    "score = model.evaluate(x_test, y_test, verbose=0)\n",
    "print('Test loss:', score[0])\n",
    "print('Test accuracy:', score[1])\n",
    "\n",
    "score = model.evaluate(x_train, y_train, verbose=0)\n",
    "print('Training loss:', score[0])\n",
    "print('Training accuracy:', score[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "78% accuracy! Our best yet. Looks heavily overfit though (99% accuracy on the training set... maybe needs more dropout?) \n",
    "\n",
    "Still a long way to go to beat the [record](http://rodrigob.github.io/are_we_there_yet/build/classification_datasets_results.html#43494641522d3130) (96%).  We can make a lot of progress by making the network (much) bigger, training for (much) longer and using a lot of little tricks (like data augmentation) but that is beyond the scope of this lesson for now."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's also recall how to predict a single value and look at its probabilities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted = 3, actual = 3\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 10 artists>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "x_sample = x_test[0].reshape(1,32,32,3)\n",
    "y_prob = model.predict(x_sample)[0]\n",
    "y_pred = y_prob.argmax()\n",
    "y_actual = y_test[0].argmax()\n",
    "\n",
    "print(\"predicted = %d, actual = %d\" % (y_pred, y_actual))\n",
    "matplotlib.pyplot.bar(range(10), y_prob)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's also review here how to save and load trained keras models. It's easy! From [Keras docuemtnation](https://keras.io/getting-started/faq/#how-can-i-save-a-keras-model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import load_model\n",
    "\n",
    "model.save('my_model.h5')  # creates a HDF5 file 'my_model.h5'\n",
    "del model  # deletes the existing model\n",
    "\n",
    "# returns a compiled model\n",
    "# identical to the previous one\n",
    "model = load_model('my_model.h5')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}