5.1-introduction-to-convnets.ipynb

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      "Using TensorFlow backend.\n"
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       "'2.0.8'"
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     "execution_count": 1,
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   "source": [
    "import keras\n",
    "keras.__version__"
   ]
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   "source": [
    "# 5.1 - Introduction to convnets\n",
    "\n",
    "This notebook contains the code sample found in Chapter 5, Section 1 of [Deep Learning with Python](https://www.manning.com/books/deep-learning-with-python?a_aid=keras&a_bid=76564dff). Note that the original text features far more content, in particular further explanations and figures: in this notebook, you will only find source code and related comments.\n",
    "\n",
    "----\n",
    "\n",
    "First, let's take a practical look at a very simple convnet example. We will use our convnet to classify MNIST digits, a task that you've already been \n",
    "through in Chapter 2, using a densely-connected network (our test accuracy then was 97.8%). Even though our convnet will be very basic, its \n",
    "accuracy will still blow out of the water that of the densely-connected model from Chapter 2.\n",
    "\n",
    "The 6 lines of code below show you what a basic convnet looks like. It's a stack of `Conv2D` and `MaxPooling2D` layers. We'll see in a \n",
    "minute what they do concretely.\n",
    "Importantly, a convnet takes as input tensors of shape `(image_height, image_width, image_channels)` (not including the batch dimension). \n",
    "In our case, we will configure our convnet to process inputs of size `(28, 28, 1)`, which is the format of MNIST images. We do this via \n",
    "passing the argument `input_shape=(28, 28, 1)` to our first layer."
   ]
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  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
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   "source": [
    "from keras import layers\n",
    "from keras import models\n",
    "\n",
    "model = models.Sequential()\n",
    "model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(layers.Conv2D(64, (3, 3), activation='relu'))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(layers.Conv2D(64, (3, 3), activation='relu'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's display the architecture of our convnet so far:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
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     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 26, 26, 32)        320       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 13, 13, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 11, 11, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 5, 5, 64)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 3, 3, 64)          36928     \n",
      "=================================================================\n",
      "Total params: 55,744\n",
      "Trainable params: 55,744\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "You can see above that the output of every `Conv2D` and `MaxPooling2D` layer is a 3D tensor of shape `(height, width, channels)`. The width \n",
    "and height dimensions tend to shrink as we go deeper in the network. The number of channels is controlled by the first argument passed to \n",
    "the `Conv2D` layers (e.g. 32 or 64).\n",
    "\n",
    "The next step would be to feed our last output tensor (of shape `(3, 3, 64)`) into a densely-connected classifier network like those you are \n",
    "already familiar with: a stack of `Dense` layers. These classifiers process vectors, which are 1D, whereas our current output is a 3D tensor. \n",
    "So first, we will have to flatten our 3D outputs to 1D, and then add a few `Dense` layers on top:"
   ]
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   "cell_type": "code",
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   "source": [
    "model.add(layers.Flatten())\n",
    "model.add(layers.Dense(64, activation='relu'))\n",
    "model.add(layers.Dense(10, activation='softmax'))"
   ]
  },
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   "source": [
    "We are going to do 10-way classification, so we use a final layer with 10 outputs and a softmax activation. Now here's what our network \n",
    "looks like:"
   ]
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      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 26, 26, 32)        320       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 13, 13, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 11, 11, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 5, 5, 64)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 3, 3, 64)          36928     \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 576)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 64)                36928     \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 10)                650       \n",
      "=================================================================\n",
      "Total params: 93,322\n",
      "Trainable params: 93,322\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, our `(3, 3, 64)` outputs were flattened into vectors of shape `(576,)`, before going through two `Dense` layers.\n",
    "\n",
    "Now, let's train our convnet on the MNIST digits. We will reuse a lot of the code we have already covered in the MNIST example from Chapter \n",
    "2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
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   "source": [
    "from keras.datasets import mnist\n",
    "from keras.utils import to_categorical\n",
    "\n",
    "(train_images, train_labels), (test_images, test_labels) = mnist.load_data()\n",
    "\n",
    "train_images = train_images.reshape((60000, 28, 28, 1))\n",
    "train_images = train_images.astype('float32') / 255\n",
    "\n",
    "test_images = test_images.reshape((10000, 28, 28, 1))\n",
    "test_images = test_images.astype('float32') / 255\n",
    "\n",
    "train_labels = to_categorical(train_labels)\n",
    "test_labels = to_categorical(test_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
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     "text": [
      "Epoch 1/5\n",
      "60000/60000 [==============================] - 8s - loss: 0.1766 - acc: 0.9440     \n",
      "Epoch 2/5\n",
      "60000/60000 [==============================] - 7s - loss: 0.0462 - acc: 0.9855     \n",
      "Epoch 3/5\n",
      "60000/60000 [==============================] - 7s - loss: 0.0322 - acc: 0.9902     \n",
      "Epoch 4/5\n",
      "60000/60000 [==============================] - 7s - loss: 0.0241 - acc: 0.9926     \n",
      "Epoch 5/5\n",
      "60000/60000 [==============================] - 7s - loss: 0.0187 - acc: 0.9943     \n"
     ]
    },
    {
     "data": {
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       "<keras.callbacks.History at 0x7fbd9c4cd828>"
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   ],
   "source": [
    "model.compile(optimizer='rmsprop',\n",
    "              loss='categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "model.fit(train_images, train_labels, epochs=5, batch_size=64)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's evaluate the model on the test data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 9536/10000 [===========================>..] - ETA: 0s"
     ]
    }
   ],
   "source": [
    "test_loss, test_acc = model.evaluate(test_images, test_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.99129999999999996"
      ]
     },
     "execution_count": 9,
     "metadata": {},
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   ],
   "source": [
    "test_acc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While our densely-connected network from Chapter 2 had a test accuracy of 97.8%, our basic convnet has a test accuracy of 99.3%: we \n",
    "decreased our error rate by 68% (relative). Not bad! "
   ]
  }
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