3.4_Multilayer_Perceptron.ipynb

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      "source": [
        "## Multilayer Perceptron"
      ]
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      "source": [
        "# Import MINST data\n",
        "from tensorflow.examples.tutorials.mnist import input_data\n",
        "mnist = input_data.read_data_sets(\"tmp/data/\", one_hot=True)\n",
        "\n",
        "import tensorflow as tf"
      ],
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            "WARNING:tensorflow:From <ipython-input-1-4666c8b62dd4>:2: read_data_sets (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use alternatives such as official/mnist/dataset.py from tensorflow/models.\n",
            "WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:260: maybe_download (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please write your own downloading logic.\n",
            "WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:262: extract_images (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use tf.data to implement this functionality.\n",
            "Extracting tmp/data/train-images-idx3-ubyte.gz\n",
            "WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:267: extract_labels (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use tf.data to implement this functionality.\n",
            "Extracting tmp/data/train-labels-idx1-ubyte.gz\n",
            "WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:110: dense_to_one_hot (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use tf.one_hot on tensors.\n",
            "Extracting tmp/data/t10k-images-idx3-ubyte.gz\n",
            "Extracting tmp/data/t10k-labels-idx1-ubyte.gz\n",
            "WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:290: __init__ (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use alternatives such as official/mnist/dataset.py from tensorflow/models.\n"
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      "source": [
        "# Parameters\n",
        "learning_rate = 0.01\n",
        "training_epochs = 15\n",
        "batch_size = 100\n",
        "display_step = 1\n",
        "\n",
        "# Network Parameters\n",
        "n_hidden_1 = 256 # 1st layer number of features\n",
        "n_hidden_2 = 256 # 2nd layer number of features\n",
        "n_input = 784 # MNIST data input (img shape: 28*28)\n",
        "n_classes = 10 # MNIST total classes (0-9 digits)\n",
        "\n",
        "# tf Graph input\n",
        "x = tf.placeholder(\"float\", [None, n_input])\n",
        "y = tf.placeholder(\"float\", [None, n_classes])"
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        "# Create model\n",
        "def multilayer_perceptron(x, weights, biases):\n",
        "    # Hidden layer with RELU activation\n",
        "    layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])\n",
        "    layer_1 = tf.nn.relu(layer_1)\n",
        "    # Hidden layer with RELU activation\n",
        "    layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2'])\n",
        "    layer_2 = tf.nn.relu(layer_2)\n",
        "    # Output layer with linear activation\n",
        "    out_layer = tf.matmul(layer_2, weights['out']) + biases['out']\n",
        "    return out_layer"
      ],
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        "# Store layers weight & bias\n",
        "weights = {\n",
        "    'h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])),\n",
        "    'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),\n",
        "    'out': tf.Variable(tf.random_normal([n_hidden_2, n_classes]))\n",
        "}\n",
        "biases = {\n",
        "    'b1': tf.Variable(tf.random_normal([n_hidden_1])),\n",
        "    'b2': tf.Variable(tf.random_normal([n_hidden_2])),\n",
        "    'out': tf.Variable(tf.random_normal([n_classes]))\n",
        "}\n",
        "\n",
        "# Construct model\n",
        "pred = multilayer_perceptron(x, weights, biases)\n",
        "\n",
        "# Define loss and optimizer\n",
        "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))\n",
        "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)\n",
        "\n",
        "# Initializing the variables\n",
        "init = tf.global_variables_initializer()"
      ],
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        "# Launch the graph\n",
        "with tf.Session() as sess:\n",
        "    sess.run(init)\n",
        "\n",
        "    # Training cycle\n",
        "    for epoch in range(training_epochs):\n",
        "        avg_cost = 0.\n",
        "        total_batch = int(mnist.train.num_examples/batch_size)\n",
        "        # Loop over all batches\n",
        "        for i in range(total_batch):\n",
        "            batch_x, batch_y = mnist.train.next_batch(batch_size)\n",
        "            # Run optimization op (backprop) and cost op (to get loss value)\n",
        "            _, c = sess.run([optimizer, cost], feed_dict={x: batch_x,\n",
        "                                                          y: batch_y})\n",
        "            # Compute average loss\n",
        "            avg_cost += c / total_batch\n",
        "        # Display logs per epoch step\n",
        "        if epoch % display_step == 0:\n",
        "            print \"Epoch:\", '%04d' % (epoch+1), \"cost=\", \\\n",
        "                \"{:.9f}\".format(avg_cost)\n",
        "    print \"Optimization Finished!\"\n",
        "\n",
        "    # Test model\n",
        "    correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))\n",
        "    # Calculate accuracy\n",
        "    accuracy = tf.reduce_mean(tf.cast(correct_prediction, \"float\"))\n",
        "    print \"Accuracy:\", accuracy.eval({x: mnist.test.images, y: mnist.test.labels})"
      ],
      "execution_count": 7,
      "outputs": [
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          "text": [
            "Epoch: 0001 cost= 43.282013203\n",
            "Epoch: 0002 cost= 8.349755084\n",
            "Epoch: 0003 cost= 4.672669222\n",
            "Epoch: 0004 cost= 3.394152646\n",
            "Epoch: 0005 cost= 2.467624623\n",
            "Epoch: 0006 cost= 2.193587589\n",
            "Epoch: 0007 cost= 2.039622562\n",
            "Epoch: 0008 cost= 1.736807735\n",
            "Epoch: 0009 cost= 1.386611417\n",
            "Epoch: 0010 cost= 1.591948932\n",
            "Epoch: 0011 cost= 1.320480854\n",
            "Epoch: 0012 cost= 1.160013903\n",
            "Epoch: 0013 cost= 1.042314344\n",
            "Epoch: 0014 cost= 0.775135798\n",
            "Epoch: 0015 cost= 0.914223716\n",
            "Optimization Finished!\n",
            "Accuracy: 0.9594\n"
          ],
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        }
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      "source": [
        "### Exercice 1\n",
        "Modify the architecture of the network. You can add extra hidden layers and/or the number of neurons per layer. How do you obtain the best results?"
      ]
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