In [1]:

    

# Convolutional network with Tensorflow
import tensorflow as tf

from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)

sess = tf.InteractiveSession()


    

    




Extracting MNIST_data/train-images-idx3-ubyte.gz
Extracting MNIST_data/train-labels-idx1-ubyte.gz
Extracting MNIST_data/t10k-images-idx3-ubyte.gz
Extracting MNIST_data/t10k-labels-idx1-ubyte.gz

In [2]:

    

# placeholders
x = tf.placeholder(tf.float32, shape=[None, 784])
y_ = tf.placeholder(tf.float32, shape=[None, 10])


    

In [17]:

    

# 2 functions for bias and weights variables
# initialization with a small values in order to prevent symmetry and dead neurons
def weight_variable(shape):
    initial = tf.truncated_normal(shape, stddev=0.1)
    return tf.Variable(initial)

def bias_variable(shape):
    initial = tf.constant(0.1, shape=shape)
    return tf.Variable(initial)


    

In [18]:

    

# 2 functions for convolution and pooling
def conv2d(x, W):
    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')

def max_pool_2x2(x):
    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')


    

In [19]:

    

# first convolutional layer
W_conv1 = weight_variable([5, 5, 1, 32])  # 5*5 is patch size and 1 is the input dimension and 32 is the output dimension
b_conv1 = bias_variable([32])             # since we have 32 neurons


    

In [20]:

    

x_image = tf.reshape(x, [-1, 28, 28, 1])  # 28*28 is the size of picture and 1 is the number of color channels


    

In [21]:

    

# compute convolutional and max pooling for the first layer
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)


    

In [22]:

    

# second convolutional layer
W_conv2 = weight_variable([5, 5, 32, 64])  # 32 input and 64 output
b_conv2 = bias_variable([64])              # since we have 64 neurons


    

In [23]:

    

# compute convolutional and max pooling for the second layer
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2)


    

In [24]:

    

# densely fully connected layer
W_fc1 = weight_variable([7*7*64, 1024])
b_fc1 = bias_variable([1024])

h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)


    

In [25]:

    

# dropout: we send a placeholder for that in order to enable it in training and disable it in testing
keep_prob = tf.placeholder(tf.float32)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)


    

In [26]:

    

# softmax layer to compute the distribution for the final layer
W_fc2 = weight_variable([1024, 10])    # 10 because we have 10 classes (of digits)
b_fc2 = bias_variable([10])

y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)


    

In [27]:

    

# train and evaluate the model
cross_entropy = -tf.reduce_sum(y_*tf.log(y_conv))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
sess.run(tf.initialize_all_variables())
for i in range(500):
  batch = mnist.train.next_batch(50)
  if i%100 == 0:
    train_accuracy = accuracy.eval(feed_dict={x:batch[0], y_: batch[1], keep_prob: 1.0})
    print("step %d, training accuracy %g"%(i, train_accuracy))
  train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})

print("test accuracy %g"%accuracy.eval(feed_dict={
    x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))


    

    




step 0, training accuracy 0.1
step 100, training accuracy 0.78
step 200, training accuracy 0.92
step 300, training accuracy 0.88
step 400, training accuracy 0.98
test accuracy 0.9436

In [28]:

    

W_conv2.eval()


    

    
Out[28]:





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         ..., 
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         ..., 
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         [  9.12411585e-02,   4.93742339e-02,   6.01217821e-02, ...,
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        [[  1.05035938e-01,  -2.38186046e-02,  -2.38994602e-02, ...,
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         ..., 
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        [[ -1.58385649e-01,  -4.56481212e-05,   1.47203162e-01, ...,
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        [[  4.22097892e-02,   5.23200817e-02,  -1.12159722e-01, ...,
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         ..., 
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         [  1.19916216e-01,   3.81405354e-02,  -1.44275740e-01, ...,
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         [ -2.98500862e-02,   5.85721843e-02,   1.47136167e-01, ...,
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In [ ]: