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

import tensorflow as tf
sess = tf.InteractiveSession()


def weightVariables(shape):
    initial = tf.truncated_normal(shape, stddev=0.1)
    return tf.Variable(initial)

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

def conv2d(x, W):
    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')

def maxPool2x2(x):
    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')

def main():
    '''PlaceHolders'''
    # Input
    x = tf.placeholder(tf.float32, shape=[None, 784])
    # Target
    y_ = tf.placeholder(tf.float32, shape=[None, 10])

    
    #  First Convolutional Layer
    wConv1 = weightVariables([5, 5, 1, 32])
    bConv1 = biasVariable([32])
    xImage = tf.reshape(x, [-1, 28, 28, 1])
    hConv1 = tf.nn.relu(conv2d(xImage, wConv1) + bConv1)
    hPool1 = maxPool2x2(hConv1)
    # Ouput : 14 * 14
    
    #  Second Convolutional Layer
    wConv2 = weightVariables([5, 5, 32, 64])
    bConv2 = biasVariable([64])
    hConv2 = tf.nn.relu(conv2d(hPool1, wConv2) + bConv2)
    hPool2 = maxPool2x2(hConv2)
    # Output : 7 * 7
    
    # Densely Connected Layer
    wFC1 = weightVariables([7 * 7 * 64, 1024])
    bFC1 = biasVariable([1024])
    hPool2Flat = tf.reshape(hPool2, [-1, 7*7*64])
    hFC1 = tf.nn.relu(tf.matmul(hPool2Flat, wFC1) + bFC1)
    
    # Dropout
    keep_prob = tf.placeholder(tf.float32)
    hFC1Drop = tf.nn.dropout(hFC1, keep_prob)
    
    wFC2 = weightVariables([1024, 10])
    bFC2 = biasVariable([10])
    
    yConv = tf.matmul(hFC1Drop, wFC2) + bFC2
    
    
    #  Train ans evaluate the model:
    crossEntropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=yConv))
    trainStep = tf.train.AdamOptimizer(1e-4).minimize(crossEntropy)
    
    correctPrediction = tf.equal(tf.argmax(yConv, 1), tf.argmax(y_, 1))
    accuracy = tf.reduce_mean(tf.cast(correctPrediction, tf.float32))
    
    sess.run(tf.global_variables_initializer())
    
    for i in range(20000):
        batch = mnist.train.next_batch(50)
        if i%100 == 0:
            trainAccuracy = accuracy.eval(feed_dict={
                x: batch[0], y_: batch[1], keep_prob: 1.0 
            })
            print("Step : %d, training accuracy : %g"%(i, trainAccuracy))
            
        trainStep.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
    }))


if __name__ == '__main__':
    main()


    

    




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
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Test Accuracy : 0.9923

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