In [2]:

    

import tensorflow as tf

from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/tmp/data/",one_hot=True)


    

    




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

In [3]:

    

learning_rate = 0.001
training_epochs = 15
batch_size = 100
display_step =1


n_input = 784
n_classes = 10

n_hidden_1 = 256
n_hidden_2 = 256


    

In [21]:

    

X = tf.placeholder(tf.float32, [None, n_input], name="input")
Y = tf.placeholder(tf.float32, [None, n_classes], name="output")


    

In [22]:

    

def multilayer_perceptron(X, weights, biases):
    
    layer_1 = tf.add(tf.matmul(X, weights['h1']), biases['b1'])
    layer_1 = tf.nn.relu(layer_1)
    
    layer_2 = tf.add(tf.matmul(layer_1,weights['h2']), biases['b2'])
    layer_2 = tf.nn.relu(layer_2)
    
    out_layer = tf.matmul(layer_2,weights['out']) + biases['out']
    
    return out_layer


    

In [23]:

    

weights = {
    'h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])),
    'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),
    'out':tf.Variable(tf.random_normal([n_hidden_2, n_classes]))
}

biases = {
    'b1' : tf.Variable(tf.random_normal([n_hidden_1])),
    'b2' : tf.Variable(tf.random_normal([n_hidden_2])),
    'out': tf.Variable(tf.random_normal([n_classes]))
    
}

pred = multilayer_perceptron(X, weights, biases)

cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred,Y))
optimizer = tf.train.AdamOptimizer(learning_rate= learning_rate).minimize(cost)

init = tf.initialize_all_variables()


    

In [25]:

    

with tf.Session() as sess:
    sess.run(init)
    
    for epoch in range(training_epochs):
        avg_cost = 0.
        
        total_batch = int(mnist.train.num_examples/batch_size)
        for i in range(total_batch):
            batch_X, batch_Y = mnist. train.next_batch(batch_size)
            
            _, c = sess.run([optimizer, cost], feed_dict={X: batch_X, Y: batch_Y})
            
            avg_cost += c/total_batch
        if epoch % display_step == 0:
            print("Epoch:"+'%04d' % (epoch+1), "cost=" + "{:.9f}".format(avg_cost))

    print("Optimization Finished!")
    
    correct_prediction = tf.equal(tf.argmax(pred,1), tf.argmax(Y,1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction,"float"))
    print("Accuracy : " +str(accuracy.eval({X:mnist.test.images, Y:mnist.test.labels})))


    

    




Epoch:0001 cost=195.111316407
Epoch:0002 cost=43.858165465
Epoch:0003 cost=27.529450860
Epoch:0004 cost=19.268953726
Epoch:0005 cost=14.040415219
Epoch:0006 cost=10.463342705
Epoch:0007 cost=7.890293884
Epoch:0008 cost=5.833286557
Epoch:0009 cost=4.502147053
Epoch:0010 cost=3.285491708
Epoch:0011 cost=2.423698032
Epoch:0012 cost=1.770643686
Epoch:0013 cost=1.418311109
Epoch:0014 cost=1.040065616
Epoch:0015 cost=0.925381133
Optimization Finished!
Accuracy : 0.946

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