Neural Network for MNIST using TensorFlow

ann_mnist_tensorflow.py

import tensorflow as tf

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

n_nodes_hl1 = 500
n_nodes_hl2 = 500
n_nodes_hl3 = 500 
n_classes = 10
batch_size = 100

x = tf.placeholder('float',[None,784]) #if shape is not defined, we can feed in any shape
y = tf.placeholder('float',[None,10]) 

def neural_network_model(data):
    hidden_layer_1 = {'weights':tf.Variable(tf.random_normal([784,n_nodes_hl1])),
                     'biases':tf.Variable(tf.random_normal([n_nodes_hl1]))}
    hidden_layer_2 = {'weights':tf.Variable(tf.random_normal([n_nodes_hl1,n_nodes_hl2])),
                     'biases':tf.Variable(tf.random_normal([n_nodes_hl2]))}
    hidden_layer_3 = {'weights':tf.Variable(tf.random_normal([n_nodes_hl2,n_nodes_hl3])),
                     'biases':tf.Variable(tf.random_normal([n_nodes_hl3]))}
    
    output_layer = {'weights':tf.Variable(tf.random_normal([n_nodes_hl3,n_classes])),
                    'biases':tf.Variable(tf.random_normal([n_classes]))}
    
    l1 = tf.add(tf.matmul(data,hidden_layer_1['weights']),hidden_layer_1['biases'])
    l1 = tf.nn.relu(l1)
    l2 = tf.add(tf.matmul(l1,hidden_layer_2['weights']),hidden_layer_2['biases'])
    l2 = tf.nn.relu(l2)
    l3 = tf.add(tf.matmul(l2,hidden_layer_3['weights']),hidden_layer_3['biases'])
    l3 = tf.nn.relu(l3)
    
    output  = tf.add(tf.matmul(l3,output_layer['weights']),output_layer['biases'])
    return output #tf.nn.softmax(output)
		
def train_neural_network(x):
    prediction = neural_network_model(x)
    cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=prediction,labels=y))
    optimizer = tf.train.AdamOptimizer().minimize(cost)
    hm_epochs = 10
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer()) 
        for epoch in range(hm_epochs):
            epoch_loss = 0
            for _ in range(int(mnist.train.num_examples/batch_size)):
                epoch_x,epoch_y = mnist.train.next_batch(batch_size)
                _, c = sess.run([optimizer,cost],feed_dict={x:epoch_x,y:epoch_y})
                epoch_loss+=c
            print('Epoch {}/{} completed, loss : {}'.format(epoch+1,hm_epochs,epoch_loss))
            
        correct = tf.equal(tf.argmax(prediction,1),tf.argmax(y,1))
        accuracy = tf.reduce_mean(tf.cast(correct,'float'))
        print('Accuracy : {}'.format(accuracy.eval({x:mnist.test.images, y:mnist.test.labels})))
				
train_neural_network(x)