tfbasics.py

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import  input_data

mnist= input_data.read_data_sets('/tmp/data/',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])
y=tf.placeholder('float')

def neural_network_model(data):
    hidden_1_layer={'weights': tf.Variable(tf.random_normal([784,n_nodes_hl1])),\
                    'biases': tf.Variable(tf.random_normal([n_nodes_hl1]))}

    hidden_2_layer={'weights': tf.Variable(tf.random_normal([n_nodes_hl1,n_nodes_hl2])),\
                    'biases': tf.Variable(tf.random_normal([n_nodes_hl2]))}

    hidden_3_layer={'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]))}

    # (input_data* weights) + biases
    l1=tf.add(tf.matmul(data,hidden_1_layer['weights']) , hidden_1_layer['biases'])
    l1= tf.nn.relu(l1)

    l2=tf.add(tf.matmul(l1,hidden_2_layer['weights']) , hidden_2_layer['biases'])
    l2= tf.nn.relu(l2)

    l3=tf.add(tf.matmul(l2,hidden_3_layer['weights']) , hidden_3_layer['biases'])
    l3= tf.nn.relu(l3)

    output= tf.matmul(l3,output_layer['weights']) + output_layer['biases']
    return 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',epoch,'completed out of',hm_epochs,'loss:',epoch_loss)

        correct= tf.equal(tf.argmax(prediction,1),tf.argmax(y,1))
        accuracy= tf.reduce_mean(tf.cast(correct,'float'))
        print('Accuracy',accuracy.eval({x:mnist.test.images,
                                        y:mnist.test.labels}))

train_neural_network(x)