NN_MNIST.py

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


n_nodes_hl1 = 500
n_nodes_hl2 = 500
n_nodes_hl3 = 500

n_classes = 10
batch_size = 100

#height by width
x = tf.placeholder('float', [None, 784])
y = tf.placeholder('float')

def neural_network_model(data):
    print(data.shape)

    hidden_1_layer ={ 'weights': tf.Variable(tf.random_normal([784, n_nodes_hl1])),
                      'bias': tf.Variable(tf.random_normal([n_nodes_hl1]))}
    hidden_2_layer = {'weights': tf.Variable(tf.random_normal([n_nodes_hl1, n_nodes_hl2])),
                      'bias': tf.Variable(tf.random_normal([n_nodes_hl2]))}
    hidden_3_layer = {'weights': tf.Variable(tf.random_normal([n_nodes_hl2, n_nodes_hl3])),
                      'bias': tf.Variable(tf.random_normal([n_nodes_hl3]))}
    output = {'weights': tf.Variable(tf.random_normal([n_nodes_hl3, n_classes])),
                      'bias': tf.Variable(tf.random_normal([n_classes]))}

    l1 = tf.add(tf.matmul(data,hidden_1_layer['weights']), hidden_1_layer['bias'])
    l1 = tf.nn.relu(l1)

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

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

    output = tf.matmul(l3, output['weights']) + output['bias']

    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)
    # precision = tf.metrics.precision( predictions=prediction,labels=y)
    # recall = tf.metrics.recall( predictions=prediction, labels=y)
    # f1 =  tf.matmul(2, (tf.matmul(precision , recall))) / (tf.add(precision + recall))

    #cycles of feed forward and backprop
    hm_epochs = 10

    with tf.Session() as sess:
        sess.run(tf.initialize_all_variables())

        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, 'compleated out of', hm_epochs, 'loss:', epoch_loss)
            # print('F1 score: ', f1)

        correct =  tf.equal(tf.arg_max(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)