Hands-On Deep Learning Algorithms with Python

Training the Capsule network

Set the number of epochs and number of steps:

num_epochs = 100num_steps = int(len(mnist.train.images)/batch_size)

Now start the TensorFlow Session and perform training:

with tf.Session(graph=graph) as sess:    init_op = tf.global_variables_initializer()    sess.run(init_op)    for epoch in range(num_epochs):        for iteration in range(num_steps):            batch_data, batch_labels = mnist.train.next_batch(batch_size)            feed_dict = {x : batch_data, y : batch_labels}                 _, loss, acc = sess.run([train_op, total_loss, accuracy], feed_dict=feed_dict)            if iteration%10 == 0:                print('Epoch: {}, iteration:{}, Loss:{} Accuracy: {}'.format(epoch,iteration,loss,acc))

You can see how the loss decreases over various iterations:

Epoch: 0, iteration:0, ...