Hands-On Deep Learning Algorithms with Python

Training the model

Now, it is time to train our model. As we have learned, first, we need to initialize all of the variables:

init = tf.global_variables_initializer()

Define the batch size, number of iterations, and learning rate, as follows:

learning_rate = 1e-4num_iterations = 1000batch_size = 128

Start the TensorFlow session:

with tf.Session() as sess:

Initialize all the variables:

    sess.run(init)

Save the event files:

    summary_writer = tf.summary.FileWriter('./graphs', graph=sess.graph)

Train the model for a number of iterations:

    for i in range(num_iterations):

Get a batch of data according to the batch size:

        batch_x, batch_y = mnist.train.next_batch(batch_size)

Train the network:

 sess.run(optimizer, feed_dict={ X: batch_x, Y: batch_y}) ...