def next_batch(batch_size):
label = [0, 1, 0, 0, 0]
X = []
Y = []
for i in range(0, batch_size):
    rand = random.choice(os.listdir(mnist))
    rand = mnist + rand
    img = cv2.imread(str(rand), 0)
    img = np.array(img)
    img = img.ravel()
    X.append(img)
    Y.append(label)
X = np.array(X)
Y = np.array(Y)
return X, Y

def train(train_model=True):
"""
Used to train the autoencoder by passing in the necessary inputs.
:param train_model: True -> Train the model, False -> Load the latest trained model and show the image grid.
:return: does not return anything
"""
with tf.variable_scope(tf.get_variable_scope()):
    encoder_output = encoder(x_input)
    # Concat class label and the encoder output
    decoder_input = tf.concat([y_input, encoder_output], 1)
    decoder_output = decoder(decoder_input)

with tf.variable_scope(tf.get_variable_scope()):
    d_real = discriminator(real_distribution)
    d_fake = discriminator(encoder_output, reuse=True)

with tf.variable_scope(tf.get_variable_scope()):
    decoder_image = decoder(manual_decoder_input, reuse=True)

# Autoencoder loss
autoencoder_loss = tf.reduce_mean(tf.square(x_target - decoder_output))

# Discriminator Loss
dc_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.ones_like(d_real), logits=d_real))
dc_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.zeros_like(d_fake), logits=d_fake))
dc_loss = dc_loss_fake + dc_loss_real

# Generator loss
generator_loss = tf.reduce_mean(
    tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.ones_like(d_fake), logits=d_fake))

all_variables = tf.trainable_variables()
dc_var = [var for var in all_variables if 'dc_' in var.name]
en_var = [var for var in all_variables if 'e_' in var.name]

# Optimizers
autoencoder_optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate,
                                               beta1=beta1).minimize(autoencoder_loss)
discriminator_optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate,
                                                 beta1=beta1).minimize(dc_loss, var_list=dc_var)
generator_optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate,
                                             beta1=beta1).minimize(generator_loss, var_list=en_var)

init = tf.global_variables_initializer()

# Reshape images to display them
input_images = tf.reshape(x_input, [-1, 368, 432, 1])
generated_images = tf.reshape(decoder_output, [-1, 368, 432, 1])

# Tensorboard visualization
tf.summary.scalar(name='Autoencoder Loss', tensor=autoencoder_loss)
tf.summary.scalar(name='Discriminator Loss', tensor=dc_loss)
tf.summary.scalar(name='Generator Loss', tensor=generator_loss)
tf.summary.histogram(name='Encoder Distribution', values=encoder_output)
tf.summary.histogram(name='Real Distribution', values=real_distribution)
tf.summary.image(name='Input Images', tensor=input_images, max_outputs=10)
tf.summary.image(name='Generated Images', tensor=generated_images, max_outputs=10)
summary_op = tf.summary.merge_all()

# Saving the model
saver = tf.train.Saver()
step = 0
with tf.Session() as sess:
    if train_model:
        tensorboard_path, saved_model_path, log_path = form_results()
        sess.run(init)
        writer = tf.summary.FileWriter(logdir=tensorboard_path, graph=sess.graph)
        for i in range(n_epochs):
            # print(n_epochs)
            n_batches = int(10000 / batch_size)
            print("------------------Epoch {}/{}------------------".format(i, n_epochs))
            for b in range(1, n_batches+1):
                # print("In the loop")
                z_real_dist = np.random.randn(batch_size, z_dim) * 5.
                batch_x, batch_y = next_batch(batch_size)
                # print("Created the batches")
                sess.run(autoencoder_optimizer, feed_dict={x_input: batch_x, x_target: batch_x, y_input: batch_y})
                print("batch_x", batch_x)
                print("x_input:", x_input)
                print("x_target:", x_target)
                print("y_input:", y_input)
                sess.run(discriminator_optimizer,
                         feed_dict={x_input: batch_x, x_target: batch_x, real_distribution: z_real_dist})
                sess.run(generator_optimizer, feed_dict={x_input: batch_x, x_target: batch_x})
                # print("setup the session")
                if b % 50 == 0:
                    a_loss, d_loss, g_loss, summary = sess.run(
                        [autoencoder_loss, dc_loss, generator_loss, summary_op],
                        feed_dict={x_input: batch_x, x_target: batch_x,
                                   real_distribution: z_real_dist, y_input: batch_y})
                    writer.add_summary(summary, global_step=step)
                    print("Epoch: {}, iteration: {}".format(i, b))
                    print("Autoencoder Loss: {}".format(a_loss))
                    print("Discriminator Loss: {}".format(d_loss))
                    print("Generator Loss: {}".format(g_loss))
                    with open(log_path + '/log.txt', 'a') as log:
                        log.write("Epoch: {}, iteration: {}n".format(i, b))
                        log.write("Autoencoder Loss: {}n".format(a_loss))
                        log.write("Discriminator Loss: {}n".format(d_loss))
                        log.write("Generator Loss: {}n".format(g_loss))
                step += 1

            saver.save(sess, save_path=saved_model_path, global_step=step)
    else:
        # Get the latest results folder
        all_results = os.listdir(results_path)
        all_results.sort()
        saver.restore(sess, save_path=tf.train.latest_checkpoint(results_path + '/' +
                                                                 all_results[-1] + '/Saved_models/'))
        generate_image_grid(sess, op=decoder_image)


if __name__ == '__main__':
    parser = argparse.ArgumentParser(description="Autoencoder Train Parameter")
    parser.add_argument('--train', '-t', type=bool, default=True,
                        help='Set to True to train a new model, False to load weights and display image grid')
    args = parser.parse_args()
    train(train_model=args.train)