aae_train.py

def train(self, epochs, train_data, batch_size):

    real = np.ones((batch_size, 1))
    fake = np.zeros((batch_size, 1))
    history = []
    for epoch in range(epochs):
        #  Train Discriminator
        batch_indexes = np.random.randint(0, train_data.shape[0], batch_size)
        batch = train_data[batch_indexes]

        latent_vector_fake = self.encoder_model.predict(batch)
        latent_vector_real = np.random.normal(size=(batch_size, self.latent_dimension))

        loss_real = self.discriminator_model.train_on_batch(latent_vector_real, real)
        loss_fake = self.discriminator_model.train_on_batch(latent_vector_fake, fake)
        discriminator_loss = 0.5 * np.add(loss_real, loss_fake)

        #  Train Generator
        generator_loss = self.aae.train_on_batch(batch, [batch, real])

        # Plot the progress
        print ("---------------------------------------------------------")
        print ("******************Epoch {}***************************".format(epoch))
        print ("Discriminator loss: {}".format(discriminator_loss[0]))
        print ("Generator loss: {}".format(generator_loss))
        print ("---------------------------------------------------------")

        history.append({"D":discriminator_loss[0],"G":generator_loss})

        # Save images from every hundereth epoch generated images
        if epoch % 100 == 0:
            self._save_images(epoch)

    self._plot_loss(history)
    self._image_helper.makegif("generated-aae/")