Created
January 19, 2019 14:55
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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/") |
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