def train(self, initial_epoch, nb_epochs, steps_per_epoch=int(1e3)):
"""Model training method"""
# Adversarial ground truths
valid = np.ones((self.batch_generator.batch_size, 1))
fake = np.zeros((self.batch_generator.batch_size, 1))
for epoch in range(initial_epoch + 1, initial_epoch + nb_epochs):
discrim_losses = []
for step in range(steps_per_epoch):
# -------------------
# Train discriminator
# -------------------
img, mask = next(self._get_batch())
generated = self.generator.predict(img)
generated = np.reshape(
generated,
(self.batch_generator.batch_size, *self.mask_shape))
# train discriminator where real-like classified images are 1's
# and fake-like ones are 0's
discrim_loss_real = self.discriminator.train_on_batch(
np.concatenate([img, mask], axis=-1), valid)
discrim_loss_fake = self.discriminator.train_on_batch(
np.concatenate([img, generated], axis=-1), fake)
discrim_losses.append(discrim_loss_real)
discrim_losses.append(discrim_loss_fake)
# ---------------
# Train generator
# ---------------
self.combined_model.train_on_batch(img, valid)
logging.info('One image IOU: {:2f}'.format(
DCGAN.metric(generated, mask)))
discrim_loss = np.sum(discrim_losses, axis=0) / len(discrim_losses)
logging.info('Discrimination loss: {:2f}, accucacy: {:2f}'.format(
discrim_loss[0], 100 * discrim_loss[1]))
# log error value
self.loss_validate.error_log(epoch)
if epoch % 50 == 0:
self.combined_model.save_weights(
self.dcgan_weights_path.format(epoch))
self.generator.save_weights(
self.generator_weights_path.format(epoch))
self.discriminator.save_weights(
self.discriminator_weights_path.format(epoch))
discriminator_nn.trainable = False
# ------------------------
# Define Optimizers
opt_discriminator = Adam(lr=1E-4, beta_1=0.9, beta_2=0.999, epsilon=1e-08)
opt_dcgan = Adam(lr=1E-4, beta_1=0.9, beta_2=0.999, epsilon=1e-08)
# -------------------------
# compile generator
generator_nn.compile(loss='mae', optimizer=opt_discriminator)
# ----------------------
# MAKE FULL DCGAN
# ----------------------
dc_gan_nn = DCGAN(generator_model=generator_nn,
discriminator_model=discriminator_nn,
input_img_dim=input_img_dim,
patch_dim=sub_patch_dim)
dc_gan_nn.summary()
# ---------------------
# Compile DCGAN
# we use a combination of mae and bin_crossentropy
loss = ['mae', 'binary_crossentropy']
loss_weights = [1E2, 1]
dc_gan_nn.compile(loss=loss, loss_weights=loss_weights, optimizer=opt_dcgan)
# ---------------------
# ENABLE DISCRIMINATOR AND COMPILE
discriminator_nn.trainable = True
discriminator_nn.compile(loss='binary_crossentropy',
opt_DCGAN = Adam(lr=lr, beta_1=beta_1, beta_2=beta_2, epsilon=epsilon)
# UNetGenerator
generator = UNetGenerator(input_dim, output_channels)
generator.summary()
generator.compile(loss='mae',
optimizer=opt_generator) # L1 loss for image generation
# Patch GAN Discriminator
nb_patches, patch_gan_dim = patch_utils.num_patches(output_dim, patch_size)
discriminator = PatchGanDiscriminator(output_dim, patch_size, nb_patches)
discriminator.summary()
discriminator.trainable = False
# DCGAN
dc_gan = DCGAN(generator, discriminator, input_dim, patch_size)
dc_gan.summary()
# Total Loss
loss = ['mae', 'binary_crossentropy']
loss_weights = [1e2, 1]
dc_gan.compile(loss=loss, loss_weights=loss_weights, optimizer=opt_DCGAN)
discriminator.trainable = True
discriminator.compile(loss='binary_crossentropy',
optimizer=opt_discriminator)
# -------------------
# set up tensorboard
# -------------------