self.model_to_save = model
def on_epoch_end(self, epoch, logs=None):
fmt = checkpoint_models_path + 'final.%02d-%.4f.hdf5'
self.model_to_save.save(fmt % (epoch, logs['val_loss']))
# Load our model, added support for Multi-GPUs
num_gpu = len(get_available_gpus())
if num_gpu >= 2:
with tf.device("/cpu:0"):
model = build_encoder_decoder()
model = build_refinement(model)
if pretrained_path is not None:
model.load_weights(pretrained_path)
else:
migrate_model(model)
final = multi_gpu_model(model, gpus=num_gpu)
# rewrite the callback: saving through the original model and not the multi-gpu model.
model_checkpoint = MyCbk(model)
else:
model = build_encoder_decoder()
final = build_refinement(model)
if pretrained_path is not None:
final.load_weights(pretrained_path)
else:
migrate_model(final)
decoder_target = tf.placeholder(dtype='float32',
shape=(None, None, None, None))
final.compile(optimizer='nadam',
model_checkpoint = ModelCheckpoint(model_names,
monitor='val_loss',
verbose=1,
save_best_only=True)
early_stop = EarlyStopping('val_loss', patience=patience)
reduce_lr = ReduceLROnPlateau('val_loss',
factor=0.1,
patience=int(patience / 4),
verbose=1)
if pretrained_path is not None:
model = build_encoder_decoder()
model.load_weights(pretrained_path)
else:
model = build_encoder_decoder()
migrate.migrate_model(model)
model.compile(optimizer='nadam', loss=depth_loss)
print(model.summary())
# Final callbacks
callbacks = [tensor_board, model_checkpoint, early_stop, reduce_lr]
batch_size = 14
# Start Fine-tuning
model.fit_generator(train_gen(batch_size),
steps_per_epoch=num_train_samples // batch_size,
validation_data=valid_gen(batch_size),
validation_steps=num_valid_samples // batch_size,
