model.compile(optimizer=optimizer, loss='mean_squared_error')
model.summary()
train_dataset, valid_dataset = get_datasets(params)
model.fit(train_dataset,
steps_per_epoch=params.train_data_size//params.batchsize,
epochs=params.epochs,
validation_data=valid_dataset,
validation_steps=params.valid_data_size//params.batchsize,
callbacks=callbacks)
if __name__ == '__main__':
if len(sys.argv) != 3:
logging.error("Usage", sys.argv[0], "configuration_YAML_file", "configuration")
exit()
# load hyperparameters
params = YParams(os.path.abspath(sys.argv[1]), sys.argv[2])
os.mkdir(params.experiment_dir)
log_filename=params.experiment_dir+'/output.log'
logging_utils.log_to_file(logger_name=None, log_filename=log_filename)
logging_utils.log_versions()
params.log()
keras_logger = keras.callbacks.CSVLogger(log_filename, separator=',', append=True)
tensorboard = keras.callbacks.TensorBoard(log_dir=params.experiment_dir)
train(params, [keras_logger, tensorboard])
#expDir = parameters.load_params('./config.yaml', configtag)['expDir']
baseDir = './expts/' + configtag + '/'
expDir = baseDir + 'run' + str(run_num) + '/'
if not os.path.isdir(baseDir):
os.mkdir(baseDir)
if not os.path.isdir(expDir):
os.mkdir(expDir)
os.mkdir(expDir + 'models')
else:
print("Experiment directory %s already exists, exiting" % expDir)
sys.exit()
#Set up logger
logging_utils.config_logger(log_level=logging.INFO)
logging_utils.log_to_file(logger_name=None, log_filename=expDir + 'train.log')
# Build GAN
GAN = GANbuild.DCGAN(configtag, expDir)
Nepochs = GAN.Nepochs
Nbatches = GAN.n_imgs // GAN.batchsize
for epoch in np.arange(GAN.start, Nepochs + GAN.start):
logging.info(
"| ******************************* Epoch %d of %d ******************************* |"
% (epoch + 1, Nepochs + GAN.start))
shuff_idxs = np.random.permutation(GAN.n_imgs)
GAN.train_epoch(shuff_idxs, Nbatches, epoch)
if (epoch + 1) % 5 == 0: