def test_global_emnist_dataset_structure(self):
global_train, global_test = dataset.get_centralized_emnist_datasets(
batch_size=32, only_digits=False)
train_batch = next(iter(global_train))
train_batch_shape = train_batch[0].shape
test_batch = next(iter(global_test))
test_batch_shape = test_batch[0].shape
self.assertEqual(train_batch_shape.as_list(), [32, 28, 28, 1])
self.assertEqual(test_batch_shape.as_list(), [TEST_BATCH_SIZE, 28, 28, 1])
def main(argv):
if len(argv) > 1:
raise app.UsageError('Too many command-line arguments.')
tf.compat.v1.enable_v2_behavior()
experiment_output_dir = FLAGS.root_output_dir
tensorboard_dir = os.path.join(experiment_output_dir, 'logdir',
FLAGS.experiment_name)
results_dir = os.path.join(experiment_output_dir, 'results',
FLAGS.experiment_name)
for path in [experiment_output_dir, tensorboard_dir, results_dir]:
try:
tf.io.gfile.makedirs(path)
except tf.errors.OpError:
pass # Directory already exists.
hparam_dict = collections.OrderedDict([(name, FLAGS[name].value)
for name in hparam_flags])
hparam_dict['results_file'] = results_dir
hparams_file = os.path.join(results_dir, 'hparams.csv')
logging.info('Saving hyper parameters to: [%s]', hparams_file)
utils_impl.atomic_write_to_csv(pd.Series(hparam_dict), hparams_file)
train_dataset, eval_dataset = dataset.get_centralized_emnist_datasets(
batch_size=FLAGS.batch_size, only_digits=False)
optimizer = optimizer_utils.create_optimizer_fn_from_flags('centralized')()
if FLAGS.model == 'cnn':
model = models.create_conv_dropout_model(only_digits=False)
elif FLAGS.model == '2nn':
model = models.create_two_hidden_layer_model(only_digits=False)
else:
raise ValueError('Cannot handle model flag [{!s}].'.format(
FLAGS.model))
model.compile(loss=tf.keras.losses.SparseCategoricalCrossentropy(),
optimizer=optimizer,
metrics=[tf.keras.metrics.SparseCategoricalAccuracy()])
logging.info('Training model:')
logging.info(model.summary())
csv_logger_callback = keras_callbacks.AtomicCSVLogger(results_dir)
tensorboard_callback = tf.keras.callbacks.TensorBoard(
log_dir=tensorboard_dir)
# Reduce the learning rate after a fixed number of epochs.
def decay_lr(epoch, learning_rate):
if (epoch + 1) % FLAGS.decay_epochs == 0:
return learning_rate * FLAGS.lr_decay
else:
return learning_rate
lr_callback = tf.keras.callbacks.LearningRateScheduler(decay_lr, verbose=1)
history = model.fit(
train_dataset,
validation_data=eval_dataset,
epochs=FLAGS.num_epochs,
callbacks=[lr_callback, tensorboard_callback, csv_logger_callback])
logging.info('Final metrics:')
for name in ['loss', 'sparse_categorical_accuracy']:
metric = history.history['val_{}'.format(name)][-1]
logging.info('\t%s: %.4f', name, metric)