def run(method, output_dir, num_epochs, fake_data=False, fake_training=False):
"""Trains a model and records its predictions on configured datasets.
Args:
method: Modeling method to experiment with.
output_dir: Directory to record the trained model and output stats.
num_epochs: Number of training epochs.
fake_data: If true, use fake data.
fake_training: If true, train for a trivial number of steps.
Returns:
Trained Keras model.
"""
tf.io.gfile.makedirs(output_dir)
data_config_train = data_lib.DataConfig(split='train', fake_data=fake_data)
data_config_valid = data_lib.DataConfig(split='valid', fake_data=fake_data)
hparams = hparams_lib.get_tuned_hparams(method, parameterization='C')
model_opts = hparams_lib.model_opts_from_hparams(hparams, method,
parameterization='C',
fake_training=fake_training)
experiment_utils.record_config(model_opts, output_dir+'/model_options.json')
model = models_lib.build_and_train_model(
model_opts, data_config_train, data_config_valid,
output_dir=output_dir,
num_epochs=num_epochs,
fake_training=fake_training)
logging.info('Saving model to output_dir.')
model.save_weights(output_dir + '/model.ckpt')
# TODO(yovadia): Looks like Keras save_model does not work with Python3.
# (e.g. see b/129323565).
# experiment_utils.save_model(model, output_dir)
return model
def test_build_dataset(self):
config = data_lib.DataConfig(split='train', fake_data=True)
dataset = data_lib.build_dataset(config,
batch_size=8,
is_training=False,
fake_training=False)
# Check output_shapes.
features_shapes, label_shape = dataset.output_shapes
self.assertEqual([None], label_shape.as_list())
expected_keys = [
data_lib.feature_name(i)
for i in range(1, data_lib.NUM_TOTAL_FEATURES + 1)
]
self.assertSameElements(expected_keys, list(features_shapes.keys()))
for key, shape in six.iteritems(features_shapes):
self.assertEqual([None], shape.as_list(),
'Unexpected shape at key=' + key)
# Check output_types.
features_types, label_type = tf.compat.v1.data.get_output_types(
dataset)
self.assertEqual(tf.float32, label_type)
for idx in data_lib.INT_FEATURE_INDICES:
self.assertEqual(tf.float32,
features_types[data_lib.feature_name(idx)])
for idx in data_lib.CAT_FEATURE_INDICES:
self.assertEqual(tf.string,
features_types[data_lib.feature_name(idx)])