def create_estimator(params=None):
"""Returns the glyphs DNNClassifier estimator.
Args:
params: Optional hyperparameters, defaulting to command-line values.
Returns:
A DNNClassifier instance.
"""
params = params or get_flag_params()
if not params['layer_dims'] and params['activation_fn'] != 'sigmoid':
tf.logging.warning(
'activation_fn should be sigmoid for logistic regression. Got: %s',
params['activation_fn'])
activation_fn = getattr(tf.nn, params['activation_fn'])
estimator = tf.estimator.DNNClassifier(
params['layer_dims'],
feature_columns=[glyph_patches.create_patch_feature_column()],
weight_column=glyph_patches.WEIGHT_COLUMN_NAME,
n_classes=len(musicscore_pb2.Glyph.Type.keys()),
optimizer=tf.train.FtrlOptimizer(
learning_rate=params['learning_rate'],
l1_regularization_strength=params['l1_regularization_strength'],
l2_regularization_strength=params['l2_regularization_strength'],
),
activation_fn=activation_fn,
dropout=FLAGS.dropout,
model_dir=glyph_patches.FLAGS.model_dir,
)
return hyperparameters.estimator_with_saved_params(
tf.contrib.estimator.add_metrics(estimator, _custom_metrics), params)
def testSimpleModel(self):
learning_rate = np.float32(0.123)
params = {'learning_rate': learning_rate}
estimator = hyperparameters.estimator_with_saved_params(
tf.estimator.DNNClassifier(
hidden_units=[10],
feature_columns=[tf.feature_column.numeric_column('feature')]),
params)
with self.test_session():
# Build the estimator model.
estimator.model_fn(
features={'feature': tf.placeholder(tf.float32)},
labels=tf.placeholder(tf.float32),
mode='TRAIN',
config=None)
# We should be able to pull hyperparameters out of the TensorFlow graph.
# The entire graph will also be written to the saved model in training.
self.assertEqual(
learning_rate,
tf.get_default_graph().get_tensor_by_name(
'params/learning_rate:0').eval())