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Rename TF input helper functions. #323

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Rename TF input helper functions. #323

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14 changes: 9 additions & 5 deletions sagemaker-python-sdk/tensorflow_abalone_age_predictor_using_keras/abalone.py
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Original file line number Diff line number Diff line change
Expand Up @@ -71,19 +71,23 @@ def serving_input_fn(params):


def train_input_fn(training_dir, params):
return _input_fn(training_dir, 'abalone_train.csv')
"""Returns training input data as a Tuple: (dict of `features`, `targets`)"""
return _get_numpy_data(training_dir, 'abalone_train.csv')


def eval_input_fn(training_dir, params):
return _input_fn(training_dir, 'abalone_test.csv')
"""Returns evaluation input data as a Tuple: (dict of `features`, `targets`)"""
return _get_numpy_data(training_dir, 'abalone_test.csv')


def _input_fn(training_dir, training_filename):
def _get_numpy_data(training_dir, training_filename):
training_set = tf.contrib.learn.datasets.base.load_csv_without_header(
filename=os.path.join(training_dir, training_filename), target_dtype=np.int, features_dtype=np.float32)

return tf.estimator.inputs.numpy_input_fn(
numpy_input_fn = tf.estimator.inputs.numpy_input_fn(
x={INPUT_TENSOR_NAME: np.array(training_set.data)},
y=np.array(training_set.target),
num_epochs=None,
shuffle=True)()
shuffle=True)

return numpy_input_fn()
12 changes: 8 additions & 4 deletions sagemaker-python-sdk/tensorflow_abalone_age_predictor_using_layers/abalone.py
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Original file line number Diff line number Diff line change
Expand Up @@ -58,19 +58,23 @@ def serving_input_fn(params):


def train_input_fn(training_dir, params):
return _input_fn(training_dir, 'abalone_train.csv')
"""Returns training input data as a Tuple: (dict of `features`, `targets`)"""
return _get_numpy_data(training_dir, 'abalone_train.csv')


def eval_input_fn(training_dir, params):
return _input_fn(training_dir, 'abalone_test.csv')
"""Returns evaluation input data as a Tuple: (dict of `features`, `targets`)"""
return _get_numpy_data(training_dir, 'abalone_test.csv')


def _input_fn(training_dir, training_filename):
def _get_numpy_data(training_dir, training_filename):
training_set = tf.contrib.learn.datasets.base.load_csv_without_header(
filename=os.path.join(training_dir, training_filename), target_dtype=np.int, features_dtype=np.float32)

return tf.estimator.inputs.numpy_input_fn(
numpy_input_fn = tf.estimator.inputs.numpy_input_fn(
x={INPUT_TENSOR_NAME: np.array(training_set.data)},
y=np.array(training_set.target),
num_epochs=None,
shuffle=True)()
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I think you need to remove the "()" at the end of shuffle=True)(). Otherwise, I get a TypeError: 'tuple' object is not callable error.


return numpy_input_fn()
6 changes: 3 additions & 3 deletions sagemaker-python-sdk/tensorflow_distributed_mnist/mnist.py
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Expand Up @@ -102,14 +102,14 @@ def read_and_decode(filename_queue):


def train_input_fn(training_dir, params):
return _input_fn(training_dir, 'train.tfrecords', batch_size=100)
return _read_tfrecord_data(training_dir, 'train.tfrecords', batch_size=100)


def eval_input_fn(training_dir, params):
return _input_fn(training_dir, 'test.tfrecords', batch_size=100)
return _read_tfrecord_data(training_dir, 'test.tfrecords', batch_size=100)


def _input_fn(training_dir, training_filename, batch_size=100):
def _read_tfrecord_data(training_dir, training_filename, batch_size=100):
test_file = os.path.join(training_dir, training_filename)
filename_queue = tf.train.string_input_producer([test_file])

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16 changes: 9 additions & 7 deletions sagemaker-python-sdk/tensorflow_iris_dnn_classifier_using_estimators/iris_dnn_classifier.py
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Original file line number Diff line number Diff line change
Expand Up @@ -19,23 +19,25 @@ def serving_input_fn(params):


def train_input_fn(training_dir, params):
"""Returns input function that would feed the model during training"""
return _generate_input_fn(training_dir, 'iris_training.csv')
"""Returns training input data as a Tuple: (dict of `features`, `targets`)"""
return _get_numpy_data(training_dir, 'iris_training.csv')


def eval_input_fn(training_dir, params):
"""Returns input function that would feed the model during evaluation"""
return _generate_input_fn(training_dir, 'iris_test.csv')
"""Returns evaluation input data as a Tuple: (dict of `features`, `targets`)"""
return _get_numpy_data(training_dir, 'iris_test.csv')


def _generate_input_fn(training_dir, training_filename):
def _get_numpy_data(training_dir, training_filename):
training_set = tf.contrib.learn.datasets.base.load_csv_with_header(
filename=os.path.join(training_dir, training_filename),
target_dtype=np.int,
features_dtype=np.float32)

return tf.estimator.inputs.numpy_input_fn(
numpy_input_fn = tf.estimator.inputs.numpy_input_fn(
x={INPUT_TENSOR_NAME: np.array(training_set.data)},
y=np.array(training_set.target),
num_epochs=None,
shuffle=True)()
shuffle=True)

return numpy_input_fn()