Python tensorflow.parse_csv() Examples

The following are 9 code examples of tensorflow.parse_csv(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module tensorflow , or try the search function .
Example #1
Source File: iris_data.py    From Deep-Learning-with-TensorFlow-Second-Edition with MIT License 6 votes vote down vote up 
def eval_input_fn(features, labels, batch_size):
    """An input function for evaluation or prediction"""
    features=dict(features)
    if labels is None:
        # No labels, use only features.
        inputs = features
    else:
        inputs = (features, labels)

    # Convert the inputs to a Dataset.
    dataset = tf.data.Dataset.from_tensor_slices(inputs)

    # Batch the examples
    assert batch_size is not None, "batch_size must not be None"
    dataset = dataset.batch(batch_size)

    # Return the dataset.
    return dataset


# The remainder of this file contains a simple example of a csv parser,
#     implemented using a the `Dataset` class.

# `tf.parse_csv` sets the types of the outputs to match the examples given in
#     the `record_defaults` argument.
Example #2
Source File: iris_data.py    From yolo_v2 with Apache License 2.0 6 votes vote down vote up 
def eval_input_fn(features, labels, batch_size):
    """An input function for evaluation or prediction"""
    features=dict(features)
    if labels is None:
        # No labels, use only features.
        inputs = features
    else:
        inputs = (features, labels)

    # Convert the inputs to a Dataset.
    dataset = tf.data.Dataset.from_tensor_slices(inputs)

    # Batch the examples
    assert batch_size is not None, "batch_size must not be None"
    dataset = dataset.batch(batch_size)

    # Return the read end of the pipeline.
    return dataset.make_one_shot_iterator().get_next()


# The remainder of this file contains a simple example of a csv parser,
#     implemented using a the `Dataset` class.

# `tf.parse_csv` sets the types of the outputs to match the examples given in
#     the `record_defaults` argument.
Example #3
Source File: iris_data.py    From Gun-Detector with Apache License 2.0 6 votes vote down vote up 
def eval_input_fn(features, labels, batch_size):
    """An input function for evaluation or prediction"""
    features=dict(features)
    if labels is None:
        # No labels, use only features.
        inputs = features
    else:
        inputs = (features, labels)

    # Convert the inputs to a Dataset.
    dataset = tf.data.Dataset.from_tensor_slices(inputs)

    # Batch the examples
    assert batch_size is not None, "batch_size must not be None"
    dataset = dataset.batch(batch_size)

    # Return the dataset.
    return dataset


# The remainder of this file contains a simple example of a csv parser,
#     implemented using a the `Dataset` class.

# `tf.parse_csv` sets the types of the outputs to match the examples given in
#     the `record_defaults` argument.
Example #4
Source File: iris_data.py    From multithreaded-estimators with Apache License 2.0 6 votes vote down vote up 
def load_data(y_name='Species'):
    """Returns the iris dataset as (train_x, train_y), (test_x, test_y)."""
    train_path, test_path = maybe_download()

    train = pd.read_csv(train_path, names=CSV_COLUMN_NAMES, header=0)
    train_x, train_y = train, train.pop(y_name)

    test = pd.read_csv(test_path, names=CSV_COLUMN_NAMES, header=0)
    test_x, test_y = test, test.pop(y_name)

    return (train_x, train_y), (test_x, test_y)


# The remainder of this file contains a simple example of a csv parser,
#     implemented using a the `Dataset` class.

# `tf.parse_csv` sets the types of the outputs to match the examples given in
#     the `record_defaults` argument.
Example #5
Source File: iris_data.py    From Live-feed-object-device-identification-using-Tensorflow-and-OpenCV with Apache License 2.0 6 votes vote down vote up 
def eval_input_fn(features, labels, batch_size):
    """An input function for evaluation or prediction"""
    features=dict(features)
    if labels is None:
        # No labels, use only features.
        inputs = features
    else:
        inputs = (features, labels)

    # Convert the inputs to a Dataset.
    dataset = tf.data.Dataset.from_tensor_slices(inputs)

    # Batch the examples
    assert batch_size is not None, "batch_size must not be None"
    dataset = dataset.batch(batch_size)

    # Return the dataset.
    return dataset


# The remainder of this file contains a simple example of a csv parser,
#     implemented using the `Dataset` class.

# `tf.parse_csv` sets the types of the outputs to match the examples given in
#     the `record_defaults` argument.
Example #6
Source File: iris_data.py    From auptimizer with GNU General Public License v3.0 6 votes vote down vote up 
def eval_input_fn(features, labels, batch_size):
    """An input function for evaluation or prediction"""
    features=dict(features)
    if labels is None:
        # No labels, use only features.
        inputs = features
    else:
        inputs = (features, labels)

    # Convert the inputs to a Dataset.
    dataset = tf.data.Dataset.from_tensor_slices(inputs)

    # Batch the examples
    assert batch_size is not None, "batch_size must not be None"
    dataset = dataset.batch(batch_size)

    # Return the dataset.
    return dataset


# The remainder of this file contains a simple example of a csv parser,
#     implemented using the `Dataset` class.

# `tf.parse_csv` sets the types of the outputs to match the examples given in
#     the `record_defaults` argument.
Example #7
Source File: iris_data_utils.py    From mlflow with Apache License 2.0 6 votes vote down vote up 
def eval_input_fn(features, labels, batch_size):
    """An input function for evaluation or prediction"""
    features = dict(features)
    if labels is None:
        # No labels, use only features.
        inputs = features
    else:
        inputs = (features, labels)

    # Convert the inputs to a Dataset.
    dataset = tf.data.Dataset.from_tensor_slices(inputs)

    # Batch the examples
    assert batch_size is not None, "batch_size must not be None"
    dataset = dataset.batch(batch_size)

    # Return the dataset.
    return dataset


# The remainder of this file contains a simple example of a csv parser,
#     implemented using the `Dataset` class.

# `tf.parse_csv` sets the types of the outputs to match the examples given in
#     the `record_defaults` argument.
Example #8
Source File: iris_data.py    From object_detection_with_tensorflow with MIT License 6 votes vote down vote up 
def eval_input_fn(features, labels, batch_size):
    """An input function for evaluation or prediction"""
    features=dict(features)
    if labels is None:
        # No labels, use only features.
        inputs = features
    else:
        inputs = (features, labels)

    # Convert the inputs to a Dataset.
    dataset = tf.data.Dataset.from_tensor_slices(inputs)

    # Batch the examples
    assert batch_size is not None, "batch_size must not be None"
    dataset = dataset.batch(batch_size)

    # Return the read end of the pipeline.
    return dataset.make_one_shot_iterator().get_next()


# The remainder of this file contains a simple example of a csv parser,
#     implemented using a the `Dataset` class.

# `tf.parse_csv` sets the types of the outputs to match the examples given in
#     the `record_defaults` argument.
Example #9
Source File: iris_data.py    From g-tensorflow-models with Apache License 2.0 6 votes vote down vote up 
def eval_input_fn(features, labels, batch_size):
    """An input function for evaluation or prediction"""
    features=dict(features)
    if labels is None:
        # No labels, use only features.
        inputs = features
    else:
        inputs = (features, labels)

    # Convert the inputs to a Dataset.
    dataset = tf.data.Dataset.from_tensor_slices(inputs)

    # Batch the examples
    assert batch_size is not None, "batch_size must not be None"
    dataset = dataset.batch(batch_size)

    # Return the dataset.
    return dataset


# The remainder of this file contains a simple example of a csv parser,
#     implemented using the `Dataset` class.

# `tf.parse_csv` sets the types of the outputs to match the examples given in
#     the `record_defaults` argument.