def placeholder_inputs(batch_size):
    'Generate placeholder variables to represent the input tensors.\n    These placeholders are used as inputs by the rest of the model building\n    code and will be fed from the downloaded data in the .run() loop, below.\n    Args:\n        batch_size: The batch size will be baked into both placeholders.\n    Returns:\n        images_placeholder: Images placeholder.\n        labels_placeholder: Labels placeholder.\n    '
    images_placeholder = tf.placeholder(tf.float32, shape=(batch_size, c3d_model.NUM_FRAMES_PER_CLIP, c3d_model.CROP_SIZE, c3d_model.CROP_SIZE, c3d_model.CHANNELS))
    labels_placeholder = tf.placeholder(tf.int64, shape=batch_size)
    return (images_placeholder, labels_placeholder)
Generate placeholder variables to represent the input tensors.
These placeholders are used as inputs by the rest of the model building
code and will be fed from the downloaded data in the .run() loop, below.
Args:
    batch_size: The batch size will be baked into both placeholders.
Returns:
    images_placeholder: Images placeholder.
    labels_placeholder: Labels placeholder.
def placeholder_inputs(batch_size):
    'Generate placeholder variables to represent the input tensors.\n    These placeholders are used as inputs by the rest of the model building\n    code and will be fed from the downloaded data in the .run() loop, below.\n    Args:\n        batch_size: The batch size will be baked into both placeholders.\n    Returns:\n        images_placeholder: Images placeholder.\n        labels_placeholder: Labels placeholder.\n    '
    images_placeholder = tf.placeholder(tf.float32, shape=(batch_size, c3d_model.NUM_FRAMES_PER_CLIP, c3d_model.CROP_SIZE, c3d_model.CROP_SIZE, c3d_model.CHANNELS))
    labels_placeholder = tf.placeholder(tf.int64, shape=batch_size)
    return (images_placeholder, labels_placeholder)