input_data(shape=None, placeholder=None, dtype=tf.float32, data_preprocessing=None, data_augmentation=None, name='InputData')
Input Data.
This layer is used for inputting (aka. feeding) data to a network.
A TensorFlow placeholder will be used if it is supplied,
otherwise a new placeholder will be created with the given shape.
Either a shape or placeholder must be provided, otherwise an
exception will be raised.
Furthermore, the placeholder is added to TensorFlow collections
so it can be retrieved using tf.get_collection(tf.GraphKeys.INPUTS)
as well as tf.GraphKeys.LAYER_TENSOR + '/' + name. Similarly for
the data preprocessing and augmentation objects which are stored in
the collections with tf.GraphKeys.DATA_PREP and tf.GraphKeys.DATA_AUG.
This allows other parts of TFLearn to easily retrieve and use these
objects by referencing these graph-keys.
Input:
List of `int` (Shape), to create a new placeholder.
Or
`Tensor` (Placeholder), to use an existing placeholder.
Output:
Placeholder Tensor with given shape.
Arguments:
shape: list of `int`. An array or tuple representing input data shape.
It is required if no placeholder is provided. First element should
be 'None' (representing batch size), if not provided, it will be
added automatically.
placeholder: A Placeholder to use for feeding this layer (optional).
If not specified, a placeholder will be automatically created.
You can retrieve that placeholder through graph key: 'INPUTS',
or the 'placeholder' attribute of this function's returned tensor.
dtype: `tf.type`, Placeholder data type (optional). Default: float32.
data_preprocessing: A `DataPreprocessing` subclass object to manage
real-time data pre-processing when training and predicting (such
as zero center data, std normalization...).
data_augmentation: `DataAugmentation`. A `DataAugmentation` subclass
object to manage real-time data augmentation while training (
such as random image crop, random image flip, random sequence
reverse...).
name: `str`. A name for this layer (optional).
