Function Input

tensorflow/python/keras/engine/input_layer.py:163–273

`Input()` is used to instantiate a Keras tensor. A Keras tensor is a tensor object from the underlying backend (Theano or TensorFlow), which we augment with certain attributes that allow us to build a Keras model just by knowing the inputs and outputs of the model. For instance, if a, b

(  # pylint: disable=invalid-name
    shape=None,
    batch_size=None,
    name=None,
    dtype=None,
    sparse=False,
    tensor=None,
    ragged=False,
    **kwargs)

@keras_export('keras.layers.Input', 'keras.Input')
def Input(  # pylint: disable=invalid-name
    shape=None,
    batch_size=None,
    name=None,
    dtype=None,
    sparse=False,
    tensor=None,
    ragged=False,
    **kwargs):
  """`Input()` is used to instantiate a Keras tensor.

  A Keras tensor is a tensor object from the underlying backend
  (Theano or TensorFlow), which we augment with certain
  attributes that allow us to build a Keras model
  just by knowing the inputs and outputs of the model.

  For instance, if a, b and c are Keras tensors,
  it becomes possible to do:
  `model = Model(input=[a, b], output=c)`

  The added Keras attribute is:
      `_keras_history`: Last layer applied to the tensor.
          the entire layer graph is retrievable from that layer,
          recursively.

  Arguments:
      shape: A shape tuple (integers), not including the batch size.
          For instance, `shape=(32,)` indicates that the expected input
          will be batches of 32-dimensional vectors. Elements of this tuple
          can be None; 'None' elements represent dimensions where the shape is
          not known.
      batch_size: optional static batch size (integer).
      name: An optional name string for the layer.
          Should be unique in a model (do not reuse the same name twice).
          It will be autogenerated if it isn't provided.
      dtype: The data type expected by the input, as a string
          (`float32`, `float64`, `int32`...)
      sparse: A boolean specifying whether the placeholder to be created is
          sparse. Only one of 'ragged' and 'sparse' can be True.
      tensor: Optional existing tensor to wrap into the `Input` layer.
          If set, the layer will not create a placeholder tensor.
      ragged: A boolean specifying whether the placeholder to be created is
          ragged. Only one of 'ragged' and 'sparse' can be True. In this case,
          values of 'None' in the 'shape' argument represent ragged dimensions.
          For more information about RaggedTensors, see
          https://www.tensorflow.org/guide/ragged_tensors.
      **kwargs: deprecated arguments support. Supports `batch_shape` and
          `batch_input_shape`.

  Returns:
    A `tensor`.

  Example:

  ```python
  # this is a logistic regression in Keras
  x = Input(shape=(32,))
  y = Dense(16, activation='softmax')(x)