tf.keras.layers.InputLayer

Layer to be used as an entry point into a Network (a graph of layers).

Inherits From: Layer, Module

tf.keras.layers.InputLayer(
    input_shape=None,
    batch_size=None,
    dtype=None,
    input_tensor=None,
    sparse=None,
    name=None,
    ragged=None,
    type_spec=None,
    **kwargs
)

It can either wrap an existing tensor (pass an input_tensor argument) or create a placeholder tensor (pass arguments input_shape, and optionally, dtype).

It is generally recommend to use the Keras Functional model via Input, (which creates an InputLayer) without directly using InputLayer.

When using InputLayer with the Keras Sequential model, it can be skipped by moving the input_shape parameter to the first layer after the InputLayer.

This class can create placeholders for tf.Tensors, tf.SparseTensors, and tf.RaggedTensors by choosing sparse=True or ragged=True. Note that sparse and ragged can't be configured to True at the same time. Usage:

# With explicit InputLayer.
model = tf.keras.Sequential([
  tf.keras.layers.InputLayer(input_shape=(4,)),
  tf.keras.layers.Dense(8)])
model.compile(tf.keras.optimizers.RMSprop(0.001), loss='mse')
model.fit(np.zeros((10, 4)),
          np.ones((10, 8)))

# Without InputLayer and let the first layer to have the input_shape.
# Keras will add a input for the model behind the scene.
model = tf.keras.Sequential([
  tf.keras.layers.Dense(8, input_shape=(4,))])
model.compile(tf.keras.optimizers.RMSprop(0.001), loss='mse')
model.fit(np.zeros((10, 4)),
          np.ones((10, 8)))

Args
`input_shape`	Shape tuple (not including the batch axis), or `TensorShape` instance (not including the batch axis).
`batch_size`	Optional input batch size (integer or `None`).
`dtype`	Optional datatype of the input. When not provided, the Keras default `float` type will be used.
`input_tensor`	Optional tensor to use as layer input. If set, the layer will use the `tf.TypeSpec` of this tensor rather than creating a new placeholder tensor.
`sparse`	Boolean, whether the placeholder created is meant to be sparse. Defaults to `False`.
`ragged`	Boolean, whether the placeholder created is meant to be ragged. In this case, values of `None` in the `shape` argument represent ragged dimensions. For more information about `tf.RaggedTensor`, see this guide. Defaults to `False`.
`type_spec`	A `tf.TypeSpec` object to create Input from. This `tf.TypeSpec` represents the entire batch. When provided, all other args except name must be `None`.
`name`	Optional name of the layer (string).

tf.keras.layers.InputLayer

Args