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  • tf.keras.Input
  • tf.keras.layers.Input

Defined in tensorflow/python/keras/engine/

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.


  • 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.
  • 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.
  • tensor: Optional existing tensor to wrap into the Input layer. If set, the layer will not create a placeholder tensor.
  • **kwargs: deprecated arguments support.


A tensor.


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

Note that even if eager execution is enabled,
`Input` produces a symbolic tensor (i.e. a placeholder).
This symbolic tensor can be used with other
TensorFlow ops, as such:

x = Input(shape=(32,))
y = tf.square(x)


  • ValueError: in case of invalid arguments.