# tf.keras.Input

### Aliases:

• tf.keras.Input
• tf.keras.layers.Input
tf.keras.Input(
shape=None,
batch_size=None,
name=None,
dtype=None,
sparse=False,
tensor=None,
**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.
• 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.

#### Returns:

A tensor.


Example:

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



#### Raises:

• ValueError: in case of invalid arguments.