tf.keras.layers.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,
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 (
sparse: A boolean specifying whether the placeholder to be created is sparse.
tensor: Optional existing tensor to wrap into the
Inputlayer. If set, the layer will not create a placeholder tensor.
**kwargs: deprecated arguments support.
```python # 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: ```python x = Input(shape=(32,)) y = tf.square(x) ```
ValueError: in case of invalid arguments.