tf.keras.Input
tf.keras.layers.Input
tf.keras.Input( shape=None, batch_size=None, name=None, dtype=None, sparse=False, tensor=None, **kwargs )
Defined in tensorflow/python/keras/_impl/keras/engine/input_layer.py
.
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.
Example:
```python # this is a logistic regression in Keras x = Input(shape=(32,)) y = Dense(16, activation='softmax')(x) model = Model(x, y) ```
ValueError
: in case of invalid arguments.
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Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/keras/Input