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 and 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 (integer), not including the batch size. For instance, `shape=(32,)` indicates that the expected input will be batches of 32-dimensional vectors. batch_shape: A shape tuple (integer), including the batch size. For instance, `batch_shape=(10, 32)` indicates that the expected input will be batches of 10 32-dimensional vectors. `batch_shape=(None, 32)` indicates batches of an arbitrary number of 32-dimensional vectors. 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.
```python # this is a logistic regression in Keras x = Input(shape=(32,)) y = Dense(16, activation='softmax')(x) model = Model(x, y) ```
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