contrib.keras.layers.Input

tf.contrib.keras.layers.Input

tf.contrib.keras.layers.Input

Input(
    shape=None,
    batch_shape=None,
    name=None,
    dtype=K.floatx(),
    sparse=False,
    tensor=None
)

Defined in tensorflow/contrib/keras/python/keras/engine/topology.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 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.

Arguments:

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.

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)
```