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Input() is used to instantiate a Keras tensor.

A Keras tensor is a TensorFlow symbolic tensor object, 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)

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. Elements of this tuple can be None; 'None' elements represent dimensions where the shape is not known.
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. Only one of 'ragged' and 'sparse' can be True.
tensor Optional existing tensor to wrap into the Input layer. If set, the layer will not create a placeholder tensor.
ragged A boolean specifying whether the placeholder to be created is ragged. Only one of 'ragged' and 'sparse' can be True. In this case, values of 'None' in the 'shape' argument represent ragged dimensions. For more information about RaggedTensors, see
**kwargs deprecated arguments support. Supports batch_shape and batch_input_shape.

A tensor.


# 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:

x = Input(shape=(32,))
y = tf.square(x)

ValueError If both sparse and ragged are provided.
ValueError If both shape and (batch_input_shape or batch_shape) are provided.
ValueError If both shape and tensor are None.
ValueError if any unrecognized parameters are provided.