tf.keras.Input

Used to instantiate a Keras tensor.

tf.keras.Input(
    shape=None,
    batch_size=None,
    dtype=None,
    sparse=None,
    batch_shape=None,
    name=None,
    tensor=None
)

Used in the notebooks

Used in the guide Used in the tutorials

A Keras tensor is a symbolic tensor-like 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 (tuple of integers or None objects), 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 and may vary (e.g. sequence length).
batch_size Optional static batch size (integer).
dtype The data type expected by the input, as a string (e.g. "float32", "int32"...)
sparse A boolean specifying whether the expected input will be sparse tensors. Note that, if sparse is False, sparse tensors can still be passed into the input - they will be densified with a default value of 0. This feature is only supported with the TensorFlow backend. Defaults to False.
name 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.
tensor Optional existing tensor to wrap into the Input layer. If set, the layer will use this tensor rather than creating a new placeholder tensor.

A Keras tensor.

Example:

# This is a logistic regression in Keras
x = Input(shape=(32,))
y = Dense(16, activation='softmax')(x)
model = Model(x, y)