Input
functionkeras.Input(
shape=None,
batch_size=None,
dtype=None,
sparse=None,
batch_shape=None,
name=None,
tensor=None,
optional=False,
)
Used to instantiate a Keras tensor.
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)
Arguments
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)."float32"
, "int32"
...)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
.None
objects),
including the batch size.Input
layer.
If set, the layer will use this tensor rather
than creating a new placeholder tensor.None
values.Returns
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)