tf.keras.Input( shape=None, batch_size=None, name=None, dtype=None, sparse=None, tensor=None, ragged=None, type_spec=None, **kwargs )
Input() is 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
c are Keras tensors,
it becomes possible to do:
model = Model(input=[a, b], output=c)
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.
sparseis False, sparse tensors can still be passed into the input - they will be densified with a default value of 0.
Inputlayer. If set, the layer will use the
tf.TypeSpecof this tensor rather than creating a new placeholder tensor.
tf.TypeSpecobject to create the input placeholder from. When provided, all other args except name must be None.
# 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-like object (i.e. a placeholder).
This symbolic tensor-like object can be used with lower-level
TensorFlow ops that take tensors as inputs, as such:
x = Input(shape=(32,)) y = tf.square(x) # This op will be treated like a layer model = Model(x, y)
(This behavior does not work for higher-order TensorFlow APIs such as
control flow and being directly watched by a
However, the resulting model will not track any variables that were used as inputs to TensorFlow ops. All variable usages must happen within Keras layers to make sure they will be tracked by the model's weights.
The Keras Input can also create a placeholder from an arbitrary
x = Input(type_spec=tf.RaggedTensorSpec(shape=[None, None], dtype=tf.float32, ragged_rank=1)) y = x.values model = Model(x, y)
When passing an arbitrary
tf.TypeSpec, it must represent the signature of
an entire batch instead of just one example.
batch_shape) are provided.
type_specare non-None while