Input object

[source]

Input function

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 TF-Keras tensor.

A TF-Keras tensor is a symbolic tensor-like object, which we augment with certain attributes that allow us to build a TF-Keras model just by knowing the inputs and outputs of the model.

For instance, if a, b and c are TF-Keras tensors, it becomes possible to do: model = Model(input=[a, b], output=c)

Arguments

• 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. 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.
• tensor: Optional existing tensor to wrap into the Input layer. If set, the layer will use the tf.TypeSpec of this tensor rather than creating a new 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 this guide.
• type_spec: A tf.TypeSpec object to create the input placeholder from. When provided, all other args except name must be None.
• **kwargs: deprecated arguments support. Supports batch_shape and batch_input_shape.

Returns

A tensor.

Example

# 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 tf.GradientTape).

However, the resulting model will not track any variables that were used as inputs to TensorFlow ops. All variable usages must happen within TF-Keras layers to make sure they will be tracked by the model's weights.

The TF-Keras Input can also create a placeholder from an arbitrary tf.TypeSpec, e.g:

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.

Raises

• ValueError: If both sparse and ragged are provided.
• ValueError: If both shape and (batch_input_shape or batch_shape) are provided.
• ValueError: If shape, tensor and type_spec are None.
• ValueError: If arguments besides type_spec are non-None while type_spec is passed.
• ValueError: if any unrecognized parameters are provided.