tf.io.gfile.walk

TensorFlow 1 version

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Input() is used to instantiate a Keras tensor.

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Main aliases

tf.keras.layers.Input

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.keras.Input, tf.compat.v1.keras.layers.Input

tf.keras.Input(
    shape=None, batch_size=None, name=None, dtype=None, sparse=False, tensor=None,
    ragged=False, **kwargs
)

Used in the notebooks

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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)

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.
`**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 (i.e. a placeholder). This symbolic tensor can be used with other TensorFlow ops, as such:

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

Raises
`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.

TensorFlow

tf

tf.audio

tf.autograph

tf.bitwise

tf.compat

tf.config

tf.data

tf.debugging

tf.distribute

tf.dtypes

tf.errors

tf.estimator

tf.experimental

tf.feature_column

tf.graph_util

tf.image

tf.initializers

tf.io

tf.keras

tf.linalg

tf.lite

tf.lookup

tf.losses

tf.math

tf.metrics

tf.nest

tf.nn

tf.optimizers

tf.quantization

tf.queue

tf.ragged

tf.random

tf.raw_ops

tf.saved_model

tf.sets

tf.signal

tf.sparse

tf.strings

tf.summary

tf.sysconfig

tf.test

tf.tpu

tf.train

tf.version

tf.xla

tf.io / gfile / gfile.walk

View aliases

Used in the notebooks

Arguments

Returns

Example:

Raises