tf.keras.layers.GRU

TensorFlow 1 version

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Layer to be used as an entry point into a Network (a graph of layers).

Inherits From: Layer, Module

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Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.keras.layers.InputLayer

tf.keras.layers.InputLayer(
    input_shape=None, batch_size=None, dtype=None, input_tensor=None, sparse=False,
    name=None, ragged=False, **kwargs
)

Used in the notebooks

Used in the guide	Used in the tutorials
Weight clustering in Keras example Pruning in Keras example Quantization aware training in Keras example	Convolutional Variational Autoencoder Post-training dynamic range quantization TFF for Federated Learning Research: Model and Update Compression Retraining an Image Classifier

It can either wrap an existing tensor (pass an input_tensor argument) or create a placeholder tensor (pass arguments input_shape, and optionally, dtype).

It is generally recommend to use the functional layer API via Input, (which creates an InputLayer) without directly using InputLayer.

When using InputLayer with Keras Sequential model, it can be skipped by moving the input_shape parameter to the first layer after the InputLayer.

This class can create placeholders for tf.Tensors, tf.SparseTensors, and tf.RaggedTensors by choosing 'sparse=True' or 'ragged=True'. Note that 'sparse' and 'ragged' can't be configured to True at same time. Usage:

# With explicit InputLayer.
model = tf.keras.Sequential([
  tf.keras.layers.InputLayer(input_shape=(4,)),
  tf.keras.layers.Dense(8)])
model.compile(tf.optimizers.RMSprop(0.001), loss='mse')
model.fit(np.zeros((10, 4)),
          np.ones((10, 8)))

# Without InputLayer and let the first layer to have the input_shape.
# Keras will add a input for the model behind the scene.
model = tf.keras.Sequential([
  tf.keras.layers.Dense(8, input_shape=(4,))])
model.compile(tf.optimizers.RMSprop(0.001), loss='mse')
model.fit(np.zeros((10, 4)),
          np.ones((10, 8)))

Arguments
`input_shape`	Shape tuple (not including the batch axis), or `TensorShape` instance (not including the batch axis).
`batch_size`	Optional input batch size (integer or None).
`dtype`	Optional datatype of the input. When not provided, the Keras default float type will be used.
`input_tensor`	Optional tensor to use as layer input. If set, the layer will use the `tf.TypeSpec` of this tensor rather than creating a new placeholder tensor.
`sparse`	Boolean, whether the placeholder created is meant to be sparse. Default to False.
`ragged`	Boolean, whether the placeholder created is meant to be ragged. In this case, values of 'None' in the 'shape' argument represent ragged dimensions. For more information about RaggedTensors, see this guide. Default to False.
`name`	Optional name of the layer (string).

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tf.keras / layers / layers.GRU

View aliases

Used in the notebooks

Arguments