tf.contrib.layers.stack( inputs, layer, stack_args, **kwargs )
Builds a stack of layers by applying layer repeatedly using stack_args.
stack allows you to repeatedly apply the same operation with different arguments
stack_args[i]. For each application of the layer,
stack creates a new scope appended with an increasing number. For example:
y = stack(x, fully_connected, [32, 64, 128], scope='fc') # It is equivalent to: x = fully_connected(x, 32, scope='fc/fc_1') x = fully_connected(x, 64, scope='fc/fc_2') y = fully_connected(x, 128, scope='fc/fc_3')
scope argument is not given in
kwargs, it is set to
functools.partial objects). If neither
func.__name__ is available, the layers are called with
Tensorsuitable for layer.
layer: A layer with arguments
(inputs, *args, **kwargs)
stack_args: A list/tuple of parameters for each call of layer.
**kwargs: Extra kwargs for the layer.
Tensor result of applying the stacked layers.
ValueError: If the op is unknown or wrong.
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Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.