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:
1 2 3 4 5 6 | 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') |
If the scope argument is not given in kwargs, it is set to layer.__name__, or layer.func.__name__ (for functools.partial objects). If neither __name__ nor func.__name__ is available, the layers are called with scope='stack'.
Args:
-
inputs: ATensorsuitable 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.
Returns:
a Tensor result of applying the stacked layers.
Raises:
-
ValueError: if the op is unknown or wrong.
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