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tf.contrib.layers.stack
Builds a stack of layers by applying layer repeatedly using stack_args.
tf.contrib.layers.stack(
inputs, layer, stack_args, **kwargs
)
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')
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 |
A Tensor suitable 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. |
© 2020 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/contrib/layers/stack