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# tf.contrib.layers.rev_block

```tf.contrib.layers.rev_block(
x1,
x2,
f,
g,
num_layers=1,
f_side_input=None,
g_side_input=None,
is_training=True
)
```

A block of reversible residual layers.

A reversible residual layer is defined as:

```y1 = x1 + f(x2, f_side_input)
y2 = x2 + g(y1, g_side_input)
```

A reversible residual block, defined here, is a series of reversible residual layers.

Limitations: f and g must not close over any Tensors; all side inputs to f and g should be passed in with f_side_input and g_side_input which will be forwarded to f and g. f and g must not change the dimensionality of their inputs in order for the addition in the equations above to work.

#### Args:

• `x1`: a float Tensor.
• `x2`: a float Tensor.
• `f`: a function, (Tensor) -> (Tensor) (or list of such of length num_layers). Should not change the shape of the Tensor. Can make calls to get_variable. See f_side_input if there are side inputs.
• `g`: a function, (Tensor) -> (Tensor) (or list of such of length num_layers). Should not change the shape of the Tensor. Can make calls to get_variable. See g_side_input if there are side inputs.
• `num_layers`: int, number of reversible residual layers. Each layer will apply f and g according to the equations above, with new variables in each layer.
• `f_side_input`: list of Tensors, side input to f. If not None, signature of f should be (Tensor, list) -> (Tensor).
• `g_side_input`: list of Tensors, side input to g. If not None, signature of g should be (Tensor, list) -> (Tensor).
• `is_training`: bool, whether to actually use the efficient backprop codepath.

#### Returns:

y1, y2: tuple of float Tensors.

© 2018 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/api_docs/python/tf/contrib/layers/rev_block