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.
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
g_side_input: list of Tensors, side input to g. If not None, signature of g should be (Tensor, list
is_training: bool, whether to actually use the efficient backprop codepath.
y1, y2: tuple of float Tensors.
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Code samples licensed under the Apache 2.0 License.