Class GradientDef

java.lang.Object
com.google.protobuf.AbstractMessageLite
com.google.protobuf.AbstractMessage
com.google.protobuf.GeneratedMessage
org.tensorflow.proto.GradientDef
All Implemented Interfaces:
Message, MessageLite, MessageLiteOrBuilder, MessageOrBuilder, Serializable, GradientDefOrBuilder
@Generated public final class GradientDef extends GeneratedMessage implements GradientDefOrBuilder
GradientDef defines the gradient function of a function defined in
a function library.

A gradient function g (specified by gradient_func) for a function f
(specified by function_name) must follow the following:

The function 'f' must be a numerical function which takes N inputs
and produces M outputs. Its gradient function 'g', which is a
function taking N + M inputs and produces N outputs.

I.e. if we have
(y1, y2, ..., y_M) = f(x1, x2, ..., x_N),
then, g is
(dL/dx1, dL/dx2, ..., dL/dx_N) = g(x1, x2, ..., x_N,
dL/dy1, dL/dy2, ..., dL/dy_M),
where L is a scalar-value function of (x1, x2, ..., xN) (e.g., the
loss function). dL/dx_i is the partial derivative of L with respect
to x_i.
Protobuf type tensorflow.GradientDef
See Also: