Type ITowerOptimizer
Namespace tensorflow_estimator.contrib.estimator
Interfaces IOptimizer
Public instance methods
object apply_gradients(object grads_and_vars, object global_step, IDictionary<string, object> kwargs)
object compute_gradients(object loss, Object[] args)
Compute gradients of `loss` for the variables in `var_list`. This is the first part of `minimize()`. It returns a list
of (gradient, variable) pairs where "gradient" is the gradient
for "variable". Note that "gradient" can be a `Tensor`, an
`IndexedSlices`, or `None` if there is no gradient for the
given variable.
Parameters
-
objectloss - A Tensor containing the value to minimize or a callable taking no arguments which returns the value to minimize. When eager execution is enabled it must be a callable.
-
Object[]args
Returns
-
object - A list of (gradient, variable) pairs. Variable is always present, but gradient can be `None`.
object compute_gradients(object loss, IDictionary<string, object> kwargs, Object[] args)
Compute gradients of `loss` for the variables in `var_list`. This is the first part of `minimize()`. It returns a list
of (gradient, variable) pairs where "gradient" is the gradient
for "variable". Note that "gradient" can be a `Tensor`, an
`IndexedSlices`, or `None` if there is no gradient for the
given variable.
Parameters
-
objectloss - A Tensor containing the value to minimize or a callable taking no arguments which returns the value to minimize. When eager execution is enabled it must be a callable.
-
IDictionary<string, object>kwargs -
Object[]args
Returns
-
object - A list of (gradient, variable) pairs. Variable is always present, but gradient can be `None`.