public static Tensor[] fused_batch_norm_grad_v3(FusedBatchNormParams @params)
=> tf.Context.ExecuteOp("FusedBatchNormGradV3", @params.Name,
new ExecuteOpArgs(@params.YBackprop,
@params.X,
@params.Scale,
@params.ReserveSpace1,
@params.ReserveSpace2,
@params.ReserveSpace3)
.SetAttributes(new
{
epsilon = @params.Epsilon,
data_format = @params.DataFormat,
is_training = @params.IsTraining
}));
/// <summary>
/// Gradient for batch normalization.
/// </summary>
/// <param name="params"></param>
/// <returns></returns>
public static Tensor[] fused_batch_norm_grad(FusedBatchNormParams @params)
{
var op = tf.OpDefLib._apply_op_helper("FusedBatchNormGrad", name: @params.Name, args: new
{
y_backprop = @params.YBackprop,
x = @params.X,
scale = @params.Scale,
reserve_space_1 = @params.ReserveSpace1,
reserve_space_2 = @params.ReserveSpace2,
epsilon = @params.Epsilon,
data_format = @params.DataFormat,
is_training = @params.IsTraining
});
return(op.outputs);
}
public static Tensor[] fused_batch_norm_grad_v3(FusedBatchNormParams @params)
=> tf.Context.RunInAutoMode(()
=> tf.OpDefLib._apply_op_helper("FusedBatchNormGradV3", name: @params.Name,
args: new
{
y_backprop = @params.YBackprop,
x = @params.X,
scale = @params.Scale,
reserve_space_1 = @params.ReserveSpace1,
reserve_space_2 = @params.ReserveSpace2,
reserve_space_3 = @params.ReserveSpace3,
epsilon = @params.Epsilon,
data_format = @params.DataFormat,
is_training = @params.IsTraining
}).outputs, ()
=> tf.Runner.TFE_FastPathExecute(tf.Context, tf.Context.DeviceName,
"FusedBatchNormGradV3", @params.Name,
null,
@params.YBackprop, @params.X, @params.Scale,
@params.ReserveSpace1, @params.ReserveSpace2, @params.ReserveSpace3,
"epsilon", @params.Epsilon,
"data_format", @params.DataFormat,
"is_training", @params.IsTraining),
@params.YBackprop);
