Mod_SoftmaxCrossEntropyWithLogits

chapter04/alexnet/main.py

@@ -83,7 +83,7 @@ if __name__ == "__main__":
     context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
 
     network = AlexNet(cfg.num_classes)
-    loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean")
+    loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
     repeat_size = 1
     # when batch_size=32, steps is 1562
     lr = Tensor(get_lr(0, cfg.learning_rate, cfg.epoch_size, 1562))

chapter05/resnet/resnet_cifar.py

@@ -119,7 +119,7 @@ if __name__ == '__main__':
 
     epoch_size = args_opt.epoch_size
     net = resnet50(args_opt.num_classes)
-    ls = SoftmaxCrossEntropyWithLogits(sparse=True, is_grad=False, reduction="mean")
+    ls = SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
     opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.01, 0.9)
 
     model = Model(net, loss_fn=ls, optimizer=opt, metrics={'acc'})

chapter06/lstm/eval.py

@@ -64,7 +64,7 @@ if __name__ == '__main__':
                            weight=Tensor(embedding_table),
                            batch_size=cfg.batch_size)
 
-    loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True)
+    loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
     opt = nn.Momentum(network.trainable_params(), cfg.learning_rate, cfg.momentum)
     loss_cb = LossMonitor()
 

chapter06/lstm/train.py

@@ -70,7 +70,7 @@ if __name__ == '__main__':
     if args.pre_trained:
         load_param_into_net(network, load_checkpoint(args.pre_trained))
 
-    loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True)
+    loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
     opt = nn.Momentum(network.trainable_params(), cfg.learning_rate, cfg.momentum)
     loss_cb = LossMonitor()