Mod_SoftmaxCrossEntropyWithLogits

docs/source_en/operator_list.md

@@ -67,7 +67,7 @@
 | [mindspore.nn.L1Loss](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.nn.html#mindspore.nn.L1Loss)                                                |Supported |Supported | Doing |loss/loss
 | [mindspore.nn.MSELoss](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.nn.html#mindspore.nn.MSELoss)                                              |  Supported |Doing | Doing |loss/loss
 | [mindspore.nn.SmoothL1Loss](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.nn.html#mindspore.nn.SmoothL1Loss)                                    |Supported |Doing | Doing |loss/loss
-| [mindspore.nn.SoftmaxCrossEntropyWithLogits](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.nn.html#mindspore.nn.SoftmaxCrossEntropyWithLogits)  |  Supported |  Supported |   Doing |loss/loss
+| [mindspore.nn.SoftmaxCrossEntropyWithLogits](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.nn.html#mindspore.nn.SoftmaxCrossEntropyWithLogits)  |  Supported |  Supported |   Supported |loss/loss
 | [mindspore.nn.SoftmaxCrossEntropyExpand](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.nn.html#mindspore.nn.SoftmaxCrossEntropyExpand)     |  Supported |Supported | Doing |loss/loss
 | [mindspore.nn.CosineEmbeddingLoss](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.nn.html#mindspore.nn.CosineEmbeddingLoss)                                                |Supported |Supported | Doing |loss/loss
 | [mindspore.nn.ProximalAdagrad](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.nn.html#mindspore.nn.ProximalAdagrad)                              | Supported | Doing | Doing |optim/ProximalAdagrad
@@ -128,7 +128,7 @@
 | [mindspore.ops.operations.Conv2DBackpropInput](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.Conv2DBackpropInput)     |  Supported | Supported    |Doing | nn_ops
 | [mindspore.ops.operations.BiasAdd](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.BiasAdd)                             |    Supported | Supported    |  Supported | nn_ops
 | [mindspore.ops.operations.TopK](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.TopK)                                   |    Supported | Supported  |Doing | nn_ops
-| [mindspore.ops.operations.SoftmaxCrossEntropyWithLogits](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.SoftmaxCrossEntropyWithLogits) |  Supported | Supported  |Doing | nn_ops
+| [mindspore.ops.operations.SoftmaxCrossEntropyWithLogits](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.SoftmaxCrossEntropyWithLogits) |  Supported | Supported  |Supported | nn_ops
 | [mindspore.ops.operations.SparseSoftmaxCrossEntropyWithLogits](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.SparseSoftmaxCrossEntropyWithLogits) |  Doing   | Supported  |  Supported | nn_ops
 | [mindspore.ops.operations.ApplyMomentum](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.ApplyMomentum)                 |    Supported  | Supported    |   Supported | nn_ops
 | [mindspore.ops.operations.ApplyAddSign](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.ApplyAddSign)                 |    Supported  | Doing    |   Doing | nn_ops

docs/source_zh_cn/operator_list.md

@@ -67,7 +67,7 @@
 | [mindspore.nn.L1Loss](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.nn.html#mindspore.nn.L1Loss)                                                |Supported |Supported | Doing |loss/loss
 | [mindspore.nn.MSELoss](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.nn.html#mindspore.nn.MSELoss)                                              |  Supported |Doing | Doing |loss/loss
 | [mindspore.nn.SmoothL1Loss](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.nn.html#mindspore.nn.SmoothL1Loss)                                    | Supported |Doing | Doing |loss/loss
-| [mindspore.nn.SoftmaxCrossEntropyWithLogits](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.nn.html#mindspore.nn.SoftmaxCrossEntropyWithLogits)  |  Supported |  Supported |   Doing |loss/loss
+| [mindspore.nn.SoftmaxCrossEntropyWithLogits](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.nn.html#mindspore.nn.SoftmaxCrossEntropyWithLogits)  |  Supported |  Supported |   Supported |loss/loss
 | [mindspore.nn.SoftmaxCrossEntropyExpand](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.nn.html#mindspore.nn.SoftmaxCrossEntropyExpand)          |  Supported |Supported | Doing |loss/loss
 | [mindspore.nn.CosineEmbeddingLoss](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.nn.html#mindspore.nn.CosineEmbeddingLoss)                                                |Supported |Supported | Doing |loss/loss
 | [mindspore.nn.ProximalAdagrad](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.nn.html#mindspore.nn.ProximalAdagrad)                              |  Supported |Doing | Doing |optim/ProximalAdagrad
@@ -128,7 +128,7 @@
 | [mindspore.ops.operations.Conv2DBackpropInput](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.Conv2DBackpropInput)     |  Supported | Supported    |Doing | nn_ops
 | [mindspore.ops.operations.BiasAdd](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.BiasAdd)                             |    Supported | Supported    |  Supported | nn_ops
 | [mindspore.ops.operations.TopK](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.TopK)                                   |    Supported | Supported  |Doing | nn_ops
-| [mindspore.ops.operations.SoftmaxCrossEntropyWithLogits](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.SoftmaxCrossEntropyWithLogits) |  Supported | Supported  |Doing | nn_ops
+| [mindspore.ops.operations.SoftmaxCrossEntropyWithLogits](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.SoftmaxCrossEntropyWithLogits) |  Supported | Supported  |Supported | nn_ops
 | [mindspore.ops.operations.SparseSoftmaxCrossEntropyWithLogits](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.SparseSoftmaxCrossEntropyWithLogits) |  Doing   | Supported  |  Supported | nn_ops
 | [mindspore.ops.operations.ApplyMomentum](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.ApplyMomentum)                 |    Supported  | Supported    |   Supported | nn_ops
 | [mindspore.ops.operations.ApplyAddSign](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.ApplyAddSign)                 |    Supported  | Doing    |   Doing | nn_ops

tutorials/notebook/computer_vision_application.ipynb

@@ -387,7 +387,7 @@
     "from mindspore.nn.optim.momentum import Momentum\n",
     "from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits\n",
     "\n",
-    "ls = SoftmaxCrossEntropyWithLogits(sparse=True, is_grad=False, reduction=\"mean\")\n",
+    "ls = SoftmaxCrossEntropyWithLogits(sparse=True, reduction=\"mean\")\n",
     "opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.01, 0.9)"
    ]
   },

tutorials/notebook/customized_debugging_information.ipynb

@@ -386,7 +386,7 @@
     "train_data_path = \"./MNIST_Data/train\"\n",
     "eval_data_path = \"./MNIST_Data/train\"\n",
     "\n",
-    "net_loss = SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction='mean')\n",
+    "net_loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction=\"mean\")\n",
     "repeat_size = epoch_size\n",
     "network = LeNet5()\n",
     "\n",

tutorials/notebook/debugging_in_pynative_mode.ipynb

@@ -488,7 +488,7 @@
     "\n",
     "net = LeNet5()\n",
     "optimizer = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.1, 0.9)\n",
-    "criterion = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True)\n",
+    "criterion = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')\n",
     "net_with_criterion = WithLossCell(net, criterion)\n",
     "train_network = GradWrap(net_with_criterion)\n",
     "train_network.set_train()\n",

tutorials/notebook/mindinsight/calculate_and_datagraphic.ipynb

@@ -311,7 +311,7 @@
     "    ds_train = create_dataset(data_path=\"./MNIST_Data/train/\")\n",
     "\n",
     "    network = LeNet5()\n",
-    "    net_loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction=\"mean\")\n",
+    "    net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction=\"mean\")\n",
     "    net_opt = nn.Momentum(network.trainable_params(), learning_rate=0.01, momentum=0.9)\n",
     "    time_cb = TimeMonitor(data_size=ds_train.get_dataset_size())\n",
     "    model = Model(network, net_loss, net_opt, metrics={\"Accuracy\": Accuracy()})\n",

tutorials/notebook/mindinsight/mindinsight_image_histogram_scalar_tensor.ipynb

@@ -544,7 +544,7 @@
    "source": [
     "\n",
     "network = AlexNet(num_classes=10)\n",
-    "net_loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction=\"mean\")\n",
+    "net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction=\"mean\")\n",
     "lr = Tensor(get_lr(0, 0.002, 10, ds_train.get_dataset_size()))\n",
     "net_opt = nn.Momentum(network.trainable_params(), learning_rate=lr, momentum=0.9)\n",
     "time_cb = TimeMonitor(data_size=ds_train.get_dataset_size())\n",
@@ -777,7 +777,7 @@
     "\n",
     "lr = Tensor(get_lr(0, 0.002, 10, ds_train.get_dataset_size()))\n",
     "network = AlexNet(num_classes=10)\n",
-    "net_loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction=\"mean\")\n",
+    "net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction=\"mean\")\n",
     "net_opt = nn.Momentum(network.trainable_params(), learning_rate=lr, momentum=0.9)\n",
     "time_cb = TimeMonitor(data_size=ds_train.get_dataset_size())\n",
     "config_ck = CheckpointConfig(save_checkpoint_steps=1562, keep_checkpoint_max=10)\n",
@@ -873,7 +873,7 @@
    "source": [
     "lr = Tensor(get_lr(0, 0.002, 1, ds_train.get_dataset_size()))\n",
     "network = AlexNet(num_classes=10)\n",
-    "net_loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction=\"mean\")\n",
+    "net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction=\"mean\")\n",
     "net_opt = nn.Momentum(network.trainable_params(), learning_rate=lr, momentum=0.9)\n",
     "time_cb = TimeMonitor(data_size=ds_train.get_dataset_size())\n",
     "config_ck = CheckpointConfig(save_checkpoint_steps=1562, keep_checkpoint_max=10)\n",
@@ -1017,7 +1017,7 @@
     "\n",
     "lr = Tensor(get_lr(0, 0.002, 1, ds_train.get_dataset_size()))\n",
     "network = AlexNet(num_classes=10)\n",
-    "net_loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction=\"mean\")\n",
+    "net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction=\"mean\")\n",
     "net_opt = nn.Momentum(network.trainable_params(), learning_rate=lr, momentum=0.9)\n",
     "time_cb = TimeMonitor(data_size=ds_train.get_dataset_size())\n",
     "config_ck = CheckpointConfig(save_checkpoint_steps=1562, keep_checkpoint_max=10)\n",
@@ -1153,7 +1153,7 @@
     "\n",
     "lr = Tensor(get_lr(0, 0.002, 1, ds_train.get_dataset_size()))\n",
     "network = AlexNet(num_classes=10)\n",
-    "net_loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction=\"mean\")\n",
+    "net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction=\"mean\")\n",
     "net_opt = nn.Momentum(network.trainable_params(), learning_rate=lr, momentum=0.9)\n",
     "time_cb = TimeMonitor(data_size=ds_train.get_dataset_size())\n",
     "config_ck = CheckpointConfig(save_checkpoint_steps=1562, keep_checkpoint_max=10)\n",

tutorials/notebook/mindinsight/mindinsight_model_lineage_and_data_lineage.ipynb

@@ -313,7 +313,7 @@
     "    epoch_size = 10\n",
     "    mnist_path = \"./MNIST_Data\"\n",
     "    \n",
-    "    net_loss = SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction='mean')\n",
+    "    net_loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')\n",
     "    repeat_size = 1\n",
     "    # create the network\n",
     "    network = LeNet5()\n",

tutorials/notebook/mixed_precision.ipynb

@@ -859,7 +859,7 @@
     "    weight_decay = 1e-4\n",
     "    \n",
     "    # define loss, model\n",
-    "    loss = SoftmaxCrossEntropyWithLogits(sparse=True, is_grad=False, reduction='mean')\n",
+    "    loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')\n",
     "    opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr, momentum)\n",
     "    model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'},amp_level=\"O2\")\n",
     "    \n",

tutorials/notebook/model_security.ipynb

@@ -422,7 +422,7 @@
     "lr = 0.01\n",
     "momentum = 0.9\n",
     "network = LeNet5()\n",
-    "net_loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction=\"mean\")\n",
+    "net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction=\"mean\")\n",
     "net_opt = nn.Momentum(network.trainable_params(), lr, momentum)\n",
     "time_cb = TimeMonitor(data_size=ds_train.get_dataset_size())\n",
     "config_ck = CheckpointConfig(save_checkpoint_steps=1875,\n",
@@ -752,7 +752,7 @@
     "from mindarmour.defenses import NaturalAdversarialDefense\n",
     "\n",
     "\n",
-    "loss = SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=False)\n",
+    "loss = SoftmaxCrossEntropyWithLogits(sparse=False, reduction='mean')\n",
     "opt = nn.Momentum(net.trainable_params(), 0.01, 0.09)\n",
     "\n",
     "nad = NaturalAdversarialDefense(net, loss_fn=loss, optimizer=opt,\n",

tutorials/notebook/nlp_application.ipynb

@@ -821,7 +821,7 @@
     "from mindspore import nn\n",
     "\n",
     "\n",
-    "loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True)\n",
+    "loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')\n",
     "opt = nn.Momentum(network.trainable_params(), cfg.learning_rate, cfg.momentum)"
    ]
   },

tutorials/notebook/quick_start.ipynb

@@ -858,7 +858,7 @@
     "net_opt = nn.Momentum(network.trainable_params(), lr, momentum)\n",
     "\n",
     "# define the loss function\n",
-    "net_loss = SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction='mean')\n",
+    "net_loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')\n",
     "\n",
     "# define the model\n",
     "model = Model(network, net_loss, net_opt, metrics={\"Accuracy\": Accuracy()} )\n",

tutorials/notebook/synchronization_training_and_evaluation.ipynb

@@ -371,7 +371,7 @@
     "    eval_data = create_dataset(eval_data_path, repeat_size=repeat_size)\n",
     "    \n",
     "    # define the loss function\n",
-    "    net_loss = SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction='mean')\n",
+    "    net_loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')\n",
     "    # define the optimizer\n",
     "    net_opt = nn.Momentum(network.trainable_params(), learning_rate=0.01, momentum=0.9)\n",
     "    config_ck = CheckpointConfig(save_checkpoint_steps=eval_per_epoch*1875, keep_checkpoint_max=15)\n",

tutorials/source_en/advanced_use/computer_vision_application.md

@@ -167,7 +167,7 @@ An example of the code for defining the loss function and optimizer in MindSpore
 
 ```python
 # loss function definition
-ls = SoftmaxCrossEntropyWithLogits(sparse=True, is_grad=False, reduction="mean")
+ls = SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
 
 # optimization definition
 opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.01, 0.9)

tutorials/source_en/advanced_use/debugging_in_pynative_mode.md

@@ -361,7 +361,7 @@ class GradWrap(nn.Cell):
 
 net = LeNet5()
 optimizer = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.1, 0.9)
-criterion = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True)
+criterion = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
 net_with_criterion = WithLossCell(net, criterion)
 train_network = GradWrap(net_with_criterion)
 train_network.set_train()

tutorials/source_en/advanced_use/differential_privacy.md

@@ -233,7 +233,7 @@ Load the LeNet network, define the loss function, configure the checkpoint param
 
 ```python
 network = LeNet5()
-net_loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean")
+net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
 config_ck = CheckpointConfig(save_checkpoint_steps=cfg.save_checkpoint_steps,
                              keep_checkpoint_max=cfg.keep_checkpoint_max)
 ckpoint_cb = ModelCheckpoint(prefix="checkpoint_lenet",

tutorials/source_en/advanced_use/model_security.md

@@ -185,7 +185,7 @@ The LeNet model is used as an example. You can also create and train your own mo
                                       batch_size=batch_size, repeat_size=1,
                                       sparse=False)
     net = LeNet5()
-    loss = SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=False)
+    loss = SoftmaxCrossEntropyWithLogits(sparse=False)
     opt = nn.Momentum(net.trainable_params(), 0.01, 0.09)
     model = Model(net, loss, opt, metrics=None)
     model.train(10, ds_train, callbacks=[LossMonitor()],

tutorials/source_en/advanced_use/network_migration.md

@@ -223,7 +223,7 @@ The ResNet-50 network migration and training on the Ascend 910 is used as an exa
    After the network is defined, the loss function and optimizer need to be defined accordingly.
 
    ```python
-   loss = SoftmaxCrossEntropyWithLogits(sparse=True)
+   loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
    opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr, config.momentum, config.weight_decay, config.loss_scale)
    ```
 

tutorials/source_en/advanced_use/nlp_application.md

@@ -193,7 +193,7 @@ if args.pre_trained:
 The sample code for defining the optimizer and loss function is as follows:
 
 ```python
-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()
 ```

tutorials/source_en/advanced_use/summary_record.md

@@ -106,7 +106,7 @@ class AlexNet(nn.Cell):
 context.set_context(mode=context.GRAPH_MODE)
 
 network = AlexNet(num_classes=10)
-loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean")
+loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
 lr = Tensor(0.1)
 opt = nn.Momentum(network.trainable_params(), lr, momentum=0.9)
 model = Model(network, loss, opt)

tutorials/source_en/quick_start/quick_start.md

@@ -291,7 +291,7 @@ Call the defined loss function in the `__main__` function.
 if __name__ == "__main__":
     ...
     #define the loss function
-    net_loss = SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction='mean')
+    net_loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
     ...
 ```
 

tutorials/source_en/use/multi_platform_inference.md

@@ -63,7 +63,7 @@ MindSpore supports the following inference scenarios based on the hardware platf
 
     ```python
     network = LeNet5(cfg.num_classes)
-    net_loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean")
+    net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
     net_opt = nn.Momentum(network.trainable_params(), cfg.lr, cfg.momentum)
     model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()})
 
@@ -86,7 +86,7 @@ MindSpore supports the following inference scenarios based on the hardware platf
 
     ```python
     network = LeNet5(cfg.num_classes)
-    net_loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean")
+    net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
     net_opt = nn.Momentum(network.trainable_params(), cfg.lr, cfg.momentum)
     model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()})
 

tutorials/source_zh_cn/advanced_use/computer_vision_application.md

@@ -170,7 +170,7 @@ MindSpore中定义损失函数和优化器的代码样例如下：
 
 ```python
 # loss function definition
-ls = SoftmaxCrossEntropyWithLogits(sparse=True, is_grad=False, reduction="mean")
+ls = SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
 
 # optimization definition
 opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.01, 0.9)

tutorials/source_zh_cn/advanced_use/debugging_in_pynative_mode.md

@@ -363,7 +363,7 @@ class GradWrap(nn.Cell):
 
 net = LeNet5()
 optimizer = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.1, 0.9)
-criterion = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True)
+criterion = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
 net_with_criterion = WithLossCell(net, criterion)
 train_network = GradWrap(net_with_criterion)
 train_network.set_train()

tutorials/source_zh_cn/advanced_use/differential_privacy.md

@@ -233,7 +233,7 @@ class LeNet5(nn.Cell):
 
 ```python
 network = LeNet5()
-net_loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean")
+net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
 config_ck = CheckpointConfig(save_checkpoint_steps=cfg.save_checkpoint_steps,
                              keep_checkpoint_max=cfg.keep_checkpoint_max)
 ckpoint_cb = ModelCheckpoint(prefix="checkpoint_lenet",

tutorials/source_zh_cn/advanced_use/gradient_accumulation.md

@@ -218,7 +218,7 @@ if __name__ == "__main__":
     ds_train = create_dataset(os.path.join(args.data_path, "train"), 32)
 
     network = LeNet5(10)
-    net_loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean")
+    net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
     net_opt = nn.Momentum(network.trainable_params(), 0.01, 0.9)
     model = GradientAccumulation(network, net_loss, net_opt)
 

tutorials/source_zh_cn/advanced_use/model_security.md

@@ -185,7 +185,7 @@ def generate_mnist_dataset(data_path, batch_size=32, repeat_size=1,
                                       batch_size=batch_size, repeat_size=1,
                                       sparse=False)
     net = LeNet5()
-    loss = SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=False)
+    loss = SoftmaxCrossEntropyWithLogits(sparse=False)
     opt = nn.Momentum(net.trainable_params(), 0.01, 0.09)
     model = Model(net, loss, opt, metrics=None)
     model.train(10, ds_train, callbacks=[LossMonitor()],

tutorials/source_zh_cn/advanced_use/nlp_application.md

@@ -193,7 +193,7 @@ if args.pre_trained:
 定义优化器及损失函数的示例代码如下：
 
 ```python
-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()
 ```

tutorials/source_zh_cn/advanced_use/summary_record.md

@@ -108,7 +108,7 @@ class AlexNet(nn.Cell):
 context.set_context(mode=context.GRAPH_MODE)
 
 network = AlexNet(num_classes=10)
-loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean")
+loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
 lr = Tensor(0.1)
 opt = nn.Momentum(network.trainable_params(), lr, momentum=0.9)
 model = Model(network, loss, opt)

tutorials/source_zh_cn/quick_start/quick_start.md

@@ -291,7 +291,7 @@ from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
 if __name__ == "__main__":
     ...
     #define the loss function
-    net_loss = SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction='mean')
+    net_loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
     ...
 ```
 

tutorials/source_zh_cn/use/multi_platform_inference.md

@@ -62,7 +62,7 @@ CPU | ONNX格式 | 支持ONNX推理的runtime/SDK，如TensorRT。
    首先构建模型，然后使用`mindspore.train.serialization`模块的`load_checkpoint`和`load_param_into_net`从本地加载模型与参数，传入验证数据集后即可进行模型推理，验证数据集的处理方式与训练数据集相同。
     ```python
     network = LeNet5(cfg.num_classes)
-    net_loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean")
+    net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
     net_opt = nn.Momentum(network.trainable_params(), cfg.lr, cfg.momentum)
     model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()})
 
@@ -84,7 +84,7 @@ CPU | ONNX格式 | 支持ONNX推理的runtime/SDK，如TensorRT。
    首先构建模型，然后使用`hub.load_weights`从云端加载模型参数，传入验证数据集后即可进行推理，验证数据集的处理方式与训练数据集相同。
     ```python
     network = LeNet5(cfg.num_classes)
-    net_loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean")
+    net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
     net_opt = nn.Momentum(network.trainable_params(), cfg.lr, cfg.momentum)
     model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()})
 

tutorials/tutorial_code/gradient_accumulation/train.py

@@ -139,7 +139,7 @@ if __name__ == "__main__":
     ds_train = create_dataset(os.path.join(args.data_path, "train"), 32)
 
     network = LeNet5(10)
-    net_loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean")
+    net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
     net_opt = nn.Momentum(network.trainable_params(), 0.01, 0.9)
     model = GradientAccumulation(network, net_loss, net_opt)
 

tutorials/tutorial_code/lenet.py

@@ -205,7 +205,7 @@ if __name__ == "__main__":
     epoch_size = 1
     mnist_path = "./MNIST_Data"
     # define the loss function
-    net_loss = SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction='mean')
+    net_loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
     repeat_size = 1
     # create the network
     network = LeNet5()

tutorials/tutorial_code/model_safety/mnist_defense_nad.py

@@ -57,7 +57,7 @@ def test_nad_method():
     load_dict = load_checkpoint(ckpt_name)
     load_param_into_net(net, load_dict)
 
-    loss = SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=False)
+    loss = SoftmaxCrossEntropyWithLogits(sparse=False)
     opt = nn.Momentum(net.trainable_params(), 0.01, 0.09)
 
     nad = NaturalAdversarialDefense(net, loss_fn=loss, optimizer=opt,

tutorials/tutorial_code/resnet/cifar_resnet50.py

@@ -111,7 +111,7 @@ if __name__ == '__main__':
 
     epoch_size = args_opt.epoch_size
     net = resnet50(args_opt.batch_size, 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'})