Updated the custom_models.md changed cross_entropy code (huggingface#…

…33118)
zucchini-nlp · Aug 30, 2024 · ab6d35f · ab6d35f
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Showing 7 changed files with 7 additions and 7 deletions.
diff --git a/docs/source/en/custom_models.md b/docs/source/en/custom_models.md
@@ -185,7 +185,7 @@ class ResnetModelForImageClassification(PreTrainedModel):
     def forward(self, tensor, labels=None):
         logits = self.model(tensor)
         if labels is not None:
-            loss = torch.nn.cross_entropy(logits, labels)
+            loss = torch.nn.functional.cross_entropy(logits, labels)
             return {"loss": loss, "logits": logits}
         return {"logits": logits}
 ```

diff --git a/docs/source/es/custom_models.md b/docs/source/es/custom_models.md
@@ -173,7 +173,7 @@ class ResnetModelForImageClassification(PreTrainedModel):
     def forward(self, tensor, labels=None):
         logits = self.model(tensor)
         if labels is not None:
-            loss = torch.nn.cross_entropy(logits, labels)
+            loss = torch.nn.functional.cross_entropy(logits, labels)
             return {"loss": loss, "logits": logits}
         return {"logits": logits}
 ```

diff --git a/docs/source/it/custom_models.md b/docs/source/it/custom_models.md
@@ -174,7 +174,7 @@ class ResnetModelForImageClassification(PreTrainedModel):
     def forward(self, tensor, labels=None):
         logits = self.model(tensor)
         if labels is not None:
-            loss = torch.nn.cross_entropy(logits, labels)
+            loss = torch.nn.functional.cross_entropy(logits, labels)
             return {"loss": loss, "logits": logits}
         return {"logits": logits}
 ```

diff --git a/docs/source/ja/custom_models.md b/docs/source/ja/custom_models.md
@@ -161,7 +161,7 @@ class ResnetModelForImageClassification(PreTrainedModel):
     def forward(self, tensor, labels=None):
         logits = self.model(tensor)
         if labels is not None:
-            loss = torch.nn.cross_entropy(logits, labels)
+            loss = torch.nn.functional.cross_entropy(logits, labels)
             return {"loss": loss, "logits": logits}
         return {"logits": logits}
 ```

diff --git a/docs/source/ko/custom_models.md b/docs/source/ko/custom_models.md
@@ -169,7 +169,7 @@ class ResnetModelForImageClassification(PreTrainedModel):
     def forward(self, tensor, labels=None):
         logits = self.model(tensor)
         if labels is not None:
-            loss = torch.nn.cross_entropy(logits, labels)
+            loss = torch.nn.functional.cross_entropy(logits, labels)
             return {"loss": loss, "logits": logits}
         return {"logits": logits}
 ```

diff --git a/docs/source/pt/custom_models.md b/docs/source/pt/custom_models.md
@@ -173,7 +173,7 @@ class ResnetModelForImageClassification(PreTrainedModel):
     def forward(self, tensor, labels=None):
         logits = self.model(tensor)
         if labels is not None:
-            loss = torch.nn.cross_entropy(logits, labels)
+            loss = torch.nn.functional.cross_entropy(logits, labels)
             return {"loss": loss, "logits": logits}
         return {"logits": logits}
 ```

diff --git a/docs/source/zh/custom_models.md b/docs/source/zh/custom_models.md
@@ -154,7 +154,7 @@ class ResnetModelForImageClassification(PreTrainedModel):
     def forward(self, tensor, labels=None):
         logits = self.model(tensor)
         if labels is not None:
-            loss = torch.nn.cross_entropy(logits, labels)
+            loss = torch.nn.functional.cross_entropy(logits, labels)
             return {"loss": loss, "logits": logits}
         return {"logits": logits}
 ```