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From GitBox <...@apache.org>
Subject [GitHub] [singa] XJDKC commented on a change in pull request #697: New Model Layer Operator API
Date Tue, 02 Jun 2020 01:46:39 GMT

XJDKC commented on a change in pull request #697:
URL: https://github.com/apache/singa/pull/697#discussion_r433577123



##########
File path: examples/mlp/module.py
##########
@@ -56,10 +56,9 @@ def forward(self, inputs):
         x = autograd.add_bias(x, self.b1)
         return x
 
-    def loss(self, out, ty):
-        return autograd.softmax_cross_entropy(out, ty)
-
-    def optim(self, loss, dist_option, spars):
+    def train_one_batch(self, x, y, dist_option, spars):
+        out = self.forward(x)
+        loss = autograd.softmax_cross_entropy(out, y)

Review comment:
       Yes. In this way, the user only needs to use layers to define their model.

##########
File path: examples/mlp/module.py
##########
@@ -56,10 +56,9 @@ def forward(self, inputs):
         x = autograd.add_bias(x, self.b1)
         return x
 
-    def loss(self, out, ty):
-        return autograd.softmax_cross_entropy(out, ty)
-
-    def optim(self, loss, dist_option, spars):
+    def train_one_batch(self, x, y, dist_option, spars):
+        out = self.forward(x)
+        loss = autograd.softmax_cross_entropy(out, y)

Review comment:
       Yes. In this way, users only needs to use layers to define their model.




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