fix bug

recommend-model-produce/src/main/python/models/dssm/net.py

@@ -128,38 +128,3 @@ class DSSMLayer(nn.Layer):
         
         return left_vec, right_vec
 
-
-    
-    
-    def forward(self, input_data, is_infer):
-        query_fc = input_data[0]
-        for n_layer in self._query_layers:
-            query_fc = n_layer(query_fc)
-        self.query_fc = query_fc
-
-        doc_pos_fc = input_data[1]
-        for n_layer in self._doc_layers:
-            doc_pos_fc = n_layer(doc_pos_fc)
-        self.doc_pos_fc = doc_pos_fc
-
-        self.params = [self._query_layers[-2].bias]
-
-        R_Q_D_p = F.cosine_similarity(
-            query_fc, doc_pos_fc, axis=1).reshape([-1, 1])
-
-        if is_infer:
-            return R_Q_D_p, paddle.ones(shape=[self.slice_end, 1])
-
-        R_Q_D_ns = []
-        for i in range(len(input_data) - 2):
-            doc_neg_fc_i = input_data[i + 2]
-            for n_layer in self._doc_layers:
-                doc_neg_fc_i = n_layer(doc_neg_fc_i)
-            R_Q_D_n = F.cosine_similarity(
-                query_fc, doc_neg_fc_i, axis=1).reshape([-1, 1])
-            R_Q_D_ns.append(R_Q_D_n)
-        concat_Rs = paddle.concat(x=[R_Q_D_p] + R_Q_D_ns, axis=1)
-        prob = F.softmax(concat_Rs, axis=1)
-        hit_prob = paddle.slice(
-            prob, axes=[0, 1], starts=[0, 0], ends=[self.slice_end, 1])
-        return R_Q_D_p, hit_prob