static_model

Author: wangzhen38

models/rank/xdeepfm/static_model.py

@@ -0,0 +1,115 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import math
+import paddle
+
+from net import xDeepFMLayer
+
+
+class StaticModel():
+    def __init__(self, config):
+        self.cost = None
+        self.config = config
+        self._init_hyper_parameters()
+
+    def _init_hyper_parameters(self):
+        self.is_distributed = False
+        self.distributed_embedding = False
+
+        if self.config.get("hyper_parameters.distributed_embedding", 0) == 1:
+            self.distributed_embedding = True
+
+        self.sparse_feature_number = self.config.get(
+            "hyper_parameters.sparse_feature_number")
+        self.sparse_feature_dim = self.config.get(
+            "hyper_parameters.sparse_feature_dim")
+        self.sparse_inputs_slot = self.config.get(
+            "hyper_parameters.sparse_inputs_slots")
+        self.dense_input_dim = self.config.get(
+            "hyper_parameters.dense_input_dim")
+        self.learning_rate = self.config.get(
+            "hyper_parameters.optimizer.learning_rate")
+        #self.fc_sizes = self.config.get("hyper_parameters.fc_sizes")
+        self.layer_sizes_cin = self.config.get(
+            "hyper_parameters.layer_sizes_cin")
+        self.layer_sizes_dnn = self.config.get(
+            "hyper_parameters.layer_sizes_dnn")
+
+    def create_feeds(self, is_infer=False):
+        dense_input = paddle.static.data(
+            name="dense_input",
+            shape=[None, self.dense_input_dim],
+            dtype="float32")
+
+        sparse_input_ids = [
+            paddle.static.data(
+                name="C" + str(i), shape=[None, 1], dtype="int64")
+            for i in range(1, self.sparse_inputs_slot)
+        ]
+
+        label = paddle.static.data(
+            name="label", shape=[None, 1], dtype="int64")
+
+        self._sparse_data_var = [label] + sparse_input_ids
+        self._dense_data_var = [dense_input]
+
+        feeds_list = [label] + sparse_input_ids + [dense_input]
+        return feeds_list
+
+    def net(self, input, is_infer=False):
+        self.sparse_inputs = input[1:self.sparse_inputs_slot]
+        self.dense_input = input[-1]
+        self.label_input = input[0]
+        sparse_number = self.sparse_inputs_slot - 1
+        assert sparse_number == len(self.sparse_inputs)
+
+        xdeepfm_model = xDeepFMLayer(
+            self.sparse_feature_number, self.sparse_feature_dim,
+            self.dense_input_dim, sparse_number, self.layer_sizes_cin,
+            self.layer_sizes_dnn)
+
+        pred = xdeepfm_model(self.sparse_inputs, self.dense_input)
+
+        #pred = F.sigmoid(prediction)
+
+        predict_2d = paddle.concat(x=[1 - pred, pred], axis=1)
+
+        auc, batch_auc_var, _ = paddle.static.auc(input=predict_2d,
+                                                  label=self.label_input,
+                                                  slide_steps=0)
+
+        self.inference_target_var = auc
+        if is_infer:
+            fetch_dict = {'auc': auc}
+            return fetch_dict
+
+        cost = paddle.nn.functional.log_loss(
+            input=pred, label=paddle.cast(
+                self.label_input, dtype="float32"))
+        avg_cost = paddle.mean(x=cost)
+        self._cost = avg_cost
+        fetch_dict = {'cost': avg_cost, 'auc': auc}
+        return fetch_dict
+
+    def create_optimizer(self, strategy=None):
+        optimizer = paddle.optimizer.Adam(
+            learning_rate=self.learning_rate, lazy_mode=True)
+        if strategy != None:
+            import paddle.distributed.fleet as fleet
+            optimizer = fleet.distributed_optimizer(optimizer, strategy)
+        optimizer.minimize(self._cost)
+
+    def infer_net(self, input):
+        return self.net(input, is_infer=True)