add widedeep

model_zoo/wide_and_deep/tools/config.py

@@ -0,0 +1,91 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+""" config. """
+import argparse
+
+
+def argparse_init():
+    """
+    argparse_init
+    """
+    parser = argparse.ArgumentParser(description='WideDeep')
+    parser.add_argument("--data_path", type=str, default="./test_raw_data/")
+    parser.add_argument("--epochs", type=int, default=15)
+    parser.add_argument("--batch_size", type=int, default=10000)
+    parser.add_argument("--eval_batch_size", type=int, default=15)
+    parser.add_argument("--field_size", type=int, default=39)
+    parser.add_argument("--vocab_size", type=int, default=184965)
+    parser.add_argument("--emb_dim", type=int, default=80)
+    parser.add_argument("--deep_layer_dim", type=int, nargs='+', default=[1024, 512, 256, 128])
+    parser.add_argument("--deep_layer_act", type=str, default='relu')
+    parser.add_argument("--keep_prob", type=float, default=1.0)
+
+    parser.add_argument("--output_path", type=str, default="./output/")
+    parser.add_argument("--ckpt_path", type=str, default="./checkpoints/")
+    parser.add_argument("--eval_file_name", type=str, default="eval.log")
+    parser.add_argument("--loss_file_name", type=str, default="loss.log")
+    return parser
+
+
+class Config_WideDeep():
+    """
+    Config_WideDeep
+    """
+    def __init__(self):
+        self.data_path = "./test_raw_data/"
+        self.epochs = 15
+        self.batch_size = 10000
+        self.eval_batch_size = 10000
+        self.field_size = 39
+        self.vocab_size = 184965
+        self.emb_dim = 80
+        self.deep_layer_dim = [1024, 512, 256, 128]
+        self.deep_layer_act = 'relu'
+        self.weight_bias_init = ['normal', 'normal']
+        self.emb_init = 'normal'
+        self.init_args = [-0.01, 0.01]
+        self.dropout_flag = False
+        self.keep_prob = 1.0
+        self.l2_coef = 8e-5
+
+        self.output_path = "./output"
+        self.eval_file_name = "eval.log"
+        self.loss_file_name = "loss.log"
+        self.ckpt_path = "./checkpoints/"
+
+    def argparse_init(self):
+        """
+        argparse_init
+        """
+        parser = argparse_init()
+        args, _ = parser.parse_known_args()
+        self.epochs = args.epochs
+        self.batch_size = args.batch_size
+        self.eval_batch_size = args.eval_batch_size
+        self.field_size = args.field_size
+        self.vocab_size = args.vocab_size
+        self.emb_dim = args.emb_dim
+        self.deep_layer_dim = args.deep_layer_dim
+        self.deep_layer_act = args.deep_layer_act
+        self.keep_prob = args.keep_prob
+        self.weight_bias_init = ['normal', 'normal']
+        self.emb_init = 'normal'
+        self.init_args = [-0.01, 0.01]
+        self.dropout_flag = False
+        self.l2_coef = 8e-5
+
+        self.output_path = args.output_path
+        self.eval_file_name = args.eval_file_name
+        self.loss_file_name = args.loss_file_name
+        self.ckpt_path = args.ckpt_path

model_zoo/wide_and_deep/tools/train_and_test.py

@@ -0,0 +1,97 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+""" test_training """
+import os
+
+from mindspore import Model, context
+from mindspore.train.callback import ModelCheckpoint, CheckpointConfig
+
+from wide_deep.models.WideDeep import PredictWithSigmoid, TrainStepWrap, NetWithLossClass, WideDeepModel
+from wide_deep.utils.callbacks import LossCallBack, EvalCallBack
+from wide_deep.data.datasets import create_dataset
+from wide_deep.utils.metrics import AUCMetric
+from tools.config import Config_WideDeep
+
+context.set_context(mode=context.GRAPH_MODE, device_target="Davinci")
+
+
+def get_WideDeep_net(config):
+    WideDeep_net = WideDeepModel(config)
+
+    loss_net = NetWithLossClass(WideDeep_net, config)
+    train_net = TrainStepWrap(loss_net)
+    eval_net = PredictWithSigmoid(WideDeep_net)
+
+    return train_net, eval_net
+
+
+class ModelBuilder():
+    """
+    ModelBuilder
+    """
+    def __init__(self):
+        pass
+
+    def get_hook(self):
+        pass
+
+    def get_train_hook(self):
+        hooks = []
+        callback = LossCallBack()
+        hooks.append(callback)
+
+        if int(os.getenv('DEVICE_ID')) == 0:
+            pass
+        return hooks
+
+    def get_net(self, config):
+        return get_WideDeep_net(config)
+
+
+def test_train_eval(config):
+    """
+    test_train_eval
+    """
+    data_path = config.data_path
+    batch_size = config.batch_size
+    epochs = config.epochs
+    ds_train = create_dataset(data_path, train_mode=True, epochs=epochs, batch_size=batch_size)
+    ds_eval = create_dataset(data_path, train_mode=False, epochs=epochs + 1, batch_size=batch_size)
+    print("ds_train.size: {}".format(ds_train.get_dataset_size()))
+    print("ds_eval.size: {}".format(ds_eval.get_dataset_size()))
+
+    net_builder = ModelBuilder()
+
+    train_net, eval_net = net_builder.get_net(config)
+    train_net.set_train()
+    auc_metric = AUCMetric()
+
+    model = Model(train_net, eval_network=eval_net, metrics={"auc": auc_metric})
+
+    eval_callback = EvalCallBack(model, ds_eval, auc_metric, config)
+
+    callback = LossCallBack()
+    ckptconfig = CheckpointConfig(save_checkpoint_steps=1, keep_checkpoint_max=5)
+    ckpoint_cb = ModelCheckpoint(prefix='widedeep_train', directory=config.ckpt_path, config=ckptconfig)
+
+    out = model.eval(ds_eval)
+    print("=====" * 5 + "model.eval() initialized: {}".format(out))
+    model.train(epochs, ds_train, callbacks=[eval_callback, callback, ckpoint_cb])
+
+
+if __name__ == "__main__":
+    wide_deep_config = Config_WideDeep()
+    wide_deep_config.argparse_init()
+
+    test_train_eval(wide_deep_config)