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model.py
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model.py
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# 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
from collections import OrderedDict
import paddle.fluid as fluid
from paddlerec.core.utils import envs
from paddlerec.core.model import ModelBase
class Model(ModelBase):
def __init__(self, config):
ModelBase.__init__(self, config)
def _init_hyper_parameters(self):
self.is_distributed = True if envs.get_fleet_mode().upper(
) == "PSLIB" else False
self.sparse_feature_number = envs.get_global_env(
"hyper_parameters.sparse_feature_number", None)
self.sparse_feature_dim = envs.get_global_env(
"hyper_parameters.sparse_feature_dim", None)
self.is_sparse = envs.get_global_env("hyper_parameters.is_sparse",
False)
self.use_batchnorm = envs.get_global_env(
"hyper_parameters.use_batchnorm", False)
self.filters = envs.get_global_env("hyper_parameters.filters",
[38, 40, 42, 44])
self.filter_size = envs.get_global_env("hyper_parameters.filter_size",
[1, 9])
self.pooling_size = envs.get_global_env(
"hyper_parameters.pooling_size", [2, 2, 2, 2])
self.new_filters = envs.get_global_env("hyper_parameters.new_filters",
[3, 3, 3, 3])
self.hidden_layers = envs.get_global_env("hyper_parameters.fc_sizes")
self.num_field = envs.get_global_env("hyper_parameters.num_field",
None)
self.act = envs.get_global_env("hyper_parameters.act", None)
def net(self, inputs, is_infer=False):
raw_feat_idx = self._sparse_data_var[1] # (batch_size * num_field) * 1
raw_feat_value = self._dense_data_var[0] # batch_size * num_field
self.label = self._sparse_data_var[0] # batch_size * 1
init_value_ = 0.1
feat_idx = raw_feat_idx
feat_value = fluid.layers.reshape(
raw_feat_value,
[-1, self.num_field, 1]) # batch_size * num_field * 1
# ------------------------- Embedding layers --------------------------
feat_embeddings_re = fluid.embedding(
input=feat_idx,
is_sparse=self.is_sparse,
is_distributed=self.is_distributed,
dtype='float32',
size=[self.sparse_feature_number + 1, self.sparse_feature_dim],
padding_idx=0,
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.TruncatedNormalInitializer(
loc=0.0,
scale=init_value_ /
math.sqrt(float(self.sparse_feature_dim))))
) # (batch_size * num_field) * 1 * embedding_size
feat_embeddings = fluid.layers.reshape(
feat_embeddings_re,
shape=[-1, self.num_field, self.sparse_feature_dim
]) # batch_size * num_field * embedding_size
# batch_size * num_field * embedding_size
feat_embeddings = feat_embeddings * feat_value
featuer_generation_input = fluid.layers.reshape(
feat_embeddings,
shape=[0, 1, self.num_field, self.sparse_feature_dim])
new_feature_list = []
new_feature_field_num = 0
# ------------------------- Feature Generation --------------------------
for i in range(len(self.filters)):
conv_out = fluid.layers.conv2d(
featuer_generation_input,
num_filters=self.filters[i],
filter_size=self.filter_size,
padding="SAME",
act="tanh")
pool_out = fluid.layers.pool2d(
conv_out,
pool_size=[self.pooling_size[i], 1],
pool_type="max",
pool_stride=[self.pooling_size[i], 1])
pool_out_shape = pool_out.shape[2]
new_feature_field_num += self.new_filters[i] * pool_out_shape
flat_pool_out = fluid.layers.flatten(pool_out)
recombination_out = fluid.layers.fc(input=flat_pool_out,
size=self.new_filters[i] *
self.sparse_feature_dim *
pool_out_shape,
act='tanh')
new_feature_list.append(recombination_out)
featuer_generation_input = pool_out
new_featues = fluid.layers.concat(new_feature_list, axis=1)
new_features_map = fluid.layers.reshape(
new_featues,
shape=[0, new_feature_field_num, self.sparse_feature_dim])
all_features = fluid.layers.concat(
[feat_embeddings, new_features_map], axis=1)
interaction_list = []
for i in range(all_features.shape[1]):
for j in range(i + 1, all_features.shape[1]):
interaction_list.append(
fluid.layers.reduce_sum(
all_features[:, i, :] * all_features[:, j, :],
dim=1,
keep_dim=True))
new_feature_dnn_input = fluid.layers.concat(interaction_list, axis=1)
feat_embeddings_dnn_input = fluid.layers.reshape(
feat_embeddings,
shape=[0, self.num_field * self.sparse_feature_dim])
dnn_input = fluid.layers.concat(
[feat_embeddings_dnn_input, new_feature_dnn_input], axis=1)
# ------------------------- DNN --------------------------
fcs = [dnn_input]
for size in self.hidden_layers:
output = fluid.layers.fc(
input=fcs[-1],
size=size,
act=self.act,
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.Normal(
scale=1.0 / math.sqrt(fcs[-1].shape[1]))))
fcs.append(output)
predict = fluid.layers.fc(
input=fcs[-1],
size=1,
act="sigmoid",
param_attr=fluid.ParamAttr(initializer=fluid.initializer.Normal(
scale=1 / math.sqrt(fcs[-1].shape[1]))))
# ------------------------- Predict --------------------------
self.predict = predict
cost = fluid.layers.log_loss(
input=self.predict, label=fluid.layers.cast(self.label, "float32"))
avg_cost = fluid.layers.reduce_sum(cost)
self._cost = avg_cost
predict_2d = fluid.layers.concat([1 - self.predict, self.predict], 1)
label_int = fluid.layers.cast(self.label, 'int64')
auc_var, batch_auc_var, _ = fluid.layers.auc(input=predict_2d,
label=label_int,
slide_steps=0)
self._metrics["AUC"] = auc_var
self._metrics["BATCH_AUC"] = batch_auc_var
if is_infer:
self._infer_results["AUC"] = auc_var