提交 ad5022d2 编写于 作者: Z zhangwenhui03

Merge branch 'develop' into 'develop'

add mmoe share-bottom infer

See merge request !37
# 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.
from __future__ import print_function
from paddlerec.core.reader import Reader
from paddlerec.core.utils import envs
import numpy as np
class EvaluateReader(Reader):
def init(self):
pass
def generate_sample(self, line):
"""
Read the data line by line and process it as a dictionary
"""
def reader():
"""
This function needs to be implemented by the user, based on data format
"""
l = line.strip().split(',')
l = list(map(float, l))
label_income = []
label_marital = []
data = l[2:]
if int(l[1]) == 0:
label_income = [1, 0]
elif int(l[1]) == 1:
label_income = [0, 1]
if int(l[0]) == 0:
label_marital = [1, 0]
elif int(l[0]) == 1:
label_marital = [0, 1]
feature_name = ["input", "label_income", "label_marital"]
yield zip(feature_name, [data] + [label_income] + [label_marital])
return reader
......@@ -12,6 +12,12 @@
# See the License for the specific language governing permissions and
# limitations under the License.
evaluate:
reader:
batch_size: 1
class: "{workspace}/census_infer_reader.py"
test_data_path: "{workspace}/data/train"
train:
trainer:
# for cluster training
......@@ -22,7 +28,7 @@ train:
device: cpu
reader:
batch_size: 2
batch_size: 1
class: "{workspace}/census_reader.py"
train_data_path: "{workspace}/data/train"
......
......@@ -23,7 +23,7 @@ class Model(ModelBase):
def __init__(self, config):
ModelBase.__init__(self, config)
def MMOE(self):
def MMOE(self, is_infer=False):
feature_size = envs.get_global_env("hyper_parameters.feature_size", None, self._namespace)
expert_num = envs.get_global_env("hyper_parameters.expert_num", None, self._namespace)
......@@ -34,6 +34,10 @@ class Model(ModelBase):
input_data = fluid.data(name="input", shape=[-1, feature_size], dtype="float32")
label_income = fluid.data(name="label_income", shape=[-1, 2], dtype="float32", lod_level=0)
label_marital = fluid.data(name="label_marital", shape=[-1, 2], dtype="float32", lod_level=0)
if is_infer:
self._infer_data_var = [input_data, label_income, label_marital]
self._infer_data_loader = fluid.io.DataLoader.from_generator(
feed_list=self._infer_data_var, capacity=64, use_double_buffer=False, iterable=False)
self._data_var.extend([input_data, label_income, label_marital])
# f_{i}(x) = activation(W_{i} * x + b), where activation is ReLU according to the paper
......@@ -75,14 +79,19 @@ class Model(ModelBase):
pred_income = fluid.layers.clip(output_layers[0], min=1e-15, max=1.0 - 1e-15)
pred_marital = fluid.layers.clip(output_layers[1], min=1e-15, max=1.0 - 1e-15)
cost_income = fluid.layers.cross_entropy(input=pred_income, label=label_income,soft_label = True)
cost_marital = fluid.layers.cross_entropy(input=pred_marital, label=label_marital,soft_label = True)
label_income_1 = fluid.layers.slice(label_income, axes=[1], starts=[1], ends=[2])
label_marital_1 = fluid.layers.slice(label_marital, axes=[1], starts=[1], ends=[2])
auc_income, batch_auc_1, auc_states_1 = fluid.layers.auc(input=pred_income, label=fluid.layers.cast(x=label_income_1, dtype='int64'))
auc_marital, batch_auc_2, auc_states_2 = fluid.layers.auc(input=pred_marital, label=fluid.layers.cast(x=label_marital_1, dtype='int64'))
if is_infer:
self._infer_results["AUC_income"] = auc_income
self._infer_results["AUC_marital"] = auc_marital
return
cost_income = fluid.layers.cross_entropy(input=pred_income, label=label_income,soft_label = True)
cost_marital = fluid.layers.cross_entropy(input=pred_marital, label=label_marital,soft_label = True)
avg_cost_income = fluid.layers.mean(x=cost_income)
avg_cost_marital = fluid.layers.mean(x=cost_marital)
......@@ -101,4 +110,4 @@ class Model(ModelBase):
def infer_net(self):
pass
self.MMOE(is_infer=True)
# 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.
from __future__ import print_function
from paddlerec.core.reader import Reader
from paddlerec.core.utils import envs
import numpy as np
class EvaluateReader(Reader):
def init(self):
pass
def generate_sample(self, line):
"""
Read the data line by line and process it as a dictionary
"""
def reader():
"""
This function needs to be implemented by the user, based on data format
"""
l = line.strip().split(',')
l = list(map(float, l))
label_income = []
label_marital = []
data = l[2:]
if int(l[1]) == 0:
label_income = [1, 0]
elif int(l[1]) == 1:
label_income = [0, 1]
if int(l[0]) == 0:
label_marital = [1, 0]
elif int(l[0]) == 1:
label_marital = [0, 1]
feature_name = ["input", "label_income", "label_marital"]
yield zip(feature_name, [data] + [label_income] + [label_marital])
return reader
......@@ -12,6 +12,12 @@
# See the License for the specific language governing permissions and
# limitations under the License.
evaluate:
reader:
batch_size: 1
class: "{workspace}/census_infer_reader.py"
test_data_path: "{workspace}/data/train"
train:
trainer:
# for cluster training
......
......@@ -23,7 +23,7 @@ class Model(ModelBase):
def __init__(self, config):
ModelBase.__init__(self, config)
def train(self):
def model(self, is_infer=False):
feature_size = envs.get_global_env("hyper_parameters.feature_size", None, self._namespace)
bottom_size = envs.get_global_env("hyper_parameters.bottom_size", None, self._namespace)
......@@ -34,6 +34,11 @@ class Model(ModelBase):
label_income = fluid.data(name="label_income", shape=[-1, 2], dtype="float32", lod_level=0)
label_marital = fluid.data(name="label_marital", shape=[-1, 2], dtype="float32", lod_level=0)
if is_infer:
self._infer_data_var = [input_data, label_income, label_marital]
self._infer_data_loader = fluid.io.DataLoader.from_generator(
feed_list=self._infer_data_var, capacity=64, use_double_buffer=False, iterable=False)
self._data_var.extend([input_data, label_income, label_marital])
bottom_output = fluid.layers.fc(input=input_data,
......@@ -60,16 +65,19 @@ class Model(ModelBase):
pred_income = fluid.layers.clip(output_layers[0], min=1e-15, max=1.0 - 1e-15)
pred_marital = fluid.layers.clip(output_layers[1], min=1e-15, max=1.0 - 1e-15)
cost_income = fluid.layers.cross_entropy(input=pred_income, label=label_income,soft_label = True)
cost_marital = fluid.layers.cross_entropy(input=pred_marital, label=label_marital,soft_label = True)
label_income_1 = fluid.layers.slice(label_income, axes=[1], starts=[1], ends=[2])
label_marital_1 = fluid.layers.slice(label_marital, axes=[1], starts=[1], ends=[2])
auc_income, batch_auc_1, auc_states_1 = fluid.layers.auc(input=pred_income, label=fluid.layers.cast(x=label_income_1, dtype='int64'))
auc_marital, batch_auc_2, auc_states_2 = fluid.layers.auc(input=pred_marital, label=fluid.layers.cast(x=label_marital_1, dtype='int64'))
if is_infer:
self._infer_results["AUC_income"] = auc_income
self._infer_results["AUC_marital"] = auc_marital
return
cost_income = fluid.layers.cross_entropy(input=pred_income, label=label_income,soft_label = True)
cost_marital = fluid.layers.cross_entropy(input=pred_marital, label=label_marital,soft_label = True)
cost = fluid.layers.elementwise_add(cost_income, cost_marital, axis=1)
avg_cost = fluid.layers.mean(x=cost)
......@@ -82,8 +90,8 @@ class Model(ModelBase):
def train_net(self):
self.train()
self.model()
def infer_net(self):
pass
self.model(is_infer=True)
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