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net.py
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net.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 paddle
import paddle.nn as nn
import paddle.nn.functional as F
import math
class DNNLayer(nn.Layer):
def __init__(self,
sparse_feature_number,
sparse_feature_dim,
dense_feature_dim,
num_field,
layer_sizes,
sync_mode=None):
super(DNNLayer, self).__init__()
self.sync_mode = sync_mode
self.sparse_feature_number = sparse_feature_number
self.sparse_feature_dim = sparse_feature_dim
self.dense_feature_dim = dense_feature_dim
self.num_field = num_field
self.layer_sizes = layer_sizes
use_sparse = True
if paddle.is_compiled_with_npu():
use_sparse = False
self.embedding = paddle.nn.Embedding(
self.sparse_feature_number,
self.sparse_feature_dim,
sparse=use_sparse,
weight_attr=paddle.ParamAttr(
name="SparseFeatFactors",
initializer=paddle.nn.initializer.Uniform()))
sizes = [sparse_feature_dim * num_field + dense_feature_dim
] + self.layer_sizes + [2]
acts = ["relu" for _ in range(len(self.layer_sizes))] + [None]
self._mlp_layers = []
for i in range(len(layer_sizes) + 1):
linear = paddle.nn.Linear(
in_features=sizes[i],
out_features=sizes[i + 1],
weight_attr=paddle.ParamAttr(
initializer=paddle.nn.initializer.Normal(
std=1.0 / math.sqrt(sizes[i]))))
self.add_sublayer('linear_%d' % i, linear)
self._mlp_layers.append(linear)
if acts[i] == 'relu':
act = paddle.nn.ReLU()
self.add_sublayer('act_%d' % i, act)
self._mlp_layers.append(act)
def forward(self, sparse_inputs, dense_inputs, show_click=None):
sparse_embs = []
for s_input in sparse_inputs:
if self.sync_mode == "gpubox":
emb = paddle.static.nn.sparse_embedding(
input=s_input,
size=[
self.sparse_feature_number, self.sparse_feature_dim + 2
],
param_attr=paddle.ParamAttr(name="embedding"))
emb = paddle.static.nn.continuous_value_model(emb, show_click,
False)
else:
emb = self.embedding(s_input)
emb = paddle.reshape(emb, shape=[-1, self.sparse_feature_dim])
sparse_embs.append(emb)
y_dnn = paddle.concat(x=sparse_embs + [dense_inputs], axis=1)
for n_layer in self._mlp_layers:
y_dnn = n_layer(y_dnn)
return y_dnn
class StaticDNNLayer(nn.Layer):
def __init__(self, sparse_feature_number, sparse_feature_dim,
dense_feature_dim, num_field, layer_sizes):
super(StaticDNNLayer, self).__init__()
self.sparse_feature_number = sparse_feature_number
self.sparse_feature_dim = sparse_feature_dim
self.dense_feature_dim = dense_feature_dim
self.num_field = num_field
self.layer_sizes = layer_sizes
#self.embedding = paddle.nn.Embedding(
# self.sparse_feature_number,
# self.sparse_feature_dim,
# sparse=True,
# weight_attr=paddle.ParamAttr(
# name="SparseFeatFactors",
# initializer=paddle.nn.initializer.Uniform()))
sizes = [sparse_feature_dim * num_field + dense_feature_dim
] + self.layer_sizes + [2]
acts = ["relu" for _ in range(len(self.layer_sizes))] + [None]
self._mlp_layers = []
for i in range(len(layer_sizes) + 1):
linear = paddle.nn.Linear(
in_features=sizes[i],
out_features=sizes[i + 1],
weight_attr=paddle.ParamAttr(
initializer=paddle.nn.initializer.Normal(
std=1.0 / math.sqrt(sizes[i]))))
self.add_sublayer('linear_%d' % i, linear)
self._mlp_layers.append(linear)
if acts[i] == 'relu':
act = paddle.nn.ReLU()
self.add_sublayer('act_%d' % i, act)
self._mlp_layers.append(act)
def forward(self, sparse_embs, dense_inputs):
#sparse_embs = []
#for s_input in sparse_inputs:
# emb = self.embedding(s_input)
# emb = paddle.reshape(emb, shape=[-1, self.sparse_feature_dim])
# sparse_embs.append(emb)
y_dnn = paddle.concat(x=sparse_embs + [dense_inputs], axis=1)
for n_layer in self._mlp_layers:
y_dnn = n_layer(y_dnn)
return y_dnn