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netTest.py
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netTest.py
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# !/usr/bin/python
# coding: utf8
import torch
#from torch import nn, optim
from torch import nn
import torch.nn.functional as nnFunction
## Define a Net class, inherited from torch.nn.Module
## This net has only one layer
class OneLayer(nn.Module):
"""
Define a simply net class, only one layer
"""
# Net ini
def __init__(self, n_feature, n_hidden, n_output):
# 继承父类的初始化函数
super(OneLayer, self).__init__()
# 网络的隐藏层创建，名称可以随便起
self.hidden_layer = nn.Sequential(nn.Linear(n_feature, n_hidden), nn.ReLU(True))#self.hidden_layer = nn.Linear(n_feature, n_hidden)#
# 输出层(预测层)创建，接收来自隐含层的数据
self.predict_layer = nn.Linear(n_hidden, n_output)
# 网络的传播函数，构造计算图
def forward(self, x):
# 用relu函数处理隐含层输出的结果并传给输出层
#hidden_result = self.hidden_layer(x)
x = self.hidden_layer(x)#relu_result = nnFunction.relu(hidden_result)#非常重要，曲面弯曲
predict_result = self.predict_layer(x)#predict_result = self.predict_layer(relu_result)#
return predict_result
class simpleNet(nn.Module):
"""
定义了一个简单的三层全连接神经网络，每一层都是线性的
"""
# Net类的初始化函数
def __init__(self, in_dim, n_hidden_1, n_hidden_2, out_dim):
super(simpleNet, self).__init__()
self.layer1 = nn.Linear(in_dim, n_hidden_1)
self.layer2 = nn.Linear(n_hidden_1, n_hidden_2)
self.layer3 = nn.Linear(n_hidden_2, out_dim)
def forward(self, x):
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
return x
class Activation_Net(nn.Module):
"""
在上面的simpleNet的基础上，在每层的输出部分添加了激活函数
"""
def __init__(self, in_dim, n_hidden_1, n_hidden_2, out_dim):
super(Activation_Net, self).__init__()
self.layer1 = nn.Sequential(nn.Linear(in_dim, n_hidden_1), nn.ReLU(True))
self.layer2 = nn.Sequential(nn.Linear(n_hidden_1, n_hidden_2), nn.ReLU(True))
self.layer3 = nn.Sequential(nn.Linear(n_hidden_2, out_dim))
"""
这里的Sequential()函数的功能是将网络的层组合到一起。
"""
def forward(self, x):
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
return x
class Batch_Net(nn.Module):
"""
在上面的Activation_Net的基础上，增加了一个加快收敛速度的方法——批标准化
"""
def __init__(self, in_dim, n_hidden_1, n_hidden_2, out_dim):
super(Batch_Net, self).__init__()
self.layer1 = nn.Sequential(nn.Linear(in_dim, n_hidden_1), nn.BatchNorm1d(n_hidden_1), nn.ReLU(True))
self.layer2 = nn.Sequential(nn.Linear(n_hidden_1, n_hidden_2), nn.BatchNorm1d(n_hidden_2), nn.ReLU(True))
self.layer3 = nn.Sequential(nn.Linear(n_hidden_2, out_dim))
def forward(self, x):
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
return x
class Batch_Net_5(nn.Module):
"""
在上面的Activation_Net的基础上，增加了一个加快收敛速度的方法——批标准化
"""
def __init__(self, in_dim, n_hidden_1, n_hidden_2, n_hidden_3, n_hidden_4, n_hidden_5, out_dim):
super(Batch_Net_5, self).__init__()
self.layer1 = nn.Sequential(nn.Linear(in_dim, n_hidden_1), nn.BatchNorm1d(n_hidden_1), nn.ReLU(True))
self.layer2 = nn.Sequential(nn.Linear(n_hidden_1, n_hidden_2), nn.BatchNorm1d(n_hidden_2), nn.ReLU(True))
self.layer3 = nn.Sequential(nn.Linear(n_hidden_2, n_hidden_3), nn.BatchNorm1d(n_hidden_3), nn.ReLU(True))
self.layer4 = nn.Sequential(nn.Linear(n_hidden_3, n_hidden_4), nn.BatchNorm1d(n_hidden_4), nn.ReLU(True))
self.layer5 = nn.Sequential(nn.Linear(n_hidden_4, n_hidden_5), nn.BatchNorm1d(n_hidden_5), nn.ReLU(True))
self.layer6 = nn.Sequential(nn.Linear(n_hidden_5, out_dim))
def forward(self, x):
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.layer5(x)
x = self.layer6(x)
return x
class Batch_Net_5_2(nn.Module):
"""
在上面的Activation_Net的基础上，增加了一个加快收敛速度的方法——批标准化
"""
def __init__(self, in_dim, n_hidden_1, n_hidden_2, n_hidden_3, n_hidden_4, n_hidden_5, out_dim):
super(Batch_Net_5_2, self).__init__()
self.layer1 = nn.Sequential(nn.Linear(in_dim, n_hidden_1), nn.ReLU(True))
self.layer2 = nn.Sequential(nn.Linear(n_hidden_1, n_hidden_2), nn.ReLU(True))
self.layer3 = nn.Sequential(nn.Linear(n_hidden_2, n_hidden_3), nn.ReLU(True))
self.layer4 = nn.Sequential(nn.Linear(n_hidden_3, n_hidden_4), nn.ReLU(True))
self.layer5 = nn.Sequential(nn.Linear(n_hidden_4, n_hidden_5), nn.ReLU(True))
self.layer6 = nn.Sequential(nn.Linear(n_hidden_5, out_dim))
def forward(self, x):
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.layer5(x)
x = self.layer6(x)
return x
class Batch_Net_6(nn.Module):
"""
在上面的Activation_Net_5_2的基础上，增加了一层
"""
def __init__(self, in_dim, n_hidden_1, n_hidden_2, n_hidden_3, n_hidden_4, n_hidden_5, n_hidden_6, out_dim):
super(Batch_Net_6, self).__init__()
self.layer1 = nn.Sequential(nn.Linear(in_dim, n_hidden_1), nn.Tanh())
self.layer2 = nn.Sequential(nn.Linear(n_hidden_1, n_hidden_2), nn.Tanh())
self.layer3 = nn.Sequential(nn.Linear(n_hidden_2, n_hidden_3), nn.Tanh())
self.layer4 = nn.Sequential(nn.Linear(n_hidden_3, n_hidden_4), nn.Tanh())
self.layer5 = nn.Sequential(nn.Linear(n_hidden_4, n_hidden_5), nn.Tanh())
self.layer6 = nn.Sequential(nn.Linear(n_hidden_5, n_hidden_6), nn.Tanh())
self.layer7 = nn.Sequential(nn.Linear(n_hidden_6, out_dim))
def forward(self, x):
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.layer5(x)
x = self.layer6(x)
x = self.layer7(x)
return x
# class Batch_Net_5_3(nn.Module):
# """
# 在上面的Activation_Net的基础上，增加了一个加快收敛速度的方法——批标准化
# """
# def __init__(self, in_dim, n_hidden_1, n_hidden_2, n_hidden_3, n_hidden_4, n_hidden_5, out_dim, bias=False):
# super(Batch_Net_5_3, self).__init__()
# self.layer1 = nn.Linear(in_dim, n_hidden_1, bias=bias)
# #nn.init.kaiming_normal_(self.layer1.weight)
# self.relu1 = nn.ReLU(inplace=True)
# self.layer2 = nn.Linear(in_dim, n_hidden_2, bias=bias)
# #nn.init.kaiming_normal_(self.layer2.weight)
# self.relu2 = nn.ReLU(inplace=True)
# self.layer3 = nn.Linear(n_hidden_2, n_hidden_3, bias=bias)
# #nn.init.kaiming_normal_(self.layer3.weight)
# self.relu3 = nn.ReLU(inplace=True)
# self.layer4 = nn.Linear(n_hidden_3, n_hidden_4, bias=bias)
# #nn.init.kaiming_normal_(self.layer4.weight)
# self.relu4 = nn.ReLU(inplace=True)
# self.layer5 = nn.Linear(n_hidden_4, n_hidden_5, bias=bias)
# #nn.init.kaiming_normal_(self.layer5.weight)
# self.relu5 = nn.ReLU(inplace=True)
# self.layer6 = nn.Linear(n_hidden_5, out_dim, bias=bias)
# #nn.init.kaiming_normal_(self.layer6.weight)
# self.relu6 = nn.ReLU(inplace=True)
# def forward(self, x):
# x = self.layer1(x)
# x = self.layer2(x)
# x = self.layer3(x)
# x = self.layer4(x)
# x = self.layer5(x)
# x = self.layer6(x)
# return x
# Define the loss function here
# the total loss is just the sum of different MSE
# the total loss is actually the sum of different MSE loss.
def NN_UT_help(prediction):
NN_UT = np.zeros((len(prediction), 2))
index = -1
for i in range(len(prediction)):
index += 1
NN_UT[index] = np.array([prediction[index][0], prediction[index][1]])
NN_UT = torch.from_numpy(NN_UT).float()
return NN_UT
def NN_p_help(prediction, out_dim):
# the out_dim should be the dim of p
NN_p = np.zeros((len(prediction), out_dim))
index = -1
for i in range(len(prediction)):
index += 1
for k in range(0, out_dim):
NN_p[index][k] = prediction[index][k+2]
NN_p = torch.from_numpy(NN_p).float()
return NN_p
def NN_p_sum_help(NN_p):
NN_p_sum = np.zeros((len(NN_p), 1))
index = -1
for i in range(len(NN_p)):
index += 1
NN_p_sum[index] = sum(NN_p[index])
NN_p_sum = torch.from_numpy(NN_p_sum).float()
return NN_p_sum