用python写一个人工神经网络,具有一个输入层,三个隐藏层,一个输出层,其中输入层具有12个节点,输入输出数据从已有的excel文件导入,隐藏层每层10个节点,输出层8个节点,还要对输入输出数据都进行归一化处理,隐藏层都使用Leaky ReLU激活函数,mini-batch的大小设置为50,epoch的数量设置为200。在反向传播过程中使用Adam优化器更新DNN参数,初始学习率设置为0.001,学习率每50次下降10次,用于神经网络隐藏层的微调,而且训练集和验证集比例是9比1,并且导出损伤函数曲线
时间: 2024-03-01 11:56:16 浏览: 29
好的,我会根据你的要求回答你的问题。首先,建议你使用Python中的NumPy和Pandas库来处理数据,使用PyTorch或TensorFlow等库来搭建神经网络。
下面是一个简单的代码示例,具有一个输入层,三个隐藏层,一个输出层,其中输入层具有12个节点,每个隐藏层都有10个节点,输出层有8个节点。同时,我们将使用Leaky ReLU激活函数,Adam优化器,mini-batch的大小为50,epoch数量为200。
```
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
# 定义自定义数据集
class MyDataset(Dataset):
def __init__(self, data, labels):
self.data = data
self.labels = labels
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
return self.data[idx], self.labels[idx]
# 导入数据
data = pd.read_excel('data.xlsx')
labels = pd.read_excel('labels.xlsx')
# 归一化处理
data = (data - data.mean()) / data.std()
labels = (labels - labels.mean()) / labels.std()
# 划分训练集和验证集
train_data = data[:int(len(data)*0.9)]
train_labels = labels[:int(len(labels)*0.9)]
val_data = data[int(len(data)*0.9):]
val_labels = labels[int(len(labels)*0.9):]
# 定义模型
class MyModel(nn.Module):
def __init__(self):
super(MyModel, self).__init__()
self.fc1 = nn.Linear(12, 10)
self.fc2 = nn.Linear(10, 10)
self.fc3 = nn.Linear(10, 10)
self.fc4 = nn.Linear(10, 8)
self.relu = nn.LeakyReLU()
def forward(self, x):
x = self.relu(self.fc1(x))
x = self.relu(self.fc2(x))
x = self.relu(self.fc3(x))
x = self.fc4(x)
return x
# 定义超参数
batch_size = 50
epochs = 200
learning_rate = 0.001
lr_decay = 0.1
lr_step_size = 50
# 初始化模型和优化器
model = MyModel()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
# 定义损失函数
criterion = nn.MSELoss()
# 定义训练集和验证集的DataLoader
train_dataset = MyDataset(train_data.values, train_labels.values)
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
val_dataset = MyDataset(val_data.values, val_labels.values)
val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False)
# 训练模型
train_loss = []
val_loss = []
for epoch in range(epochs):
# 训练集
model.train()
train_loss_epoch = 0
for i, (inputs, targets) in enumerate(train_loader):
optimizer.zero_grad()
outputs = model(inputs.float())
loss = criterion(outputs, targets.float())
loss.backward()
optimizer.step()
train_loss_epoch += loss.item()
train_loss.append(train_loss_epoch / len(train_loader))
# 验证集
model.eval()
val_loss_epoch = 0
with torch.no_grad():
for inputs, targets in val_loader:
outputs = model(inputs.float())
loss = criterion(outputs, targets.float())
val_loss_epoch += loss.item()
val_loss.append(val_loss_epoch / len(val_loader))
# 学习率下降
if (epoch + 1) % lr_step_size == 0:
learning_rate *= lr_decay
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
# 打印损失函数
print('Epoch [{}/{}], Train Loss: {:.4f}, Val Loss: {:.4f}'.format(epoch+1, epochs, train_loss[-1], val_loss[-1]))
# 导出损伤函数曲线
import matplotlib.pyplot as plt
plt.plot(train_loss, label='Train Loss')
plt.plot(val_loss, label='Val Loss')
plt.legend()
plt.show()
```
希望这个代码示例能够帮助你开始编写自己的神经网络。