前言:回归任务是神经网络的一个重要的任务,通俗的讲,回归任务就是给你一系列的输入,然后预测出输出的任务,比如预测气温,预测股票等等,都是回归任务。
下面还是直接看代码,根据代码来学习回归任务
第一步:处理输入数据
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import torch
import torch.optim as optim
import warnings
打印看一下数据的样子
features = pd.read_csv('temps.csv')
#看看数据长什么样子
features.head()
actual为标签,其余的均为输入
print('数据维度:', features.shape)
打印以下数据的形状:(348,9)
将输入数据可视化:
# 准备画图
# 指定默认风格
plt.style.use('fivethirtyeight')
# 设置布局
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(nrows=2, ncols=2, figsize = (10,10))
fig.autofmt_xdate(rotation = 45)
# 标签值
ax1.plot(dates, features['actual'])
ax1.set_xlabel(''); ax1.set_ylabel('Temperature'); ax1.set_title('Max Temp')
# 昨天
ax2.plot(dates, features['temp_1'])
ax2.set_xlabel(''); ax2.set_ylabel('Temperature'); ax2.set_title('Previous Max Temp')
# 前天
ax3.plot(dates, features['temp_2'])
ax3.set_xlabel('Date'); ax3.set_ylabel('Temperature'); ax3.set_title('Two Days Prior Max Temp')
ax4.plot(dates, features['friend'])
ax4.set_xlabel('Date'); ax4.set_ylabel('Temperature'); ax4.set_title('Friend Estimate')
plt.tight_layout(pad=2)
结果为:
然后把星期几转化为独热编码的格式,独热编码就是不重复样本数,将所有样本按照0或者1进行编码,如:0000001代表星期日
features = pd.get_dummies(features)
features.head(5)
最后编码后的结果为:
将数据处理成特征和标签的形式:
# 标签
labels = np.array(features['actual'])
# 在特征中去掉标签
features= features.drop('actual', axis = 1)
# 名字单独保存一下,以备后患
feature_list = list(features.columns)
# 转换成合适的格式
features = np.array(features)
features.shape
labels.shape
最后得到的特征为(348,14),标签为(348,)
第二步:构建网络模型
x = torch.tensor(input_features, dtype = float)
y = torch.tensor(labels, dtype = float)
# 权重参数初始化
weights = torch.randn((14, 128), dtype = float, requires_grad = True)
biases = torch.randn(128, dtype = float, requires_grad = True)
weights2 = torch.randn((128, 1), dtype = float, requires_grad = True)
biases2 = torch.randn(1, dtype = float, requires_grad = True)
learning_rate = 0.001
losses = []
for i in range(1000):
# 计算隐层
hidden = x.mm(weights) + biases
# 加入激活函数
hidden = torch.relu(hidden)
# 预测结果
predictions = hidden.mm(weights2) + biases2
# 通计算损失
loss = torch.mean((predictions - y) ** 2)
losses.append(loss.data.numpy())
# 打印损失值
if i % 100 == 0:
print('loss:', loss)
#返向传播计算
loss.backward()
#更新参数
weights.data.add_(- learning_rate * weights.grad.data)
biases.data.add_(- learning_rate * biases.grad.data)
weights2.data.add_(- learning_rate * weights2.grad.data)
biases2.data.add_(- learning_rate * biases2.grad.data)
# 每次迭代都得记得清空
weights.grad.data.zero_()
biases.grad.data.zero_()
weights2.grad.data.zero_()
biases2.grad.data.zero_()
上面的网络模型的构建是具体的过程,有助于理解,实际中一般都是用具体的包,流程为:将数据装化为张量格式---->初始化权重参数和偏置项---->计算前向传播结果---->计算损失---->反向传播---->沿梯度更新参数---->将梯度清零。
更将单的写法为:
input_size = input_features.shape[1]
hidden_size = 128
output_size = 1
batch_size = 16
my_nn = torch.nn.Sequential(
torch.nn.Linear(input_size, hidden_size),
torch.nn.Sigmoid(),
torch.nn.Linear(hidden_size, output_size),
)
cost = torch.nn.MSELoss(reduction='mean')
optimizer = torch.optim.Adam(my_nn.parameters(), lr = 0.001)
接下来就是最重要的训练网络:
# 训练网络
losses = []
for i in range(1000):
batch_loss = []
# MINI-Batch方法来进行训练
for start in range(0, len(input_features), batch_size):
end = start + batch_size if start + batch_size < len(input_features) else len(input_features)
xx = torch.tensor(input_features[start:end], dtype = torch.float, requires_grad = True)
yy = torch.tensor(labels[start:end], dtype = torch.float, requires_grad = True)
prediction = my_nn(xx)
loss = cost(prediction, yy)
loss.backward(retain_graph=True)
optimizer.step()
optimizer.zero_grad()
batch_loss.append(loss.data.numpy())
# 打印损失
if i % 100==0:
losses.append(np.mean(batch_loss))
print(i, np.mean(batch_loss))
流程为:获取batch数据---->送入网络---->获取预测值---->计算损失---->反向传播---->沿梯度更新参数---->将梯度清零
最后可视化
# 转换日期格式
dates = [str(int(year)) + '-' + str(int(month)) + '-' + str(int(day)) for year, month, day in zip(years, months, days)]
print(dates)
dates = [datetime.datetime.strptime(date, '%Y-%m-%d') for date in dates]
#print(dates)
# 创建一个表格来存日期和其对应的标签数值
true_data = pd.DataFrame(data = {'date': dates, 'actual': labels})
# 同理,再创建一个来存日期和其对应的模型预测值
months = features[:, feature_list.index('month')]
days = features[:, feature_list.index('day')]
years = features[:, feature_list.index('year')]
test_dates = [str(int(year)) + '-' + str(int(month)) + '-' + str(int(day)) for year, month, day in zip(years, months, days)]
test_dates = [datetime.datetime.strptime(date, '%Y-%m-%d') for date in test_dates]
predictions_data = pd.DataFrame(data = {'date': test_dates, 'prediction': predict.reshape(-1)})
# 真实值
plt.plot(true_data['date'], true_data['actual'], 'b-', label = 'actual')
# 预测值
plt.plot(predictions_data['date'], predictions_data['prediction'], 'ro', label = 'prediction')
plt.xticks(rotation = '60');
plt.legend()
# 图名
plt.xlabel('Date'); plt.ylabel('Maximum Temperature (F)'); plt.title('Actual and Predicted Values');
总结:
左边流程的链接:(23条消息) 神经网络入门(手写体的识别torch+jupyter+Mnist数据集)_萌新小白一只的博客-CSDN博客
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