[人工智能]pytorch做回归任务

记录写的第一个深度学习网络–pytorch做回归任务

数据格式

用open-change7个变量预测label

代码

#  导入库
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import torch
from sklearn import preprocessing

#  导入数据集
#  训练集和测试集导入数据
data = pd.read_csv(r'stock_train.csv',encoding='utf-8')
data_test1 = pd.read_csv(r'stock_test.csv',encoding='utf-8')
#  导入训练集因变量
y_train = np.array(data.label)
y_train = y_train.reshape(-1, 1)
y_train = preprocessing.StandardScaler().fit_transform(y_train)
#  导入训练集自变量
data_train = data.drop('label', axis = 1)
data_train = data_train.drop(columns = 'date', axis = 1)
x_train = np.array(data_train)
x_train = preprocessing.StandardScaler().fit_transform(x_train)
#  导入测试集因变量
y_test = np.array(data_test1.label)
y_test = y_test.reshape(-1, 1)
y_test = preprocessing.StandardScaler().fit_transform(y_test)
#  导入测试集自变量
data_test = data_test1.drop('label', axis = 1)
data_test = data_test.drop(columns = 'date', axis = 1)
x_test = np.array(data_test)
x_test = preprocessing.StandardScaler().fit_transform(x_test)

#  设置参数，有7个输入特征，一个含有128个神经元的隐藏层，因为是回归问题所以一个输出特征
input_size = 7
hidden_size = 128
output_size = 1
batch_size = 16
#  构建网络
model = torch.nn.Sequential(
    # 7--》128
    torch.nn.Linear(input_size, hidden_size),
    #  激活函数
    torch.nn.Sigmoid(),
    # 128--》1
    torch.nn.Linear(hidden_size, output_size)

)
#  定义损失函数，因为是回归均方误差采用MSE
cost = torch.nn.MSELoss()
#  定义优化器
optimizer = torch.optim.Adam(model.parameters(), lr = 0.0001)

losses = []
train_loss = []
#  定义迭代次数，尝试后发现迭代500次结果已经很好了
for i in range(500):
    batch_loss = []  #  记录每一个batch的损失值
    pre = []
    for start in range(0, len(data_train), batch_size):
        end = start + batch_size if start + batch_size < len(data_train) else len(data_train)
        # 输入一个batch数据的自变量
        xx = torch.tensor(x_train[start:end], dtype = torch.float, requires_grad = True)
        # 输入一个batch数据的因变量
        yy = torch.tensor(y_train[start:end], dtype = torch.float, requires_grad = True)
        # 前向传播
        prediction = model(xx)
        # 计算损失值
        loss = cost(prediction, yy)
        # 梯度清零
        optimizer.zero_grad()
        # 反向传播
        loss.backward(retain_graph=True)
        # 更新参数
        optimizer.step()
        batch_loss.append(loss.data.numpy())
        train_loss=batch_loss #  记录最后一次迭代的损失值
print(np.mean(train_loss))

#  测试数据
x = torch.tensor(x_test, dtype = torch.float)
predict = model(x).data.numpy()
plt.plot(data_test1['date'], y_test, label = 'truth_ground')
plt.plot(data_test1['date'], predict, label = 'test_pre')
plt.legend()
plt.show()

#  验证数据
x = [210.34,211.86,209.71,211.97,32650000,22263100,0.008520969]
x = x.reshape(1, -1)
x = preprocessing.StandardScaler().fit_transform(x)
x_vertify = torch.tensor(x, dtype = torch.float)
predict = model(x_vertify).data.numpy()
print(predict)

一些记录

1. x.reshape（1,-1）含义是将x改变成一行，任意列大小的数组，-1的含义是不用指定具体个数
2. torch.nn.MSELoss() reduction = ‘none’则返回一组向量，reduction ≠ ‘none’则返回一个数，有sum和mean（默认）两种方法
3. torch.nn.X和torch.nn.functional.X区别：前面的是类用之前需要实例化，后面的是函数直接调用