torch.autograd.Variable模块运行会return哪些数据
时间: 2024-05-28 21:08:56 浏览: 6
torch.autograd.Variable模块运行会返回包含了以下数据的变量:
1. 数据本身(tensor)
2. 是否需要进行梯度计算的标志(requires_grad)
3. 数据所处计算图中的父节点 tensor 的信息(grad_fn)
4. 变量在计算图中的名字(name)
希望我的回答对您有帮助。
相关问题
import torch import numpy as np import matplotlib.pyplot as plt from torch import nn, optim from torch.autograd import Variable x_data = np.random.rand(100) noise = np.random.normal(0, 0.01, x_data.shape) y_data = 0.1*x_data+0.2+noise # plt.scatter(x_data, y_data) # plt.show() x_data = x_data.reshape(-1, 1) y_data = y_data.reshape(-1, 1) # 把numpy数据变成张量tensor数据 x_data = torch.FloatTensor(x_data) y_data = torch.FloatTensor(y_data) # 构建网络模型 inputs = Variable(x_data) target = Variable(y_data) class LinearRegression(nn.Module): # 初始化,定义网络结构 # 一般把网络中具有可学习的参数的层放在初始化的里面,__int__()中 def __int__(self): super(LinearRegression, self).__init__() self.fc = nn.Linear(1, 1) # 定义网络计算 def forward(self, x): out = self.fc(x) return out # 实例化模型 model = LinearRegression() # 定义代价函数 mse_loss = nn.MSELoss() # 定义优化器 optimizer = optim.SGD(model.parameters(), lr=0.1) # 查看模型参数 for name, parameters in model.named_parameters(): print('name:{},parameters:{}'.format(name, parameters))
这段代码使用了Python中的一些库和模块,包括torch、numpy和matplotlib.pyplot,还有torch中的nn、optim模块和Variable函数。
首先,通过numpy库生成了一个包含100个随机数的数组x_data,同时也生成了一些符合正态分布的噪声noise。然后,根据公式y_data = 0.1*x_data + 0.2*noise,生成了相应的y_data数组。
接下来,使用torch中的nn模块定义神经网络模型,使用optim模块定义优化器,然后使用autograd中的Variable函数将x_data和y_data转化为可以进行自动求导的变量。这些步骤通常是构建神经网络模型并进行训练的前置准备。
最后,这段代码可以用来训练一个神经网络模型,以便预测y_data值。
下面的这段python代码,哪里有错误,修改一下:import numpy as np import matplotlib.pyplot as plt import pandas as pd import torch import torch.nn as nn from torch.autograd import Variable from sklearn.preprocessing import MinMaxScaler training_set = pd.read_csv('CX2-36_1971.csv') training_set = training_set.iloc[:, 1:2].values def sliding_windows(data, seq_length): x = [] y = [] for i in range(len(data) - seq_length): _x = data[i:(i + seq_length)] _y = data[i + seq_length] x.append(_x) y.append(_y) return np.array(x), np.array(y) sc = MinMaxScaler() training_data = sc.fit_transform(training_set) seq_length = 1 x, y = sliding_windows(training_data, seq_length) train_size = int(len(y) * 0.8) test_size = len(y) - train_size dataX = Variable(torch.Tensor(np.array(x))) dataY = Variable(torch.Tensor(np.array(y))) trainX = Variable(torch.Tensor(np.array(x[1:train_size]))) trainY = Variable(torch.Tensor(np.array(y[1:train_size]))) testX = Variable(torch.Tensor(np.array(x[train_size:len(x)]))) testY = Variable(torch.Tensor(np.array(y[train_size:len(y)]))) class LSTM(nn.Module): def __init__(self, num_classes, input_size, hidden_size, num_layers): super(LSTM, self).__init__() self.num_classes = num_classes self.num_layers = num_layers self.input_size = input_size self.hidden_size = hidden_size self.seq_length = seq_length self.lstm = nn.LSTM(input_size=input_size, hidden_size=hidden_size, num_layers=num_layers, batch_first=True) self.fc = nn.Linear(hidden_size, num_classes) def forward(self, x): h_0 = Variable(torch.zeros( self.num_layers, x.size(0), self.hidden_size)) c_0 = Variable(torch.zeros( self.num_layers, x.size(0), self.hidden_size)) # Propagate input through LSTM ula, (h_out, _) = self.lstm(x, (h_0, c_0)) h_out = h_out.view(-1, self.hidden_size) out = self.fc(h_out) return out num_epochs = 2000 learning_rate = 0.001 input_size = 1 hidden_size = 2 num_layers = 1 num_classes = 1 lstm = LSTM(num_classes, input_size, hidden_size, num_layers) criterion = torch.nn.MSELoss() # mean-squared error for regression optimizer = torch.optim.Adam(lstm.parameters(), lr=learning_rate) # optimizer = torch.optim.SGD(lstm.parameters(), lr=learning_rate) runn = 10 Y_predict = np.zeros((runn, len(dataY))) # Train the model for i in range(runn): print('Run: ' + str(i + 1)) for epoch in range(num_epochs): outputs = lstm(trainX) optimizer.zero_grad() # obtain the loss function loss = criterion(outputs, trainY) loss.backward() optimizer.step() if epoch % 100 == 0: print("Epoch: %d, loss: %1.5f" % (epoch, loss.item())) lstm.eval() train_predict = lstm(dataX) data_predict = train_predict.data.numpy() dataY_plot = dataY.data.numpy() data_predict = sc.inverse_transform(data_predict) dataY_plot = sc.inverse_transform(dataY_plot) Y_predict[i,:] = np.transpose(np.array(data_predict)) Y_Predict = np.mean(np.array(Y_predict)) Y_Predict_T = np.transpose(np.array(Y_Predict))
在这段代码中,可能存在以下错误:
1. 缺少必要的库或模块。
2. training_set 的文件路径是否正确。
3. training_set 的数据处理是否正确。
4. LSTM 模型的定义是否正确。
5. 训练和预测的代码是否正确。
下面是修改后的代码:
```python
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import torch
import torch.nn as nn
from torch.autograd import Variable
from sklearn.preprocessing import MinMaxScaler
# 读取数据
training_set = pd.read_csv('CX2-36_1971.csv')
training_set = training_set.iloc[:, 1:2].values
# 定义数据处理函数
def sliding_windows(data, seq_length):
x = []
y = []
for i in range(len(data) - seq_length):
_x = data[i:(i + seq_length)]
_y = data[i + seq_length]
x.append(_x)
y.append(_y)
return np.array(x), np.array(y)
# 对数据进行归一化处理
sc = MinMaxScaler()
training_data = sc.fit_transform(training_set)
# 定义窗口长度
seq_length = 1
# 对数据进行窗口划分
x, y = sliding_windows(training_data, seq_length)
# 划分训练集和测试集
train_size = int(len(y) * 0.8)
test_size = len(y) - train_size
dataX = Variable(torch.Tensor(np.array(x)))
dataY = Variable(torch.Tensor(np.array(y)))
trainX = Variable(torch.Tensor(np.array(x[1:train_size])))
trainY = Variable(torch.Tensor(np.array(y[1:train_size])))
testX = Variable(torch.Tensor(np.array(x[train_size:len(x)])))
testY = Variable(torch.Tensor(np.array(y[train_size:len(y)])))
# 定义 LSTM 模型
class LSTM(nn.Module):
def __init__(self, num_classes, input_size, hidden_size, num_layers):
super(LSTM, self).__init__()
self.num_classes = num_classes
self.num_layers = num_layers
self.input_size = input_size
self.hidden_size = hidden_size
self.seq_length = seq_length
self.lstm = nn.LSTM(input_size=input_size, hidden_size=hidden_size,
num_layers=num_layers, batch_first=True)
self.fc = nn.Linear(hidden_size, num_classes)
def forward(self, x):
h_0 = Variable(torch.zeros(
self.num_layers, x.size(0), self.hidden_size))
c_0 = Variable(torch.zeros(
self.num_layers, x.size(0), self.hidden_size))
# Propagate input through LSTM
ula, (h_out, _) = self.lstm(x, (h_0, c_0))
h_out = h_out.view(-1, self.hidden_size)
out = self.fc(h_out)
return out
# 定义训练参数
num_epochs = 2000
learning_rate = 0.001
input_size = 1
hidden_size = 2
num_layers = 1
num_classes = 1
# 实例化 LSTM 模型
lstm = LSTM(num_classes, input_size, hidden_size, num_layers)
# 定义损失函数和优化器
criterion = torch.nn.MSELoss()
optimizer = torch.optim.Adam(lstm.parameters(), lr=learning_rate)
# 训练模型
runn = 10
Y_predict = np.zeros((runn, len(dataY)))
for i in range(runn):
print('Run: ' + str(i + 1))
for epoch in range(num_epochs):
outputs = lstm(trainX)
optimizer.zero_grad()
loss = criterion(outputs, trainY)
loss.backward()
optimizer.step()
if epoch % 100 == 0:
print("Epoch: %d, loss: %1.5f" % (epoch, loss.item()))
lstm.eval()
train_predict = lstm(dataX)
data_predict = train_predict.data.numpy()
dataY_plot = dataY.data.numpy()
# 对结果进行反归一化
data_predict = sc.inverse_transform(data_predict)
dataY_plot = sc.inverse_transform(dataY_plot)
Y_predict[i,:] = np.transpose(np.array(data_predict))
Y_Predict = np.mean(Y_predict, axis=0)
Y_Predict_T = np.transpose(np.array(Y_Predict))
```
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