【发布时间】：2021-04-17 00:46:08
【问题描述】：
我正在尝试调整为线性回归创建的 Pytorch 脚本。它最初是为了将一组随机值（使用 np.random 创建）作为特征和目标而编写的。
我现在创建了一个包含实际数据的数据框以供分析：
df = pd.read_csv('file_name.csv')
df 看起来像这样：
X1 X2 X3 X4 X5 X6 X7 X8 Y1 Y2
0 0.98 514.5 294.0 110.25 7.0 2 0.0 0 15.55 21.33
1 0.98 514.5 294.0 110.25 7.0 3 0.0 0 15.55 21.33
2 0.98 514.5 294.0 110.25 7.0 4 0.0 0 15.55 21.33
3 0.98 514.5 294.0 110.25 7.0 5 0.0 0 15.55 21.33
4 0.90 563.5 318.5 122.50 7.0 2 0.0 0 20.84 28.28
...我目前只提取两列（X1 和 X2）作为我的特征，一列（Y1）作为我的目标，如下所示：
x = df[['X1', 'X2']]
y = df['Y1']
所以功能看起来像这样：
X1 X2
0 0.98 514.5
1 0.98 514.5
2 0.98 514.5
3 0.98 514.5
4 0.90 563.5
目标看起来像这样：
Y1
0 15.55
1 15.55
2 15.55
3 15.55
4 20.84
但是，当我尝试将特征（X1 和 X1）和目标（Y1）转换为张量以便将它们提供给 NN 时，代码在以下行失败：
数据集 = TensorDataset(x_tensor_flat, y_tensor_flat)
我得到错误：
line 45, in <module> dataset = TensorDataset(x_tensor, y_tensor)
AssertionError: Size mismatch between tensors
显然存在一些塑造问题，但我不知道是什么。 我试图展平和转置张量，但我得到了同样的错误。 任何帮助将不胜感激。
这是导致问题的完整代码部分：
import pandas as pd
import torch
import torch.optim as optim
import torch.nn as nn
from torch.utils.data import Dataset, TensorDataset, DataLoader
from torch.utils.data.dataset import random_split
device = 'cuda' if torch.cuda.is_available() else 'cpu'
df = pd.read_csv('file_name.csv')
x = df[['X1', 'X2']]
y = df['Y1']
x_tensor = torch.from_numpy(np.array(x)).float()
y_tensor = torch.from_numpy(np.array(y)).float()
train_loader = DataLoader(dataset=train_dataset, batch_size=10)
val_loader = DataLoader(dataset=val_dataset, batch_size=10)
class ManualLinearRegression(nn.Module):
def __init__(self):
super().__init__()
self.linear = nn.Linear(2, 1)
def forward(self, x):
return self.linear(x)
def make_train_step(model, loss_fn, optimizer):
def train_step(x, y):
model.train()
yhat = model(x)
loss = loss_fn(y, yhat)
loss.backward()
optimizer.step()
optimizer.zero_grad()
return loss.item()
return train_step
torch.manual_seed(42)
model = ManualLinearRegression().to(device)
loss_fn = nn.MSELoss(reduction='mean')
optimizer = optim.SGD(model.parameters(), lr=1e-1)
train_step = make_train_step(model, loss_fn, optimizer)
n_epochs = 50
training_losses = []
validation_losses = []
print(model.state_dict())
for epoch in range(n_epochs):
batch_losses = []
for x_batch, y_batch in train_loader:
x_batch = x_batch.to(device)
y_batch = y_batch.to(device)
loss = train_step(x_batch, y_batch)
batch_losses.append(loss)
training_loss = np.mean(batch_losses)
training_losses.append(training_loss)
with torch.no_grad():
val_losses = []
for x_val, y_val in val_loader:
x_val = x_val.to(device)
y_val = y_val.to(device)
model.eval()
yhat = model(x_val)
val_loss = loss_fn(y_val, yhat).item()
val_losses.append(val_loss)
validation_loss = np.mean(val_losses)
validation_losses.append(validation_loss)
print(f"[{epoch+1}] Training loss: {training_loss:.3f}\t Validation loss: {validation_loss:.3f}")
print(model.state_dict())
【问题讨论】：
-
作为附加信息：如果我不展平张量，我会收到错误：“ValueError：输入长度的总和不等于输入数据集的长度！”