我对 pytorch 很陌生，并且有一个我相信可以解压和运行数据的系统，但是当它这样做时，即使在数百个纪元之后，它返回的准确性仍然比随机猜测更糟糕。
我尝试过改变批量大小、层数、纪元数、学习率，但没有任何效果。这是完整的代码：
import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
import matplotlib.pyplot as pl
from torch.utils.data import Dataset
from torch.utils.data import DataLoader
import numpy as np
import torch.optim
# device config
#device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
device = torch.device('cpu')
input_size = 5
hidden_size = 10
num_classes = 3
num_epochs = 100
batch_size = 10
learning_rate = 0.0001
class SDSS(Dataset):
def __init__(self):
# Initialize data, download, etc.
# read with numpy or pandas
xy = np.loadtxt('SDSS.csv', delimiter=',', dtype=np.float32, skiprows=0)
self.n_samples = xy.shape[0]
# here the first column is the class label, the rest are the features
self.x_data = torch.from_numpy(xy[:, 1:]) # size [n_samples, n_features]
self.y_data = torch.from_numpy(xy[:, [0]]) # size [n_samples, 1]
# support indexing such that dataset[i] can be used to get i-th sample
def __getitem__(self, index):
return self.x_data[index], self.y_data[index]
# we can call len(dataset) to return the size
def __len__(self):
return self.n_samples
#I like having this separate, so I remember why I called it when I look back. Also, if I want to change only this later, I can.
class testSDSS(Dataset):
def __init__(self):
# Initialize data, download, etc.
# read with numpy or pandas
xy = np.loadtxt('SDSS.csv', delimiter=',', dtype=np.float32, skiprows=0)
self.n_samples = xy.shape[0]
# here the first column is the class label, the rest are the features
self.x_data = torch.from_numpy(xy[:, 1:]) # size [n_samples, n_features]
self.y_data = torch.from_numpy(xy[:, [0]]) # size [n_samples, 1]
# support indexing such that dataset[i] can be used to get i-th sample
def __getitem__(self, index):
return self.x_data[index], self.y_data[index]
# we can call len(dataset) to return the size
def __len__(self):
return self.n_samples
#easy to read labels
dataset = SDSS()
test_dataset = testSDSS()
data_loader = DataLoader(dataset=dataset, batch_size=batch_size, shuffle=True, num_workers=0)
test_loader = DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=False, num_workers=0)
#Use LeakyReLu to preserve backwards attempts
#softmax is applied in pytorch through cross entropy
class NeuralNet(nn.Module):
def __init__(self, input_size, hidden_size, num_classes):
super(NeuralNet,self).__init__()
self.l1 = nn.Linear(input_size, hidden_size)
self.relu = nn.LeakyReLU()
self.l2 = nn.Linear(hidden_size, num_classes)
def forward(self, x):
out = self.l1(x)
out = self.relu(out)
out = self.l2(out)
return out
model = NeuralNet(input_size, hidden_size, num_classes)
dataiter = iter(data_loader)
data = next(dataiter)
features, labels = data
# loss and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
#training loop
n_total_steps = len(dataset)
for epoch in range(num_epochs):
print(f'epoch: {epoch} / {num_epochs}')
for i, (inputs, labels) in enumerate(data_loader):
#forward
labels = labels.to(device)
outputs = model(inputs)
inputs = torch.flatten(inputs)
labels = torch.flatten(labels)
loss = criterion(outputs, labels.long())
#backward
optimizer.zero_grad()
loss.backward()
optimizer.step()
if (i+1)%100 == 0:
print(f'epoch {epoch + 1} / {num_epochs}, step {i+1}/{n_total_steps}, loss = {loss.item():.4f}')
#test
with torch.no_grad():
n_correct = 0
n_samples = 0
for inputs, labels in test_loader:
labels = labels.to(device)
outputs = model(inputs)
inputs = torch.flatten(inputs)
labels = torch.flatten(labels)
_, predictions = torch.max(outputs, 1)
n_samples += labels.shape[0]
n_correct = (predictions == labels).sum().item()
acc = 100 * n_correct / n_samples
print(f'accuracy = {acc}')
发生这种情况是因为您错误地累积了正确的预测 (
n_correct只需替换这一行：
n_correct = (predictions == labels).sum().item()
与：
n_correct += (predictions == labels).sum().item()
希望这对您有帮助！如果您还有其他问题，请联系我。谢谢！