使用 PyTorch 实现自定义数据集

Question

我正在尝试修改取自 https://github.com/yunjey/pytorch-tutorial/blob/master/tutorials/01-basics/feedforward_neural_network/main.py 的这个前馈网络 利用我自己的数据集。

我定义了一个包含两个 1 dim 数组的自定义数据集作为输入，两个标量作为相应的输出：

x = torch.tensor([[5.5, 3,3,4] , [1 , 2,3,4], [9 , 2,3,4]])
print(x)

y = torch.tensor([1,2,3])
print(y)

import torch.utils.data as data_utils

my_train = data_utils.TensorDataset(x, y)
my_train_loader = data_utils.DataLoader(my_train, batch_size=50, shuffle=True)

我已更新超参数以匹配新的 input_size (2) 和 num_classes (3)。

我也将images = images.reshape(-1, 28*28).to(device) 更改为images = images.reshape(-1, 4).to(device)

由于训练集很小，我将 batch_size 更改为 1。

进行这些修改后，我在尝试训练时收到错误：

RuntimeError Traceback（最近调用 最后）在（） 51 52#前传 ---> 53 个输出 = 模型（图像） 54损失=标准（输出，标签） 55

/home/.local/lib/python3.6/site-packages/torch/nn/modules/module.py in call(self, *input, **kwargs) 第489章 490 其他： --> 491 结果 = self.forward(*input, **kwargs) 492 for hook in self._forward_hooks.values(): 493 hook_result = hook（自我，输入，结果）

向前(self, x) 31 32 def 前进（自我，x）： ---> 33 出 = self.fc1(x) 34 out = self.relu(out) 35 out = self.fc2(out)

/home/.local/lib/python3.6/site-packages/torch/nn/modules/module.py in call(self, *input, **kwargs) 第489章 490 其他： --> 491 结果 = self.forward(*input, **kwargs) 492 for hook in self._forward_hooks.values(): 493 hook_result = hook（自我，输入，结果）

/home/.local/lib/python3.6/site-packages/torch/nn/modules/linear.py in forward(self, input) 53 54 def forward（自我，输入）： ---> 55 返回 F.linear(input, self.weight, self.bias) 56 57 def extra_repr（自我）：

/home/.local/lib/python3.6/site-packages/torch/nn/functional.py 线性（输入，权重，偏差） 990 如果 input.dim() == 2 并且偏差不是无： 991 # 融合运算稍微快一点 --> 992 return torch.addmm(bias, input, weight.t()) 993 994 输出 = input.matmul(weight.t())

RuntimeError：大小不匹配，m1：[3 x 4]，m2：[2 x 3] /pytorch/aten/src/THC/generic/THCTensorMathBlas.cu:249

如何修改代码以匹配预期的维度？我不确定要更改哪些代码，因为我更改了所有需要更新的参数？

更改前的来源：

import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms


# Device configuration
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

# Hyper-parameters 
input_size = 784
hidden_size = 500
num_classes = 10
num_epochs = 5
batch_size = 100
learning_rate = 0.001

# MNIST dataset 
train_dataset = torchvision.datasets.MNIST(root='../../data', 
                                           train=True, 
                                           transform=transforms.ToTensor(),  
                                           download=True)

test_dataset = torchvision.datasets.MNIST(root='../../data', 
                                          train=False, 
                                          transform=transforms.ToTensor())

# Data loader
train_loader = torch.utils.data.DataLoader(dataset=train_dataset, 
                                           batch_size=batch_size, 
                                           shuffle=True)

test_loader = torch.utils.data.DataLoader(dataset=test_dataset, 
                                          batch_size=batch_size, 
                                          shuffle=False)

# Fully connected neural network with one hidden layer
class NeuralNet(nn.Module):
    def __init__(self, input_size, hidden_size, num_classes):
        super(NeuralNet, self).__init__()
        self.fc1 = nn.Linear(input_size, hidden_size) 
        self.relu = nn.ReLU()
        self.fc2 = nn.Linear(hidden_size, num_classes)  

    def forward(self, x):
        out = self.fc1(x)
        out = self.relu(out)
        out = self.fc2(out)
        return out

model = NeuralNet(input_size, hidden_size, num_classes).to(device)

# Loss and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)  

# Train the model
total_step = len(train_loader)
for epoch in range(num_epochs):
    for i, (images, labels) in enumerate(train_loader):  
        # Move tensors to the configured device
        images = images.reshape(-1, 28*28).to(device)
        labels = labels.to(device)

        # Forward pass
        outputs = model(images)
        loss = criterion(outputs, labels)

        # Backward and optimize
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        if (i+1) % 100 == 0:
            print ('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}' 
                   .format(epoch+1, num_epochs, i+1, total_step, loss.item()))

# Test the model
# In test phase, we don't need to compute gradients (for memory efficiency)
with torch.no_grad():
    correct = 0
    total = 0
    for images, labels in test_loader:
        images = images.reshape(-1, 28*28).to(device)
        labels = labels.to(device)
        outputs = model(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

    print('Accuracy of the network on the 10000 test images: {} %'.format(100 * correct / total))

# Save the model checkpoint
torch.save(model.state_dict(), 'model.ckpt')

来源帖子更改：

x = torch.tensor([[5.5, 3,3,4] , [1 , 2,3,4], [9 , 2,3,4]])
print(x)

y = torch.tensor([1,2,3])
print(y)

import torch.utils.data as data_utils

my_train = data_utils.TensorDataset(x, y)
my_train_loader = data_utils.DataLoader(my_train, batch_size=50, shuffle=True)

print(my_train)

print(my_train_loader)

import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms


# Device configuration
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

# Hyper-parameters 
input_size = 2
hidden_size = 3
num_classes = 3
num_epochs = 5
batch_size = 1
learning_rate = 0.001

# MNIST dataset 
train_dataset = my_train

# Data loader
train_loader = my_train_loader

# Fully connected neural network with one hidden layer
class NeuralNet(nn.Module):
    def __init__(self, input_size, hidden_size, num_classes):
        super(NeuralNet, self).__init__()
        self.fc1 = nn.Linear(input_size, hidden_size) 
        self.relu = nn.ReLU()
        self.fc2 = nn.Linear(hidden_size, num_classes)  

    def forward(self, x):
        out = self.fc1(x)
        out = self.relu(out)
        out = self.fc2(out)
        return out

model = NeuralNet(input_size, hidden_size, num_classes).to(device)

# Loss and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)  

# Train the model
total_step = len(train_loader)
for epoch in range(num_epochs):
    for i, (images, labels) in enumerate(train_loader):  
        # Move tensors to the configured device
        images = images.reshape(-1, 4).to(device)
        labels = labels.to(device)

        # Forward pass
        outputs = model(images)
        loss = criterion(outputs, labels)

        # Backward and optimize
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        if (i+1) % 100 == 0:
            print ('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}' 
                   .format(epoch+1, num_epochs, i+1, total_step, loss.item()))

# Test the model
# In test phase, we don't need to compute gradients (for memory efficiency)
with torch.no_grad():
    correct = 0
    total = 0
    for images, labels in test_loader:
        images = images.reshape(-1, 4).to(device)
        labels = labels.to(device)
        outputs = model(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

    print('Accuracy of the network on the 10000 test images: {} %'.format(100 * correct / total))

# Save the model checkpoint
torch.save(model.state_dict(), 'model.ckpt')

Answer 1

您需要将 input_size 更改为 4 (2*2)，而不是修改后的代码当前显示的 2。
如果将其与原始 MNIST 示例进行比较，您会发现 input_size 设置为 784 (28*28) 而不仅仅是 28。