如何在 PyTorch 中检查正确的培训课程答案

【问题标题】：How to check the correct Training Class In PyTorch如何在 PyTorch 中检查正确的培训课程
【发布时间】：2018-02-27 08:29:45
【问题描述】：

我是 Pytorch 的新手，我试图编写我的培训课程，但我遇到了这个错误

import numpy as np
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as F
import torch.optim as optim

import tqdm

class MLPNet(nn.Module):
    def __init__(self):
        super(MLPNet, self).__init__()
        self.first_fully_connected = nn.Linear(8*8, 100)
        self.last_fully_connected = nn.Linear(100, 10)

    def forward(self, x):
        x = x.view(-1, 8*8) # reshape input tensor to the size (batch_size, 8*8)
        x = F.sigmoid(self.first_fully_connected(x))
        x = F.sigmoid(self.last_fully_connected(x))
        return x


def training(mlp, X, y, epochs=1, lr=.2, batch_size=101):               
    # solver
    # loss
    solver = torch.optim.SGD(mlp.parameters(), lr=lr, momentum=0.9)
    loss = nn.CrossEntropyLoss() #  nn.NLLLoss()

    n_batches = (len(X) + batch_size - 1) // batch_size
    for epoch in tqdm.tqdm(range(epochs)):
        for i in range(n_batches):
            slice_ = np.s_[i::n_batches]
            X_batch = Variable(torch.from_numpy(X[slice_])).float()
            y_batch = Variable(torch.from_numpy(y[slice_, np.newaxis])).float()

#             X_batch = Variable(torch.from_numpy(X[slice_])).long()
#             y_batch = Variable(torch.from_numpy(y[slice_, np.newaxis])).long()

            print(type(X_batch.data))
            print(type(y_batch.data))
            ### BEGIN: your optim step here. do not forget to reset gradients

            # Clear gradients w.r.t. parameters
            solver.zero_grad()

            prediction = mlp(X_batch)
            # Forward pass to get output/logits
            #outputs = mlp(X_batch)

            # Calculate Loss: softmax --> cross entropy loss
            #loss = criterion(outputs, y_batch)
            loss_f = loss(prediction, y_batch)

            # Getting gradients w.r.t. parameters
            loss_f.backward()

            # Updating parameters
            solver.step()                              
            ### END

    return mlp

mlp = nn.Sequential(
    #### Your net here    
    nn.Linear(2, 64),
    nn.ReLU(),
    nn.Linear(64, 2)
)
model_mlp = training(mlp, X_std, y_std)

但我得到了这个错误，我尝试更改类型，但仍然遇到该错误。我也尝试了更改损失函数，但仍然出现错误。

RuntimeError Traceback（最近一次调用最后一次） 在 () ----> 1 model_mlp = fit(mlp, X_std, y_std)

RuntimeError: Expected object of type Variable[torch.FloatTensor] but found type Variable[torch.LongTensor] for argument #1 'mat1'

非常感谢您提供的任何帮助。非常感谢

【问题讨论】：

标签： python neural-network deep-learning pytorch

【解决方案1】：

正如@Ioannis Nasios 在问题中编辑的那样，您有

X_batch = Variable(torch.from_numpy(X[slice_])).long()

这意味着您的 MLP 输入张量是 long 整数，但网络需要浮点数。所以你需要：

X_batch = Variable(torch.from_numpy(X[slice_])).float()

这应该可以解决您的错误。

【讨论】：