提示错误
Traceback (most recent call last):
File "G:/PYxiangmu/les_1/les4.py", line 97, in
output = mymodel(data)
File "D:\pyanzhuang38\lib\site-packages\torch\nn\modules\module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "G:/PYxiangmu/les_1/les4.py", line 57, in forward
x = self.conv1(x)
File "D:\pyanzhuang38\lib\site-packages\torch\nn\modules\module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "D:\pyanzhuang38\lib\site-packages\torch\nn\modules\container.py", line 141, in forward
input = module(input)
File "D:\pyanzhuang38\lib\site-packages\torch\nn\modules\module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "D:\pyanzhuang38\lib\site-packages\torch\nn\modules\conv.py", line 446, in forward
return self._conv_forward(input, self.weight, self.bias)
File "D:\pyanzhuang38\lib\site-packages\torch\nn\modules\conv.py", line 442, in _conv_forward
return F.conv2d(input, weight, bias, self.stride,
TypeError: conv2d() received an invalid combination of arguments - got (int, Parameter, Parameter, tuple, tuple, tuple, int), but expected one of:
import torch
import torch.nn as nn
from torchvision import datasets, transforms
from torchvision.datasets import MNIST
import torch.optim as optim
import numpy as np
import torch.nn.functional as F
import matplotlib.pyplot as plt
from torch.utils.data import DataLoader
# -- 定义超参数
input_size = 28 # -- 图片尺寸28*28
num_class = 10 # -- 最后分类情况 10分类0-9
num_epochs = 3 # -- 循环3个周期
batch_size = 64 # -- batch大小64
# -- 训练集
train_dataset = MNIST(root="./data/", train=True, transform=transforms.ToTensor(), download=True)
# -- 测试集
test_dataset = MNIST(root="./data/", train=False, transform=transforms.ToTensor())
# -- 构建batch数据
train_loader = DataLoader(dataset=train_dataset,
batch_size=batch_size,
shuffle=True)
test_loader = DataLoader(dataset=test_dataset,
batch_size=batch_size,
shuffle=True)
# -- 定义模型
class CNN_Model(nn.Module):
def __init__(self):
super(CNN_Model, self).__init__()
# -- 第一个卷积单元 卷积 relu 池化 输入图为28*28*1 输出结果为 16 *14 *14
self.conv1 = nn.Sequential(
nn.Conv2d(in_channels=1, # -- 输入的灰度图 1通道 所以是1
out_channels=16, # -- 想得到多少个特征图 输出的特征图16个
kernel_size=5, # -- 卷积核的大小 5*5 提取成1个点
stride=1, # -- 步长为1
padding=2 # -- 填充0
),
nn.ReLU(), # -- relu层
nn.MaxPool2d(kernel_size=2) # -- 池化操作 输出结果为 16 *14 *14
)
# -- 第二个卷积单元 输入为16*14*14 输出10分类
self.conv2 = nn.Sequential(
nn.Conv2d(16, 32, 5, 1, 2), # -- 输入为16*14*14
nn.ReLU(),
nn.MaxPool2d(2) # -- 输出为32*7*7
)
self.out = nn.Linear(32 * 7 * 7, 10) # -- 全连接层 输出10分类
# -- 前向传播
def forward(self, x):
x = self.conv1(x)
x = self.conv2(x)
x = x.view(x.size(0), -1) # -- flatten操作 拉成一维数组形式 才能进行全连接输出
output = self.out(x)
return output
# -- 定义一个评估函数
def pinggu(predictions, labels):
pred = torch.max(predictions.data, 1)[1]
right = pred.eq(labels.data.view_as(pred)).sum()
return right, len(labels)
# -- 训练网络模型
# -- 实例化
mymodel = CNN_Model()
# -- 损失函数
loss_fn = nn.CrossEntropyLoss()
# -- 优化器 # 普通的随机梯度下降
optimizer = optim.Adam(mymodel.parameters(), lr=0.001)
# -- 开始训练
for epoch in range(num_epochs):
# -- 当前epoch结果保存起来
train_right = []
for batch_idx, (data, target) in enumerate(train_loader):
mymodel.train()
output = mymodel(data)
loss = loss_fn(output, target)
optimizer.zero_grad()
loss.backward()
optimizer.step()
right = pinggu(output, target)
train_right.append(right)
if batch_idx % 100 == 0:
mymodel.eval()
val_right = []
for (data, target) in enumerate(test_loader):
output = mymodel(data)
right = pinggu(output, target)
val_right.append(right)
# -- 准确率计算
train_r = (sum([tup[0] for tup in train_right]), sum([tup[1] for tup in train_right]))
val_r = (sum([tup[0] for tup in val_right]), sum([tup[1] for tup in val_right]))
print('当前epoch:{} [{}/{} ({:.0f}%)]\t 损失:{:.6f}\t 训练集准确率: {:.2f}%\t 测试集准确率:{:.2f}%'.format(
epoch, batch_idx * batch_size, len(train_loader.dataset),
100. * batch_idx / len(train_loader),
loss.data,
100. * train_r[0].numpy() / train_r[1],
100. * val_r[0].numpy() / val_r[1]
))
你好，解决了吗，我也遇到这个问题
你好，请问解决了吗，我也遇到了类似的问题！
Conv2d 2维卷积的第三个参数kernel_size 是元组类型 （x,y)这种， 你这参数5.，是啥意思？一维卷积？ 看你的代码，改成kernel_size = （5，5）吧