【发布时间】：2020-06-11 20:10:15
【问题描述】：
我正在尝试构建 CNN，但出现此错误：
---> 52 x = x.view(x.size(0), 5 * 5 * 16)
RuntimeError: shape '[16, 400]' is invalid for input of size 9600
我不清楚“x.view”行的输入应该是什么。另外，我真的不明白我的代码中应该有多少次这个“x.view”函数。是不是只有一次，在 3 个卷积层和 2 个线性层之后？或者是 5 次，每层后一个？
这是我的代码：
CNN
import torch.nn.functional as F
# Convolutional neural network
class ConvNet(nn.Module):
def __init__(self, num_classes=10):
super(ConvNet, self).__init__()
self.conv1 = nn.Conv2d(
in_channels=3,
out_channels=16,
kernel_size=3)
self.conv2 = nn.Conv2d(
in_channels=16,
out_channels=24,
kernel_size=4)
self.conv3 = nn.Conv2d(
in_channels=24,
out_channels=32,
kernel_size=4)
self.dropout = nn.Dropout2d(p=0.3)
self.pool = nn.MaxPool2d(2)
self.fc1 = nn.Linear(16 * 5 * 5, 120)
self.fc2 = nn.Linear(512, 10)
self.final = nn.Softmax(dim=1)
def forward(self, x):
print('shape 0 ' + str(x.shape))
x = F.max_pool2d(F.relu(self.conv1(x)), 2)
x = self.dropout(x)
print('shape 1 ' + str(x.shape))
x = F.max_pool2d(F.relu(self.conv2(x)), 2)
x = self.dropout(x)
print('shape 2 ' + str(x.shape))
# x = F.max_pool2d(F.relu(self.conv3(x)), 2)
# x = self.dropout(x)
x = F.interpolate(x, size=(5, 5))
x = x.view(x.size(0), 5 * 5 * 16)
x = self.fc1(x)
return x
net = ConvNet()
谁能帮我理解这个问题？
'x.shape' 的输出是：
形状 0 torch.Size([16, 3, 256, 256])
shape 1 torch.Size([16, 16, 127, 127])
形状 2 火炬.Size([16, 24, 62, 62])
谢谢
【问题讨论】：
标签： neural-network artificial-intelligence pytorch conv-neural-network