[英]Flatten Tensor in Pytorch Convolutional Neural Network (size mismatch error)
我用隨機像素做了一個可重復的例子。 我試圖在卷積層之后展平密集層的張量。 問題出在卷積層和密集層的交叉處。 我不知道如何放置正確數量的神經元。
tl;dr我正在尋找與keras.layers.Flatten()
等效的手冊,因為它在pytorch
中不存在。
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import DataLoader
x = np.random.rand(1_00, 3, 100, 100)
y = np.random.randint(0, 2, 1_00)
if torch.cuda.is_available():
x = torch.from_numpy(x.astype('float32')).cuda()
y = torch.from_numpy(y.astype('float32')).cuda()
class ConvNet(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(3, 32, 3)
self.conv2 = nn.Conv2d(32, 64, 3)
self.conv3 = nn.Conv2d(64, 128, 3)
self.fc1 = nn.Linear(128, 1024) # 128 is wrong here
self.fc2 = nn.Linear(1024, 1)
def forward(self, x):
x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))
x = F.max_pool2d(F.relu(self.conv2(x)), (2, 2))
x = F.max_pool2d(F.relu(self.conv3(x)), (2, 2))
x = x.view(x.size(0), -1)
x = F.relu(self.fc1(x))
x = torch.sigmoid(self.fc2(x))
return x
net = ConvNet()
net.cuda()
optimizer = optim.Adam(net.parameters(), lr=0.03)
loss_function = nn.BCELoss()
class Train:
def __init__(self):
self.len = x.shape[0]
self.x_train = x
self.y_train = y
def __getitem__(self, index):
return x[index], y[index].unsqueeze(0)
def __len__(self):
return self.len
train = Train()
train_loader = DataLoader(dataset=train, batch_size=64, shuffle=True)
epochs = 1
train_losses = list()
for e in range(epochs):
running_loss = 0
for images, labels in train_loader:
optimizer.zero_grad()
log_ps = net(images)
loss = loss_function(log_ps, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
print('It\'s working.')
您一定會遇到尺寸不匹配錯誤,對嗎?
那是因為應用卷積后結果的輸出形狀是[B, 128, 10, 10]
,因此.flatten
的結果將是形狀[B, 128*10*10]
。 所以你需要使用輸入大小為12800
的線性層。 那應該可以解決問題。
所以,只要改變
self.fc1 = nn.Linear(128, 1024) # 128 is wrong here
到
self.fc1 = nn.Linear(12800, 1024)
通常,為了獲得正確尺寸的想法,您可以在紙上計算輸出的形狀,或者僅在正確位置的 forward 函數中使用print(x.shape)
調試語句也可以完成這項工作。
這是我制作的一個函數,用於在展平卷積張量的同時自動擬合正確數量的神經元:
def flatten(w, k=3, s=1, p=0, m=True):
"""
Returns the right size of the flattened tensor after
convolutional transformation
:param w: width of image
:param k: kernel size
:param s: stride
:param p: padding
:param m: max pooling (bool)
:return: proper shape and params: use x * x * previous_out_channels
Example:
r = flatten(*flatten(*flatten(w=100, k=3, s=1, p=0, m=True)))[0]
self.fc1 = nn.Linear(r*r*128, 1024)
"""
return int((np.floor((w - k + 2 * p) / s) + 1) / 2 if m else 1), k, s, p, m
在你的情況下:
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(3, 32, 3)
self.conv2 = nn.Conv2d(32, 64, 3)
self.conv3 = nn.Conv2d(64, 128, 3)
r = flatten(*flatten(*flatten(w=100, k=3, s=1, p=0, m=True)))[0]
self.fc1 = nn.Linear(r*r*128, 1024)
self.fc2 = nn.Linear(1024, 1)
def forward(self, x): ...
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