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HIT-UG-Group merged 3 commits into from
May 10, 2022
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Aleafy May 10, 2022
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2 changes: 1 addition & 1 deletion README.md
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| <a href="https://www.bilibili.com/video/BV1AK4y1P7vs?spm_id_from=333.999.0.0"> <img src="./imgs/cover/16.png" width="170" /></a> | [16-Pytorch神经网络基础](notes/16-Pytorch神经网络基础.md) | [讲义](https://zh-v2.d2l.ai/chapter_deep-learning-computation/model-construction.html) | [Jupyter 代码](code/16-Pytorch神经网络基础) | <a href="https://github.com/Atream"> <img src="./imgs/profile/BoXin_Zhang.png" width="80" /></a> <a href="https://github.com/benmagnifico"><img src="./imgs/profile/JingGuang_Li.png" width="80" /></a> |
| <a href="https://www.bilibili.com/video/BV1z5411c7C1?spm_id_from=333.999.0.0"> <img src="./imgs/cover/17.png" width="170" /></a> | [17-使用和购买GPU](notes/17-使用和购买GPU.md) | [讲义](https://zh-v2.d2l.ai/chapter_deep-learning-computation/use-gpu.html) | [Jupyter 代码](code/17-使用和购买GPU.ipynb) | <a href="https://github.com/dcy-dhsunabzh"> <img src="./imgs/profile/ChenYang_Ding.png" width="80" /></a> <a href="https://github.com/fghccv"><img src="./imgs/profile/ShiQi_Zhou.png" width="80" /></a> |
| <a href="https://www.bilibili.com/video/BV15Q4y1o7vc?spm_id_from=333.999.0.0"> <img src="./imgs/cover/18.png" width="170" /></a> | [18-预测房价竞赛总结](notes/18-预测房价竞赛总结.md) | [讲义](https://courses.d2l.ai/zh-v2/assets/pdfs/part-1_1.pdf) | | <a href="https://github.com/Chigland"> <img src="./imgs/profile/ZhiTao_Wang.png" width="80" /></a> <a href="https://github.com/Khadorstorm"><img src="./imgs/profile/KeCheng_Zhang.png" width="80" /></a> |
| <a href="https://www.bilibili.com/video/BV1L64y1m7Nh?spm_id_from=333.999.0.0"> <img src="./imgs/cover/19.png" width="170" /></a> | [19-卷积层] | [讲义1](https://courses.d2l.ai/zh-v2/assets/pdfs/part-1_2.pdf) [2](https://courses.d2l.ai/zh-v2/assets/pdfs/part-1_3.pdf) | | <a href="https://github.com/120L031815"> <img src="./imgs/profile/RuiChen_Yi.png" width="80" /></a> |
| <a href="https://www.bilibili.com/video/BV1L64y1m7Nh?spm_id_from=333.999.0.0"> <img src="./imgs/cover/19.png" width="170" /></a> | [19-卷积层](notes/19-卷积层.md) | [讲义1](https://courses.d2l.ai/zh-v2/assets/pdfs/part-1_2.pdf) [2](https://courses.d2l.ai/zh-v2/assets/pdfs/part-1_3.pdf) | [Jupyter 代码](code/19-卷积层.ipynb) | <a href="https://github.com/120L031815"> <img src="./imgs/profile/RuiChen_Yi.png" width="80" /></a> |
| <a href="https://www.bilibili.com/video/BV1Th411U7UN?spm_id_from=333.999.0.0"> <img src="./imgs/cover/20.png" width="170" /></a> | [20-填充和步幅](notes/20-填充和步幅.md) | [讲义](https://courses.d2l.ai/zh-v2/assets/pdfs/part-1_4.pdf) | [Jupyter 代码](code/20-填充和步幅.ipynb) | <a href="https://github.com/Aleafy"> <img src="./imgs/profile/Ye_Fang.png" width="80" /></a> |
| <a href="https://www.bilibili.com/video/BV1MB4y1F7of?spm_id_from=333.999.0.0"> <img src="./imgs/cover/21.png" width="170" /></a> | [21-多输入输出通道](notes/21-多输入输出通道.md) | [讲义](https://courses.d2l.ai/zh-v2/assets/pdfs/part-1_5.pdf) | [Jupyter 代码](code/21-多输入输出通道.ipynb) | <a href="https://github.com/kinza99"> <img src="./imgs/profile/He_Du.png" width="80" /></a> <a href="https://github.com/chase6666"><img src="./imgs/profile/Kuo_Tian.png" width="80" /></a> |
| <a href="https://www.bilibili.com/video/BV1EV411j7nX?spm_id_from=333.999.0.0"> <img src="./imgs/cover/22.png" width="170" /></a> | [22-池化层](notes/22-池化层.md) | [讲义](https://courses.d2l.ai/zh-v2/assets/pdfs/part-1_6.pdf) | [Jupyter 代码](code/22-池化层.ipynb) | <a href="https://github.com/Atream"> <img src="./imgs/profile/BoXin_Zhang.png" width="80" /></a> <a href="https://github.com/benmagnifico"><img src="./imgs/profile/JingGuang_Li.png" width="80" /></a> |
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233 changes: 233 additions & 0 deletions code/19-卷积层.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 20,
"id": "a673b451",
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"from torch import nn\n",
"\n",
"def corr2d(X,K): #X为输入，K为核矩阵\n",
" h,w=K.shape #h得到K的行数，w得到K的列数\n",
" Y=torch.zeros((X.shape[0]-h+1,X.shape[1]-w+1)) #用0初始化输出矩阵Y\n",
" for i in range(Y.shape[0]): #卷积运算\n",
" for j in range(Y.shape[1]):\n",
" Y[i,j]=(X[i:i+h,j:j+w]*K).sum()\n",
" return Y"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "85e0f332",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([[19., 25.],\n",
" [37., 43.]])"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#样例点测试\n",
"X=torch.tensor([[0,1,2],[3,4,5],[6,7,8]])\n",
"K=torch.tensor([[0,1],[2,3]])\n",
"corr2d(X,K)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "3b9a8de1",
"metadata": {},
"outputs": [],
"source": [
"#实现二维卷积层\n",
"class Conv2d(nn.Module):\n",
" def _init_(self,kernel_size):\n",
" super()._init_()\n",
" self.weight=nn.Parameter(torch.rand(kerner_size))\n",
" self.bias=nn.Parameter(torch.zeros(1))\n",
" def forward(self,x):\n",
" return corr2d(x,self.weight)+self.bias "
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "555f2489",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([[1., 1., 0., 0., 0., 0., 1., 1.],\n",
" [1., 1., 0., 0., 0., 0., 1., 1.],\n",
" [1., 1., 0., 0., 0., 0., 1., 1.],\n",
" [1., 1., 0., 0., 0., 0., 1., 1.],\n",
" [1., 1., 0., 0., 0., 0., 1., 1.],\n",
" [1., 1., 0., 0., 0., 0., 1., 1.]])"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X=torch.ones((6,8))\n",
"X[:,2:6]=0\n",
"X"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "859d2935",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([[ 0., -1., 0., 0., 0., 1., 0.],\n",
" [ 0., -1., 0., 0., 0., 1., 0.],\n",
" [ 0., -1., 0., 0., 0., 1., 0.],\n",
" [ 0., -1., 0., 0., 0., 1., 0.],\n",
" [ 0., -1., 0., 0., 0., 1., 0.],\n",
" [ 0., -1., 0., 0., 0., 1., 0.]])"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"K=torch.tensor([[-1,1]]) #这个K只能检测垂直边缘\n",
"Y=corr2d(X,K)\n",
"Y"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "926cb124",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([[0., 0., 0., 0., 0.],\n",
" [0., 0., 0., 0., 0.],\n",
" [0., 0., 0., 0., 0.],\n",
" [0., 0., 0., 0., 0.],\n",
" [0., 0., 0., 0., 0.],\n",
" [0., 0., 0., 0., 0.],\n",
" [0., 0., 0., 0., 0.],\n",
" [0., 0., 0., 0., 0.]])"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"corr2d(X.t(),K)"
]
},
{
"cell_type": "code",
"execution_count": 40,
"id": "2eb05498",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"batch 2, loss 3.852\n",
"batch 4, loss 1.126\n",
"batch 6, loss 0.386\n",
"batch 8, loss 0.145\n",
"batch 10, loss 0.057\n"
]
}
],
"source": [
"conv2d = nn.Conv2d(1, 1, kernel_size=(1, 2), bias=False)\n",
"\n",
"X = X.reshape((1, 1, 6, 8))\n",
"Y = Y.reshape((1, 1, 6, 7))\n",
"\n",
"for i in range(10):\n",
" Y_hat = conv2d(X)\n",
" l = (Y_hat - Y)**2\n",
" conv2d.zero_grad()\n",
" l.sum().backward()\n",
" conv2d.weight.data[:] -= 3e-2 * conv2d.weight.grad\n",
" if (i + 1) % 2 == 0:\n",
" print(f'batch {i+1}, loss {l.sum():.3f}')"
]
},
{
"cell_type": "code",
"execution_count": 41,
"id": "a626a495",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([[-1.0173, 0.9685]])"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"conv2d.weight.data.reshape((1, 2))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5722b641",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [conda env:py36torch040]",
"language": "python",
"name": "conda-env-py36torch040-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.13"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
137 changes: 137 additions & 0 deletions notes/19-卷积层.md
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## 19-卷积层

#### 本讲文字介绍部分请参考沐神在线书籍~：https://zh-v2.d2l.ai/chapter_convolutional-neural-networks/why-conv.html

#### 代码
```python
import torch
from torch import nn

def corr2d(X,K): #X为输入，K为核矩阵
h,w=K.shape #h得到K的行数，w得到K的列数
Y=torch.zeros((X.shape[0]-h+1,X.shape[1]-w+1)) #用0初始化输出矩阵Y
for i in range(Y.shape[0]): #卷积运算
for j in range(Y.shape[1]):
Y[i,j]=(X[i:i+h,j:j+w]*K).sum()
return Y
```


```python
#样例点测试
X=torch.tensor([[0,1,2],[3,4,5],[6,7,8]])
K=torch.tensor([[0,1],[2,3]])
corr2d(X,K)
```




>>> tensor([[19., 25.],
[37., 43.]])




```python
#实现二维卷积层
class Conv2d(nn.Module):
def _init_(self,kernel_size):
super()._init_()
self.weight=nn.Parameter(torch.rand(kerner_size))
self.bias=nn.Parameter(torch.zeros(1))
def forward(self,x):
return corr2d(x,self.weight)+self.bias
```


```python
X=torch.ones((6,8))
X[:,2:6]=0
X
```




>>> tensor([[1., 1., 0., 0., 0., 0., 1., 1.],
[1., 1., 0., 0., 0., 0., 1., 1.],
[1., 1., 0., 0., 0., 0., 1., 1.],
[1., 1., 0., 0., 0., 0., 1., 1.],
[1., 1., 0., 0., 0., 0., 1., 1.],
[1., 1., 0., 0., 0., 0., 1., 1.]])




```python
K=torch.tensor([[-1,1]]) #这个K只能检测垂直边缘
Y=corr2d(X,K)
Y
```




>>> tensor([[ 0., -1., 0., 0., 0., 1., 0.],
[ 0., -1., 0., 0., 0., 1., 0.],
[ 0., -1., 0., 0., 0., 1., 0.],
[ 0., -1., 0., 0., 0., 1., 0.],
[ 0., -1., 0., 0., 0., 1., 0.],
[ 0., -1., 0., 0., 0., 1., 0.]])




```python
corr2d(X.t(),K)
```




>>> tensor([[0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0.]])




```python
conv2d = nn.Conv2d(1, 1, kernel_size=(1, 2), bias=False)

X = X.reshape((1, 1, 6, 8))
Y = Y.reshape((1, 1, 6, 7))

for i in range(10):
Y_hat = conv2d(X)
l = (Y_hat - Y)**2
conv2d.zero_grad()
l.sum().backward()
conv2d.weight.data[:] -= 3e-2 * conv2d.weight.grad
if (i + 1) % 2 == 0:
print(f'batch {i+1}, loss {l.sum():.3f}')
```

>>> batch 2, loss 3.852
batch 4, loss 1.126
batch 6, loss 0.386
batch 8, loss 0.145
batch 10, loss 0.057



```python
conv2d.weight.data.reshape((1, 2))
```




>>> tensor([[-1.0173, 0.9685]])