diff --git a/README.md b/README.md
index 21c8d9a..a4552e4 100644
--- a/README.md
+++ b/README.md
@@ -91,7 +91,7 @@
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| [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) | 
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| [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) | 
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| [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) | | 
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| [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) | | 
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+| | [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) | |
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| [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) | 
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| [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) | 
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| [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) | |
diff --git "a/code/19-\345\215\267\347\247\257\345\261\202.ipynb" "b/code/19-\345\215\267\347\247\257\345\261\202.ipynb"
new file mode 100644
index 0000000..d926e9a
--- /dev/null
+++ "b/code/19-\345\215\267\347\247\257\345\261\202.ipynb"
@@ -0,0 +1,233 @@
+{
+ "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
+}
diff --git "a/notes/19-\345\215\267\347\247\257\345\261\202.md" "b/notes/19-\345\215\267\347\247\257\345\261\202.md"
new file mode 100644
index 0000000..ea22422
--- /dev/null
+++ "b/notes/19-\345\215\267\347\247\257\345\261\202.md"
@@ -0,0 +1,137 @@
+## 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]])
+