主要用途

当神经网络需要处理这么多输入信息的时候, 也就是卷积神经网络就可以发挥它的优势的时候了。卷积神经网络目前被广泛地用在图片识别上, 已经有层出不穷的应用。

池化

在每一次卷积的时候, 神经层可能会无意地丢失一些信息. 这时, 池化 (pooling) 就可以很好地解决这一问题. 而且池化是一个筛选过滤的过程, 能将 layer 中有用的信息筛选出来, 给下一个层分析

手写数字的CNN例子

数据集(训练和测试)

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import torch
import torch.utils.data as Data
import torchvision # 数据库模块

torch.manual_seed(1) # reproducible

# Hyper Parameters
EPOCH = 1 # 训练整批数据多少次, 为了节约时间, 我们只训练一次
BATCH_SIZE = 50 #批量大小
LR = 0.001 # 学习率
DOWNLOAD_MNIST = True # 如果你已经下载好了mnist数据就写上 False


# Mnist 手写数字 训练集合
train_data = torchvision.datasets.MNIST(
root='./mnist/', # 保存或者提取位置
train=True, # this is training data
transform=torchvision.transforms.ToTensor(), # 转换 PIL.Image or numpy.ndarray
# torch.FloatTensor (C x H x W), 训练的时候 normalize 成 [0.0, 1.0] 区间
download=DOWNLOAD_MNIST, # 没下载就下载, 下载了就不用再下了
)
# Mnist 手写数字 测试集合
test_data = torchvision.datasets.MNIST(
root='./mnist/', # 保存或者提取位置
train=False, # this is training data
transform=torchvision.transforms.ToTensor(), # 转换 PIL.Image or numpy.ndarray
# torch.FloatTensor (C x H x W), 训练的时候 normalize 成 [0.0, 1.0] 区间
download=DOWNLOAD_MNIST, # 没下载就下载, 下载了就不用再下了
)

# 批训练 50samples, 1 channel, 28x28 (50, 1, 28, 28)
train_loader = Data.DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True)

# 为了节约时间, 我们测试时只测试前2000
test_x = torch.unsqueeze(test_data.data, dim=1).type(torch.FloatTensor)[:2000]/255. # shape from (2000, 28, 28) to (2000, 1, 28, 28), value in range(0,1)
test_y = test_data.targets[:2000]

CNN模型

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import torch.nn as nn
class CNN(nn.Module):
def __init__(self):
super(CNN, self).__init__()
self.conv1 = nn.Sequential( # input shape (1, 28, 28)
nn.Conv2d(
in_channels=1, # input height
out_channels=16, # n_filters
kernel_size=5, # filter size
stride=1, # filter movement/step
padding=2, # 如果想要 con2d 出来的图片长宽没有变化, padding=(kernel_size-1)/2 当 stride=1
), # output shape (16, 28, 28)
nn.ReLU(), # activation
nn.MaxPool2d(kernel_size=2), # 在 2x2 空间里向下采样, output shape (16, 14, 14)
)
self.conv2 = nn.Sequential( # input shape (16, 14, 14)
nn.Conv2d(16, 32, 5, 1, 2), # output shape (32, 14, 14)
nn.ReLU(), # activation
nn.MaxPool2d(2), # output shape (32, 7, 7)
)
self.out = nn.Linear(32 * 7 * 7, 10) # fully connected layer, output 10 classes

def forward(self, x):
x = self.conv1(x)
x = self.conv2(x)
x = x.view(x.size(0), -1) # 展平多维的卷积图成 (batch_size, 32 * 7 * 7)
output = self.out(x)
return output

cnn = CNN()
print(cnn) # net architecture
"""
CNN (
(conv1): Sequential (
(0): Conv2d(1, 16, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
(1): ReLU ()
(2): MaxPool2d (size=(2, 2), stride=(2, 2), dilation=(1, 1))
)
(conv2): Sequential (
(0): Conv2d(16, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
(1): ReLU ()
(2): MaxPool2d (size=(2, 2), stride=(2, 2), dilation=(1, 1))
)
(out): Linear (1568 -> 10)
)
"""

训练

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import torch
import torch.nn as nn
from CNN模型 import cnn
from 数据集 import LR,EPOCH,train_loader
optimizer = torch.optim.Adam(cnn.parameters(), lr=LR) # optimize all cnn parameters
loss_func = nn.CrossEntropyLoss() # the target label is not one-hotted

# training and testing
for epoch in range(EPOCH):
for step, (b_x, b_y) in enumerate(train_loader): # 分配 batch data, normalize x when iterate train_loader
output = cnn(b_x) # cnn output
loss = loss_func(output, b_y) # cross entropy loss
optimizer.zero_grad() # clear gradients for this training step
loss.backward() # backpropagation, compute gradients
optimizer.step() # apply gradients

"""
...
Epoch: 0 | train loss: 0.0306 | test accuracy: 0.97
Epoch: 0 | train loss: 0.0147 | test accuracy: 0.98
Epoch: 0 | train loss: 0.0427 | test accuracy: 0.98
Epoch: 0 | train loss: 0.0078 | test accuracy: 0.98
"""

测试

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import torch
from CNN模型 import cnn
from 数据集 import test_x,test_y
test_output = cnn(test_x[:10])
pred_y = torch.max(test_output, 1)[1].data.numpy().squeeze()
print(pred_y, 'prediction number')
print(test_y[:10].numpy(), 'real number')

"""
[7 2 1 0 4 1 4 9 5 9] prediction number
[7 2 1 0 4 1 4 9 5 9] real number
"""

作者声明

1
如有问题,欢迎指正!