撒花！神经网络与深度学习》中文教程正式开源！全书 pdf、ppt 和代码一并发布！
最编程
2024-04-06 07:21:57
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import os
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.utils.data as Data
import torchvision
import torch.nn.functional as F
import numpy as np
learning_rate = 1e-4
keep_prob_rate = 0.7 #
max_epoch = 3
BATCH_SIZE = 50
DOWNLOAD_MNIST = False
if not(os.path.exists('./mnist/')) or not os.listdir('./mnist/'):
# not mnist dir or mnist is empyt dir
DOWNLOAD_MNIST = True
train_data = torchvision.datasets.MNIST(root='./mnist/',train=True, transform=torchvision.transforms.ToTensor(), download=DOWNLOAD_MNIST,)
train_loader = Data.DataLoader(dataset = train_data ,batch_size= BATCH_SIZE ,shuffle= True)
test_data = torchvision.datasets.MNIST(root = './mnist/',train = False)
test_x = Variable(torch.unsqueeze(test_data.test_data,dim = 1),volatile = True).type(torch.FloatTensor)[:500]/255.
test_y = test_data.test_labels[:500].numpy()
class CNN(nn.Module):
def __init__(self):
super(CNN, self).__init__()
self.conv1 = nn.Sequential(
nn.Conv2d( # ???
# patch 7 * 7 ; 1 in channels ; 32 out channels ; ; stride is 1
# padding style is same(that means the convolution opration's input and output have the same size)
in_channels= ,
out_channels= ,
kernel_size= ,
stride= ,
padding= ,
),
nn.ReLU(), # activation function
nn.MaxPool2d(2), # pooling operation
)
self.conv2 = nn.Sequential( # ???
# line 1 : convolution function, patch 5*5 , 32 in channels ;64 out channels; padding style is same; stride is 1
# line 2 : choosing your activation funciont
# line 3 : pooling operation function.
)
self.out1 = nn.Linear( 7*7*64 , 1024 , bias= True) # full connection layer one
self.dropout = nn.Dropout(keep_prob_rate)
self.out2 = nn.Linear(1024,10,bias=True)
def forward(self, x):
x = self.conv1(x)
x = self.conv2(x)
x = x.view( ) # flatten the output of coonv2 to (batch_size ,32 * 7 * 7) # ???
out1 = self.out1(x)
out1 = F.relu(out1)
out1 = self.dropout(out1)
out2 = self.out2(out1)
output = F.softmax(out2)
return output
def test(cnn):
global prediction
y_pre = cnn(test_x)
_,pre_index= torch.max(y_pre,1)
pre_index= pre_index.view(-1)
prediction = pre_index.data.numpy()
correct = np.sum(prediction == test_y)
return correct / 500.0
def train(cnn):
optimizer = torch.optim.Adam(cnn.parameters(), lr=learning_rate )
loss_func = nn.CrossEntropyLoss()
for epoch in range(max_epoch):
for step, (x_, y_) in enumerate(train_loader):
x ,y= Variable(x_),Variable(y_)
output = cnn(x)
loss = loss_func(output,y)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if step != 0 and step % 20 ==0:
print("=" * 10,step,"="*5,"="*5, "test accuracy is ",test(cnn) ,"=" * 10 )
if __name__ == '__main__':
cnn = CNN()
train(cnn)