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torch_cnn.py
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torch_cnn.py
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import torch
import torchvision
import torch.nn as nn
import matplotlib.pyplot as plt
import torch.utils.data as Data
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 = 100
LR = 0.005 # 学习率
DOWNLOAD_MNIST = False
train_data = torchvision.datasets.MNIST(
'mnist', train=True, download=DOWNLOAD_MNIST, transform=torchvision.transforms.ToTensor())
test_data = torchvision.datasets.MNIST(
'mnist', train=False, download=DOWNLOAD_MNIST)
train_loader = Data.DataLoader(
dataset=train_data, batch_size=BATCH_SIZE, shuffle=True)
test_x = torch.unsqueeze(test_data.test_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.test_labels[:2000]
class CNN(nn.Module):
"""docstring for CNN"""
def __init__(self):
super(CNN, self).__init__()
self.conv1 = nn.Sequential(
nn.Conv2d(in_channels=1, out_channels=16,
kernel_size=5, stride=1, padding=2),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2))
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)
)
# fully connected layer, output 10 classes
self.out = nn.Linear(32 * 7 * 7, 10)
def forward(self, x):
x = self.conv1(x)
x = self.conv2(x)
x = x.view(x.size(0), -1)
return self.out(x)
cnn = None
try:
cnn = torch.load('torch_cnn.pkl')
except FileNotFoundError as e:
print('we need to train it')
cnn = CNN()
optimizer = torch.optim.Adam(cnn.parameters(), lr=LR)
loss_func = nn.CrossEntropyLoss()
for epoch in range(EPOCH):
for step, (batch_x,batch_y) in enumerate(train_loader):
print('Epoch: ', epoch, '| Step: ', step)
y_pred = cnn(batch_x)
optimizer.zero_grad()
loss = loss_func(y_pred, batch_y)
loss.backward()
optimizer.step()
torch.save(cnn,'torch_cnn.pkl')
def print_accuracy_score(y_pred, y):
prob, index = torch.max(y_pred,dim=1)
sum = torch.sum(index == y).type(torch.float32)
accuracy = torch.div(sum,y.size()[0]).data.numpy()
print('score is ', accuracy)
return accuracy
print(cnn)
# print('test shape', test_y.size())
# test_output = cnn(test_x[:10])
# prob, index = torch.max(test_output,dim=1)
# print('pred number',index.numpy())
# print('real number',test_y[:10].data.numpy())
test_out = cnn(test_x)
print_accuracy_score(test_out, test_y)