目錄
1.寫在前面
2.用 gpu 訓練 cnn
3.完整**演示
4.轉移至 cpu
在 gpu 訓練可以大幅提公升運算速度. 而且 torch 也有一套很好的 gpu 運算體系. 但是要強調的是:
這份 gpu 的**是依據之前cnn的**修改的. 大概修改的地方包括將資料的形式變成 gpu 能讀的形式, 然後將 cnn 也變成 gpu 能讀的形式. 做法就是在後面加上.cuda(), 很簡單.
...
test_data = torchvision.datasets.mnist(root='./mnist/', train=false)
# !!!!!!!! 修改 test data 形式 !!!!!!!!! #
test_x = torch.unsqueeze(test_data.test_data, dim=1).type(torch.floattensor)[:2000].cuda()/255. # tensor on gpu
test_y = test_data.test_labels[:2000].cuda()
class cnn(nn.module):
...cnn = cnn()
# !!!!!!!! 轉換 cnn 去 cuda !!!!!!!!! #
cnn.cuda() # moves all model parameters and buffers to the gpu.
.cuda()
for epoch ..:
for step, ...:
# !!!!!!!! 這裡有修改 !!!!!!!!! #
b_x = x.cuda() # tensor on gpu
b_y = y.cuda() # tensor on gpu
...if step % 50 == 0:
test_output = cnn(test_x)
# !!!!!!!! 這裡有修改 !!!!!!!!! #
pred_y = torch.max(test_output, 1)[1].cuda().data.squeeze() # 將操作放去 gpu
accuracy = torch.sum(pred_y == test_y) / test_y.size(0)
...test_output = cnn(test_x[:10])
# !!!!!!!! 這裡有修改 !!!!!!!!! #
pred_y = torch.max(test_output, 1)[1].cuda().data.squeeze() # 將操作放去 gpu
...print(test_y[:10], 'real number')
import torch
import torch.nn as nn
import torch.utils.data as data
import torchvision
# torch.manual_seed(1)
epoch = 1
batch_size = 50
lr = 0.001
download_mnist = false
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)
# !!!!!!!! change in here !!!!!!!!! #
test_x = torch.unsqueeze(test_data.test_data, dim=1).type(torch.floattensor)[:2000].cuda()/255. # tensor on gpu
test_y = test_data.test_labels[:2000].cuda()
class cnn(nn.module):
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(nn.conv2d(16, 32, 5, 1, 2), nn.relu(), nn.maxpool2d(2),)
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)
output = self.out(x)
return output
cnn = cnn()
# !!!!!!!! change in here !!!!!!!!! #
cnn.cuda() # moves all model parameters and buffers to the gpu.
optimizer = torch.optim.adam(cnn.parameters(), lr=lr)
loss_func = nn.crossentropyloss()
for epoch in range(epoch):
for step, (x, y) in enumerate(train_loader):
# !!!!!!!! change in here !!!!!!!!! #
b_x = x.cuda() # tensor on gpu
b_y = y.cuda() # tensor on gpu
output = cnn(b_x)
loss = loss_func(output, b_y)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if step % 50 == 0:
test_output = cnn(test_x)
# !!!!!!!! change in here !!!!!!!!! #
pred_y = torch.max(test_output, 1)[1].cuda().data # move the computation in gpu
accuracy = torch.sum(pred_y == test_y).type(torch.floattensor) / test_y.size(0)
print('epoch: ', epoch, '| train loss: %.4f' % loss.data.cpu().numpy(), '| test accuracy: %.2f' % accuracy)
test_output = cnn(test_x[:10])
# !!!!!!!! change in here !!!!!!!!! #
pred_y = torch.max(test_output, 1)[1].cuda().data # move the computation in gpu
print(pred_y, 'prediction number')
print(test_y[:10], 'real number')
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