PyTorch Implementation of Convolutional Neural Network
This passage is to solve MNIST problem using CNN, Residual Net and Inception Module.
1. Convolutional Layer
import torch
in_channels, out_channels = 5, 10
width, height = 100, 100 # imgae size
kernel_size = 3
batch_size = 1
input = torch.randn(batch_size, in_channels, width, height)
conv_layer = torch.nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size)
output = conv_layer(input)
print(input.shape)
print(output.shape)
print(conv_layer.weight.shape)
2. Convolutional Layer – padding = 1
import torch
input = [3, 4, 5, 6, 7,
2, 4, 6, 8, 2,
1, 6, 7, 8, 4,
9, 7, 4, 6, 2,
3, 7, 5, 4, 1]
input = torch.Tensor(input).view(1, 1, 5, 5)
# view(Batch_number, input_Channel, width, height)
conv_layer = torch.nn.Conv2d(1, 1, kernel_size=3, padding=1, bias=False)
# torch.nn.Conv2d(input_channel, output_channel, kernel_size(3x3), padding, bias)
kernel = torch.Tensor([1, 2, 3, 4, 5, 6, 7, 8, 9]).view(1, 1, 3, 3)
# view is like reshape
conv_layer.weight.data = kernel.data
# kernel is a tensor. use kernel.data
# edit conv_layer.weight to change the kernel
output = conv_layer(input)
print(output)
3. Convolutional Layer – stride = 2
import torch
input = [3, 4, 5, 6, 7,
2, 4, 6, 8, 2,
1, 6, 7, 8, 4,
9, 7, 4, 6, 2,
3, 7, 5, 4, 1]
input = torch.Tensor(input).view(1, 1, 5, 5)
conv_layer = torch.nn.Conv2d(1, 1, kernel_size=3, stride=2, bias=False)
output = conv_layer(input)
print(output)
4. Max pooling layer
import torch
input = [3, 4, 6, 5,
2, 4, 6, 8,
1, 6, 7, 8,
9, 7, 4, 6,
]
input = torch.Tensor(input).view(1, 1, 4, 4)
maxpooling_layer = torch.nn.MaxPool2d(kernel_size=2)
output = maxpooling_layer(input)
print(output)
class Net(torch.nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = torch.nn.Conv2d(1, 10, kernel_size = 5)
self.conv2 = torch.nn.Conv2d(10 ,20, kernel_size = 5)
self.pooling = torch.nn.MaxPool2d(2)
self.fc = torch.nn.Linear(320, 10)
def forward(self, x):
# Flatten data from (n, 1, 28, 28) to (n, 784)
batch_size = x.size(0)
# x.size == (n, 1, 28, 28) ==> x.size(0) == n
x = F.relu(self.pooling(self.conv1(x)))
x = F.relu(self.pooling(self.conv2(x)))
x = x.view(batch_size, -1)# flatten
x = self.fc(x)
return x
model = Net()
#if wanna use gpu-----------------------
device = torch.device("cuda:0"if torch.cuda.is_available() else "cpu")
model.to(device)
#---------------------------------------
def train(epoch):
running_loss = 0.0
for batch_idx, data in enumerate(train_loader, 0):
inputs, target = data
inputs, target = inputs.to(device), target.to(device)
# send the inputs and targets at every step to the GPU
optimizer.zero_grad()
# forward + backward + update
outputs = model(inputs)
loss = criterion(outputs, target)
loss.backward()
optimizer.step()
running_loss +=loss.item()
if batch_idx % 300 == 299:
print('[%d %5d] loss:%.3f' % (epoch+1, batch_idx +1, running_loss / 2000))
running_loss = 0.0
def test():
correct = 0
total = 0
with torch.no_grad():
for data in test_loader:
inputs, target = data
inputs, target = inputs.to(device), target.to(device)
outputs = model(inputs)
_, predicted = torch.max(outputs.data, dim = 1)
total += target.size(0)
correct += (predicted == target).sum().item()
print('Accuracy on test set: %d %% [%d/%d]' % (100 * correct / total, correct, total))
Inception Module:
we can use class to decrease programming tasks.
Inception:
class InceptionA(torch.nn.Module):
def __init__(self, in_channels):
super(InceptionA, self).__init__()
self.branch1x1 = torch.nn.Conv2d(in_channels, 16, kernel_size=1)
self.branch5x5_1 = torch.nn.Conv2d(in_channels, 16, kernel_size=1)
self.branch5x5_2 = torch.nn.Conv2d(16, 24, kernel_size=5, padding=2)
self.branch3x3_1 = torch.nn.Conv2d(in_channels, 16, kernel_size=1)
self.branch3x3_2 = torch.nn.Conv2d(16, 24, kernel_size=3, padding=1)
self.branch3x3_3 = torch.nn.Conv2d(24, 24, kernel_size=3, padding=1)
self.branch_pool = torch.nn.Conv2d(in_channels, 24, kernel_size=1)
def forward(self, x):
branch1x1 = self.branch1x1(x)
branch5x5 = self.branch5x5_1(x)
branch5x5 = self.branch5x5_2(branch5x5)
branch3x3 = self.branch3x3_1(x)
branch3x3 = self.branch3x3_2(branch3x3)
branch3x3 = self.branch3x3_3(branch3x3)
branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1)
branch_pool = self.branch_pool(branch_pool)
outputs = [branch1x1, branch5x5, branch3x3, branch_pool]
return torch.cat(outputs, dim=1)
class Net(torch.nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = torch.nn.Conv2d(1, 10, kernel_size=5)
self.conv2 = torch.nn.Conv2d(88, 20, kernel_size=5)
# 88 = 24 + 24 + 24 + 16
self.incep1 = InceptionA(in_channels=10)
self.incep2 = InceptionA(in_channels=20)
self.mp = torch.nn.MaxPool2d(2)
self.fc = torch.nn.Linear(1408, 10)
def forward(self, x):
in_size = x.size(0)
x = F.relu(self.mp(self.conv1(x)))
x = self.incep1(x)
x = F.relu(self.mp(self.conv2(x)))
x = self.incep2(x)
x = x.view(in_size, -1)
x = self.fc(x)
return x
Residual net:
class ResidualBlock(torch.nn.Module):
def __init__(self, channels):
super(ResidualBlock, self).__init__()
self.channels = channels
self.conv1 = torch.nn.Conv2d(channels, channels,
kernel_size=3, padding=1)
self.conv2 = torch.nn.Conv2d(channels, channels,
kernel_size=3, padding=1)
def forward(self, x):
y = F.relu(self.conv1(x))
y = self.conv2(y)
return F.relu(x + y)
class Net(torch.nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = torch.nn.Conv2d(1, 16, kernel_size=5)
self.conv2 = torch.nn.Conv2d(16, 32, kernel_size=5)
self.mp = torch.nn.MaxPool2d(2)
self.rblock1 = ResidualBlock(16)
self.rblock2 = ResidualBlock(32)
self.fc = torch.nn.Linear(512, 10)
def forward(self, x):
in_size = x.size(0)
x = self.mp(F.relu(self.conv1(x)))
x = self.rblock1(x)
x = self.mp(F.relu(self.conv2(x)))
x = self.rblock2(x)
x = x.view(in_size, -1)
x = self.fc(x)
return x