LeNet-5 Tutorial

Introduction

Overview of LeNet: click here

• Activation Function: TanH

• Pooling: Avg. pooling

• No Padding

• F.C: softmax

  • Originally used RBF(Radial Basis Function)

• Loss Function: MSE

• Input: 32x32x1

  • MNIST image is 28x28. MNIST is padded to 32

LeNet-5 layers:

1. Convolution #1. Input = 32x32x1. Output = 28x28x6 conv2d

2. SubSampling #1. Input = 28x28x6. Output = 14x14x6. SubSampling is simply Average Pooling so we use avg_pool

3. Convolution #2. Input = 14x14x6. Output = 10x10x16 conv2d

4. SubSampling #2. Input = 10x10x16. Output = 5x5x16 avg_pool

5. Fully Connected #1. Input = 5x5x16. Output = 120

6. Fully Connected #2. Input = 120. Output = 84

7. Output 10

Keras

Another code example: click here

Full code: click here

model = keras.Sequential()

model.add(layers.Conv2D(filters=6, kernel_size=(5, 5), activation='relu', input_shape=(32,32,1)))
model.add(layers.AveragePooling2D())

model.add(layers.Conv2D(filters=16, kernel_size=(5, 5), activation='relu'))
model.add(layers.AveragePooling2D())

model.add(layers.Flatten())

model.add(layers.Dense(units=120, activation='relu'))

model.add(layers.Dense(units=84, activation='relu'))

model.add(layers.Dense(units=10, activation = 'softmax'))

PyTorch

Sample code: click here

Originally CONV 5x5. Some code use CONV 3x3

class Net(nn.Module):

    def __init__(self):
        super(Net, self).__init__()
        # 1 input image channel, 6 output channels, 3x3 square convolution
        # kernel
        self.conv1 = nn.Conv2d(1, 6, 3)
        self.conv2 = nn.Conv2d(6, 16, 3)
        # an affine operation: y = Wx + b
        self.fc1 = nn.Linear(16 * 6 * 6, 120)  # 6*6 from image dimension
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

    def forward(self, x):
        # Max pooling over a (2, 2) window
        x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))
        # If the size is a square you can only specify a single number
        x = F.max_pool2d(F.relu(self.conv2(x)), 2)
        x = x.view(-1, self.num_flat_features(x))
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

    def num_flat_features(self, x):
        size = x.size()[1:]  # all dimensions except the batch dimension
        num_features = 1
        for s in size:
            num_features *= s
        return num_features


net = Net()
print(net)

output = net(input)
target = torch.randn(10)  # a dummy target, for example
target = target.view(1, -1)  # make it the same shape as output
criterion = nn.MSELoss()

loss = criterion(output, target)
print(loss)