Here's an example of how shape dimensions change throughout a convolutional neural network.
import torch.nn.functional as F
# Formula to calculate convolutional layer output spatial dimensions
conv_spatial_dimensions = int((in_size - kernel_size + 2*(padding)) / stride + 1
class SimpleCNN(torch.nn.Module):
# Our batch shape for input x is (3, 32, 32)
# 3 comes from the fact the image is RGB, grayscale would be 1
def __init__(self):
super(SimpleCNN, self).__init__()
# Input channels = 3 (since the image has depth=3 because RGB), output channels = 18
self.conv1 = torch.nn.Conv2d(3, 18, kernel_size=3, stride=1, padding=1)
self.pool = torch.nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
#4608 input features, 64 output features (see sizing flow below)
self.fc1 = torch.nn.Linear(18 * 16 * 16, 64)
#64 input features, 10 output features for our 10 defined classes
self.fc2 = torch.nn.Linear(64, 10)
def forward(self, x):
# Computes the activation of the first convolution
# Size changes from (3, 32, 32) to (18, 32, 32)
x = F.relu(self.conv1(x))
# Size changes from (18, 32, 32) to (18, 16, 16)
x = self.pool(x)
# Reshape data to input to the input layer of the neural net
# Size changes from (18, 16, 16) to (1, 4608)
# Recall that the -1 infers this dimension from the other given dimension
x = x.view(-1, 18 * 16 * 16)
# Computes the activation of the first fully connected layer
# Size changes from (1, 4608) to (1, 64)
x = F.relu(self.fc1(x))
# Computes the second fully connected layer (activation applied later)
# Size changes from (1, 64) to (1, 10)
x = self.fc2(x)
return(x)
Typical Kernel Sizes: 2x2 - 7x7 (usually square)
Maxpooling Example: nn.MaxPool2d(kernel_size, stride). If both are 2, then the dimensions of the image would decrease by x2.
