inst/examples/cnn-example.R

In citoverse/cito: Building and Training Neural Networks

\donttest{
if(torch::torch_is_installed()){
library(cito)

# Example workflow in cito

device <- ifelse(torch::cuda_is_available(), "cuda", "cpu")

## Data
### We generate our own data:
### 320 images (3x50x50) of either rectangles or ellipsoids
shapes <- cito:::simulate_shapes(n=320, size=50, channels=3)
X <- shapes$data
Y <- shapes$labels

## Architecture
### Declare the architecture of the CNN
### Note that the output layer is added automatically by cnn()
architecture <- create_architecture(conv(5), maxPool(), conv(5), maxPool(), linear(10))

## Build and train network
### softmax is used for classification
cnn.fit <- cnn(X, Y, architecture, loss = "softmax", epochs = 50, validation = 0.1, lr = 0.05, device=device)

## The training loss is below the baseline loss but at the end of the
## training the loss was still decreasing, so continue training for another 50
## epochs
cnn.fit <- continue_training(cnn.fit, epochs = 50)

# Structure of Neural Network
print(cnn.fit)

# Plot Neural Network
plot(cnn.fit)

## Convergence can be tested via the analyze_training function
analyze_training(cnn.fit)

## Transfer learning
### With the transfer() function we can use predefined architectures with pretrained weights
transfer_architecture <- create_architecture(transfer("resnet18"))
resnet <- cnn(X, Y, transfer_architecture, loss = "softmax",
              epochs = 10, validation = 0.1, lr = 0.05, device=device)
print(resnet)
plot(resnet)
}
}