CNN Module

class cnn.CNN(train_data, validation_data, test_data, batch_size)

Bases: object

CNN Trainer class.

This class is responsible for training a CNN model.

Parameters:

• train_data (torchvision.datasets.ImageFolder) – Training data.

• validation_data (torchvision.datasets.ImageFolder) – Validation data.

• test_data (torchvision.datasets.ImageFolder) – Test data.

• batch_size (int) – Batch size.

create_and_train_cnn(model_name, num_epochs, learning_rate, weight_decay, replications)

Create and train a CNN model.

Parameters:

• model_name (str) – Model name to be trained.

• num_epochs (int) – Number of epochs to be trained.

• learning_rate (float) – Learning rate to be used at train.

• weight_decay (float) – Weight decay to be used at train.

• replications (int) – Number of replications used at each trained model.

Returns:

A dict mapping keys to the:

• ’result_name’: (str) Result name.

• ’acc_avg’: (float) Average accuracy.

• ’iter_acc_max’: (int) Iteration of maximum accuracy.

• ’duration’: (float) Duration of training.

Return type:

(dict)

create_criterion()

Create a loss criterion.

Parameters:

None

Returns:

Cross entropy loss object.

Return type:

(object)

create_model(model_name)

Create a function to a CNN model to be trained.

Note

At moment, the models available are: [VGG11, Alexnet, MobilenetV3Large].

Parameters:

model_name (str) – CNN model name.

Returns:

Function to CNN model selected.

Return type:

(function)

create_optimizer(model, learning_rate, weight_decay)

Create an optimizer.

Parameters:

• model (function) – CNN function.

• learning_rate (float) – Learning rate

• weight_decay (float) – Weight decay

Returns:

Optimizer object.

Return type:

(object)

evaluate_model(model, loader)

Evaluate a model.

Parameters:

• model (function) – Model function.

• loader (DataLoader) – Data loader

Returns:

Model (trained) accuracy.

Return type:

(float)

train_epoch(model, trainLoader, optimizer, criterion)

Train an epoch.

Parameters:

• model (function) – Model function.

• trainLoader (DataLoader) – Training data loader.

• optimizer (object) – Optimizer object.

• criterion (object) – CEL object.

Returns:

Mean of losses.

Return type:

(float)

train_model(model, train_loader, optimizer, criterion, model_name, num_epochs, learning_rate, weight_decay, replication)

Train a CNN model.

Train a CNN model and save it (PTH file) at ‘models’ directory.

Parameters:

• model (function) – Model function.

• train_loader (DataLoader) – Training data loader

• optimizer (object) – Optimizer object.

• criterion (object) – CEL object.

• model_name (str) – Model name.

• num_epochs (int) – Number of epochs.

• learning_rate (float) – Learning rate.

• weight_decay (float) – Weight decay.

• replication (int) – Replication.

Returns:

None