template.runner.bidimensional package

Submodules

template.runner.bidimensional.bidimensional module

This file is the template for the boilerplate of train/test of a DNN on a bidimensional dataset In particular, it is designed to work with clouds of bi-dimensional points.

class template.runner.bidimensional.bidimensional.Bidimensional[source]

Bases: template.runner.image_classification.image_classification.ImageClassification

static single_run(writer, current_log_folder, model_name, epochs, lr, decay_lr, validation_interval, checkpoint_all_epochs, **kwargs)[source]

This is the main routine where train(), validate() and test() are called.

Parameters

  • writer (Tensorboard.SummaryWriter) – Responsible for writing logs in Tensorboard compatible format.

  • current_log_folder (string) – Path to where logs/checkpoints are saved

  • model_name (string) – Name of the model

  • epochs (int) – Number of epochs to train

  • lr (float) – Value for learning rate

  • kwargs (dict) – Any additional arguments.

  • decay_lr (boolean) – Decay the lr flag

  • validation_interval (int) – Run evaluation on validation set every N epochs

  • checkpoint_all_epochs (bool) – If enabled, save checkpoint after every epoch.

Returns

  • train_value (ndarray[floats] of size (1, epochs)) – Accuracy values for train split

  • val_value (ndarray[floats] of size (1, `epochs`+1)) – Accuracy values for validation split

  • test_value (float) – Accuracy value for test split

Module contents

class template.runner.bidimensional.Bidimensional[source]

Bases: template.runner.image_classification.image_classification.ImageClassification

static single_run(writer, current_log_folder, model_name, epochs, lr, decay_lr, validation_interval, checkpoint_all_epochs, **kwargs)[source]

This is the main routine where train(), validate() and test() are called.

Parameters

  • writer (Tensorboard.SummaryWriter) – Responsible for writing logs in Tensorboard compatible format.

  • current_log_folder (string) – Path to where logs/checkpoints are saved

  • model_name (string) – Name of the model

  • epochs (int) – Number of epochs to train

  • lr (float) – Value for learning rate

  • kwargs (dict) – Any additional arguments.

  • decay_lr (boolean) – Decay the lr flag

  • validation_interval (int) – Run evaluation on validation set every N epochs

  • checkpoint_all_epochs (bool) – If enabled, save checkpoint after every epoch.

Returns

  • train_value (ndarray[floats] of size (1, epochs)) – Accuracy values for train split

  • val_value (ndarray[floats] of size (1, `epochs`+1)) – Accuracy values for validation split

  • test_value (float) – Accuracy value for test split