nondefaced_detector.training.training.train

train(csv_path, model_save_path, tfrecords_path, volume_shape=(128, 128, 128), image_size=(128, 128), dropout=0.2, batch_size=16, n_classes=2, n_epochs=15, mode='CV')

Train a model.

Parameters

• csv_path (str - Path) – Path to the csv file containing training volume paths, labels (X, Y).

• model_save_path (str - Path) – Path to where the save model and model weights.

• tfrecords_path (str - Path) – Path to preprocessed training tfrecords.

• volume_shape (tuple of size 3, optional, default=(128, 128, 128)) – The shape of the preprocessed volumes.

• image_size (tuple of size 2, optional, default=(128, 128)) – The shape of a 2D slice along each volume axis.

• dropout (float, optional, default=0.4) – Float between 0 and 1. Fraction of the input units to drop.

• batch_size (int, optional, default=16) – No. of training examples utilized in each iteration.

• n_classes (int, optional, default=2) – No. of unique classes to train the model on. Default assumption is a binary classifier.

• n_epochs (int, optional, default=15) – No. of complete passes through the training dataset.

• mode (str, optional, default=15) – One of “CV” or “full”. Indicates the type of training to perform.

Returns

A History object that records several metrics such as training/validation loss/metrics at successive epochs.

Return type

tf.keras.callbacks.History