deepaugment.build_features module

class deepaugment.build_features.DataOp

Bases: object

static find_num_classes(data)

static load(dataset_name)

Loads dataset from keras and returns a sample out of it

Args:

dataset_name (str): training_set_size (int): validation_set_size (int):

Returns:

dict: data, with keys X_train, Y_train, X_val, Y_val list: input shape

static preprocess(X, y, train_set_size, val_set_size=1000)

Preprocess images by:

1. normalize to 0-1 range (divide by 255)
2. convert labels to categorical)

Args:

X (numpy.array): y (numpy.array): train_set_size (int): val_set_size (int):

Returns:

dict: preprocessed data

static preprocess_normal(data)

static sample_validation_set(data)

static split_train_val_sets(X, y, train_set_size, val_set_size)

Splits given images randomly into train and val_seed groups

val_seed -> is validation seed dataset, from where validation sets are sampled

Args:

X (numpy.array): y (numpy.array): train_set_size (int): val_set_size (int):

return:

dict: dict with keys X_train, y_train, X_val_seed, y_val_seed