deepaugment/build_features.py
def 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
        """
        if hasattr(keras.datasets, dataset_name):
            (x_train, y_train), (x_val, y_val) = getattr(
                keras.datasets, dataset_name
            ).load_data()
        else:
            sys.exit(f"Unknown dataset {dataset_name}")

        X = np.concatenate([x_train, x_val])
        y = np.concatenate([y_train, y_val])
        input_shape = x_train.shape[1:]

        return X, y, input_shape
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
        
Loads dataset from keras and returns a sample out of it
Loads dataset from keras and returns a sample out of it
def load(dataset_name):
        if hasattr(keras.datasets, dataset_name):
            (x_train, y_train), (x_val, y_val) = getattr(
                keras.datasets, dataset_name
            ).load_data()
        else:
            sys.exit(f"Unknown dataset {dataset_name}")
        X = np.concatenate([x_train, x_val])
        y = np.concatenate([y_train, y_val])
        input_shape = x_train.shape[1:]
        return X, y, input_shape
def load ( dataset_name ) : if hasattr ( keras . datasets , dataset_name ) : ( x_train , y_train ) , ( x_val , y_val ) = getattr ( keras . datasets , dataset_name ) . load_data ( ) else : sys . exit ( f"Unknown dataset {dataset_name}" ) X = np . concatenate ( [ x_train , x_val ] ) y = np . concatenate ( [ y_train , y_val ] ) input_shape = x_train . shape [ 1 : ] return X , y , input_shape
Loads dataset from keras and returns a sample out of it
Loads dataset from keras and returns a sample out of it
"""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
        """
data, with keys X_train, Y_train, X_val, Y_val
list: input shape
data with keys X_train Y_train X_val Y_val list : input shape
dict
dataset_name
None
training_set_size
None
validation_set_size
None