def load_data():
# Set seed
seed = 123
np.random.seed(seed)
# Loading dataset
print("Loading the whole dataset...")
x_train, y_train = read_hoda_dataset(
dataset_path='./DigitDB/Train 60000.cdb',
images_height=32,
images_width=32,
one_hot=False,
reshape=False)
x_test, y_test = read_hoda_dataset(dataset_path='./DigitDB/Test 20000.cdb',
images_height=32,
images_width=32,
one_hot=False,
reshape=False)
# concat to a whole dataset
x, y = np.concatenate([x_train, x_test]), np.concatenate([y_train, y_test])
# shuffle images
idx = np.arange(x.shape[0])
np.random.shuffle(idx)
# set train and test size
train_size = int(0.8 * x.shape[0])
# selecting indices of train, test set
train_idx = idx[:train_size]
test_idx = idx[train_size:]
x_train = x[train_idx]
y_train = y[train_idx]
x_test = x[test_idx]
y_test = y[test_idx]
x_train_rotated = rotate_transform(x_train, 30, 45)
x_test_rotated = rotate_transform(x_test, 30, 45)
x_train_rotated[x_train_rotated >= 0.5] = 1.
x_train_rotated[x_train_rotated < 0.5] = 0.
x_test_rotated[x_test_rotated >= 0.5] = 1.
x_test_rotated[x_test_rotated < 0.5] = 0.
return x_train, x_train_rotated, y_train, x_test, x_test_rotated, y_test
def load_data():
# Set seed
seed = 123
np.random.seed(seed)
# Loading dataset
print("Loading the whole dataset...")
x_train, y_train = read_hoda_dataset(
dataset_path='./DigitDB/Train 60000.cdb',
images_height=32,
images_width=32,
one_hot=True,
reshape=True)
x_test, y_test = read_hoda_dataset(dataset_path='./DigitDB/Test 20000.cdb',
images_height=32,
images_width=32,
one_hot=True,
reshape=True)
# concat to a whole dataset
x, y = np.concatenate([x_train, x_test]), np.concatenate([y_train, y_test])
# shuffle images
idx = np.arange(x.shape[0])
np.random.shuffle(idx)
# set train and test size
train_size = int(0.7 * x.shape[0])
test_size = int(0.1 * x.shape[0])
# selecting indices of train, val, test set
train_idx = idx[:train_size]
val_idx = idx[train_size:-test_size]
test_idx = idx[-test_size:]
x_train = x[train_idx]
y_train = y[train_idx]
x_val = x[val_idx]
y_val = y[val_idx]
x_test = x[test_idx]
y_test = y[test_idx]
return x_train, y_train, x_val, y_val, x_test, y_test