e90157e7f01c85e9ef22f9679f1a40bda0efb51f,src/utils/utils.py,,split_data,#,26

Before Change

        return imresize(image_array, size)

def split_data(ground_truth_data, training_ratio=.8, do_shuffle=False):
    ground_truth_keys = sorted(ground_truth_data.keys())
    if do_shuffle == True:
        shuffle(ground_truth_keys)
    num_train = int(round(training_ratio * len(ground_truth_keys)))
    train_keys = ground_truth_keys[:num_train]
    validation_keys = ground_truth_keys[num_train:]
    return train_keys, validation_keys

def display_image(image_array):
    image_array =  np.squeeze(image_array).astype("uint8")
    plt.imshow(image_array)

After Change

def split_data(x, y, validation_split=.2):
    num_samples = len(x)
    num_train_samples = int((1 - validation_split)*num_samples)
    train_x = x[:num_train_samples]
    train_y = y[:num_train_samples]
    val_x = x[num_train_samples:]
    val_y = y[num_train_samples:]
    train_data = (train_x, train_y)
    val_data = (val_x, val_y)
    return train_data, val_data



def display_image(image_array):

Instances