utils.py

import numpy as np
import random
import scipy
from tensorflow.examples.tutorials.mnist import input_data






def randomize(x, y):
    """ Randomizes the order of data samples and their corresponding labels"""
    permutation = np.random.permutation(y.shape[0])
    shuffled_x = x[permutation, :, :, :]
    shuffled_y = y[permutation]
    return shuffled_x, shuffled_y


def reformat(x, y):
    """
    Reformats the data to the format acceptable for convolutional layers
    :param x: input array
    :param y: corresponding labels
    :return: reshaped input and labels
    """
    img_size, num_ch, num_class = int(np.sqrt(x.shape[-1])), 1, len(np.unique(np.argmax(y, 1)))
    dataset = x.reshape((-1, img_size, img_size, num_ch)).astype(np.float32)
    labels = (np.arange(num_class) == y[:, None]).astype(np.float32)
    return dataset, labels


def get_next_batch(x, y, start, end):
    x_batch = x[start:end]
    y_batch = y[start:end]
    return x_batch, y_batch