45714e343f36292cec938dc01ebb8a2dec10cea4,keras/preprocessing/image.py,ImageDataGenerator,flow,#ImageDataGenerator#Any#Any#Any#Any#Any#Any#Any#Any#,142

Before Change

            np.random.seed(seed)
            np.random.shuffle(y)

        nb_batch = int(math.ceil(float(X.shape[0])/batch_size))
        for b in range(nb_batch):
            batch_end = (b+1)*batch_size
            if batch_end > X.shape[0]:
                nb_samples = X.shape[0] - b*batch_size
            else:
                nb_samples = batch_size

            bX = np.zeros(tuple([nb_samples]+list(X.shape)[1:]))
            for i in range(nb_samples):
                x = X[b*batch_size+i]
                x = self.random_transform(x.astype("float32"))
                x = self.standardize(x)
                bX[i] = x

            if save_to_dir:
                for i in range(nb_samples):
                    img = array_to_img(bX[i], scale=True)
                    img.save(save_to_dir + "/" + save_prefix + "_" + str(i) + "." + save_format)

            yield bX, y[b*batch_size:b*batch_size+nb_samples]

    def standardize(self, x):
        if self.featurewise_center:
            x -= self.mean
        if self.featurewise_std_normalization:

After Change

                for i in range(current_batch_size):
                    img = array_to_img(bX[i], scale=True)
                    img.save(save_to_dir + "/" + save_prefix + "_" + str(i) + "." + save_format)
            if current_batch_size == batch_size:
                b += 1
            else:
                b = 0
            yield bX, y[current_index: current_index + batch_size]

    def standardize(self, x):
        if self.featurewise_center:

Instances