Example #1
0
def load_data_val_6(testList, W, index, batch):
    x_train_1 = []
    x_train_2 = []
    x_train_3 = []
    for i in range(0, batch):
        true_index = index + i
        if (true_index >= len(testList)):
            true_index = len(testList) - 1
        items = testList[true_index].split(' ')
        q_words = items[2].split('_')
        a_words = items[3].split('_')
        x_train_1_words = []
        x_train_2_words = []
        x_train_3_words = []
        for i in range(50):
            x_train_1_words.append(
                ModelUtils.build_text_image(W, q_words[i], padding=1))
            x_train_2_words.append(
                ModelUtils.build_text_image(W, a_words[i], padding=1))
            x_train_3_words.append(
                ModelUtils.build_text_image(W, a_words[i], padding=1))

        x_train_1.append(np.array(x_train_1_words).reshape((50, 300)))
        x_train_2.append(np.array(x_train_2_words).reshape((50, 300)))
        x_train_3.append(np.array(x_train_3_words).reshape((50, 300)))
    return np.array(x_train_1), np.array(x_train_2), np.array(x_train_3)
Example #2
0
def load_data_6(W, alist, raw, size):
    x_train_1 = []
    x_train_2 = []
    x_train_3 = []
    for i in range(0, size):
        items = raw[random.randint(0, len(raw) - 1)]
        nega = rand_qa(alist)
        q_words = items[2].split('_')
        a_words = items[3].split('_')
        neg_words = nega.split('_')
        x_train_1_words = []
        x_train_2_words = []
        x_train_3_words = []
        for i in range(50):
            x_train_1_words.append(
                ModelUtils.build_text_image(W, q_words[i], padding=1))
            x_train_2_words.append(
                ModelUtils.build_text_image(W, a_words[i], padding=1))
            x_train_3_words.append(
                ModelUtils.build_text_image(W, neg_words[i], padding=1))

        x_train_1.append(np.array(x_train_1_words).reshape((50, 300)))
        x_train_2.append(np.array(x_train_2_words).reshape((50, 300)))
        x_train_3.append(np.array(x_train_3_words).reshape((50, 300)))
    return np.array(x_train_1), np.array(x_train_2), np.array(x_train_3)