CBOW(Continuous Bag-of-Words) model implementation with tensorflow ,just need to change the generate_batch() function.

tf_CBOW.py

def generate_batch(data, batch_size, skip_window):
    """
    Generates a mini-batch of training data for the training CBOW
    embedding model.
    :param data (numpy.ndarray(dtype=int, shape=(corpus_size,)): holds the
        training corpus, with words encoded as an integer
    :param batch_size (int): size of the batch to generate
    :param skip_window (int): number of words to both left and right that form
        the context window for the target word.
    Batch is a vector of shape (batch_size, 2*skip_window), with each entry for the batch containing all the context words, with the corresponding label being the word in the middle of the context
    """
    global data_index
    span = 2 * skip_window + 1  # [ skip_window target skip_window ]
    batch = np.ndarray(shape=(batch_size, span - 1), dtype=np.int32)
    labels = np.ndarray(shape=(batch_size, 1), dtype=np.int32)
    buffer = collections.deque(maxlen=span)
    if data_index + span > len(data):
        data_index = 0

    for _ in range(span):
        buffer.append(data[data_index])
        data_index = (data_index + 1) % len(data)

    for i in range(batch_size):
        target = skip_window
        target_to_avoid = [skip_window]
        col_idx = 0
        for j in range(span):
            if j == span // 2:  ##skip the middel word
                continue
            batch[i, col_idx] = buffer[j]
            col_idx += 1
        labels[i, 0] = buffer[target]
        buffer.append(data[data_index])
        data_index = (data_index + 1) % len(data)

    assert batch_size[0] == batch_size
    assert batch_size[1] == span - 1

    return batch, labels