def pool3d(x, pool_size, strides=(1, 1, 1), border_mode='valid',
           dim_ordering='th', pool_mode='max'):
    if border_mode == 'same':
        # TODO: add implementation for border_mode="same"
        raise Exception('border_mode="same" not supported with Theano.')
    elif border_mode == 'valid':
        ignore_border = True
        padding = (0, 0)
    else:
        raise Exception('Invalid border mode: ' + str(border_mode))

    if dim_ordering not in {'th', 'tf'}:
        raise Exception('Unknown dim_ordering ' + str(dim_ordering))

    if dim_ordering == 'tf':
        x = x.dimshuffle((0, 4, 1, 2, 3))

    if pool_mode == 'max':
        # pooling over conv_dim2, conv_dim1 (last two channels)
        output = pool.pool_2d(input=x.dimshuffle(0, 1, 4, 3, 2),
                              ds=(pool_size[1], pool_size[0]),
                              st=(strides[1], strides[0]),
                              ignore_border=ignore_border,
                              padding=padding,
                              mode='max')

        # pooling over conv_dim3
        pool_out = pool.pool_2d(input=output.dimshuffle(0, 1, 4, 3, 2),
                                ds=(1, pool_size[2]),
                                st=(1, strides[2]),
                                ignore_border=ignore_border,
                                padding=padding,
                                mode='max')

    elif pool_mode == 'avg':
        # pooling over conv_dim2, conv_dim1 (last two channels)
        output = pool.pool_2d(input=x.dimshuffle(0, 1, 4, 3, 2),
                              ds=(pool_size[1], pool_size[0]),
                              st=(strides[1], strides[0]),
                              ignore_border=ignore_border,
                              padding=padding,
                              mode='average_exc_pad')

        # pooling over conv_dim3
        pool_out = pool.pool_2d(input=output.dimshuffle(0, 1, 4, 3, 2),
                                ds=(1, pool_size[2]),
                                st=(1, strides[2]),
                                ignore_border=ignore_border,
                                padding=padding,
                                mode='average_exc_pad')
    else:
        raise Exception('Invalid pooling mode: ' + str(pool_mode))

    if dim_ordering == 'tf':
        pool_out = pool_out.dimshuffle((0, 2, 3, 4, 1))
    return pool_out


# RANDOMNESS
dataset/ETHPy150Open fchollet/keras/keras/backend/theano_backend.py/pool3d/original