Function sample

examples/GAN/InfoGAN-mnist.py:219–258

(model_path)

def sample(model_path):
    pred = OfflinePredictor(PredictConfig(
        session_init=SmartInit(model_path),
        model=Model(),
        input_names=['z_code', 'z_noise'],
        output_names=['gen/viz']))

    # sample all one-hot encodings (10 times)
    z_cat = np.tile(np.eye(10), [10, 1])
    # sample continuos variables from -2 to +2 as mentioned in the paper
    z_uni = np.linspace(-2.0, 2.0, num=100)
    z_uni = z_uni[:, None]

    IMG_SIZE = 400

    while True:
        # only categorical turned on
        z_noise = np.random.uniform(-1, 1, (100, NOISE_DIM))
        zc = np.concatenate((z_cat, z_uni * 0, z_uni * 0), axis=1)
        o = pred(zc, z_noise)[0]
        viz1 = viz.stack_patches(o, nr_row=10, nr_col=10)
        viz1 = cv2.resize(viz1, (IMG_SIZE, IMG_SIZE))

        # show effect of first continous variable with fixed noise
        zc = np.concatenate((z_cat, z_uni, z_uni * 0), axis=1)
        o = pred(zc, z_noise * 0)[0]
        viz2 = viz.stack_patches(o, nr_row=10, nr_col=10)
        viz2 = cv2.resize(viz2, (IMG_SIZE, IMG_SIZE))

        # show effect of second continous variable with fixed noise
        zc = np.concatenate((z_cat, z_uni * 0, z_uni), axis=1)
        o = pred(zc, z_noise * 0)[0]
        viz3 = viz.stack_patches(o, nr_row=10, nr_col=10)
        viz3 = cv2.resize(viz3, (IMG_SIZE, IMG_SIZE))

        canvas = viz.stack_patches(
            [viz1, viz2, viz3],
            nr_row=1, nr_col=3, border=5, bgcolor=(255, 0, 0))

        viz.interactive_imshow(canvas)


if __name__ == '__main__':