visualize.py 文件源码

def plot_analogy():
    dataset_train, dataset_test = chainer.datasets.get_mnist()
    images_train, labels_train = dataset_train._datasets
    images_test, labels_test = dataset_test._datasets
    dataset_indices = np.arange(0, len(images_test))
    np.random.shuffle(dataset_indices)

    model = Model()
    assert model.load("model.hdf5")

    # normalize
    images_train = (images_train - 0.5) * 2
    images_test = (images_test - 0.5) * 2

    num_analogies = 10
    pylab.gray()

    batch_indices = dataset_indices[:num_analogies]
    x_batch = images_test[batch_indices]
    y_batch = labels_test[batch_indices]
    y_onehot_batch = onehot(y_batch)

    with chainer.no_backprop_mode() and chainer.using_config("train", False):
        z_batch = model.encode_x_yz(x_batch)[1].data

        # plot original image on the left
        x_batch = (x_batch + 1.0) / 2.0
        for m in range(num_analogies):
            pylab.subplot(num_analogies, 10 + 2, m * 12 + 1)
            pylab.imshow(x_batch[m].reshape((28, 28)), interpolation="none")
            pylab.axis("off")

        all_y = np.identity(10, dtype=np.float32)
        for m in range(num_analogies):
            # copy z_batch as many as the number of classes
            fixed_z = np.repeat(z_batch[m].reshape(1, -1), 10, axis=0)
            representation = model.encode_yz_representation(all_y, fixed_z)
            gen_x = model.decode_representation_x(representation).data
            gen_x = (gen_x + 1.0) / 2.0
            # plot images generated from each label
            for n in range(10):
                pylab.subplot(num_analogies, 10 + 2, m * 12 + 3 + n)
                pylab.imshow(gen_x[n].reshape((28, 28)), interpolation="none")
                pylab.axis("off")

    fig = pylab.gcf()
    fig.set_size_inches(num_analogies, 10)
    pylab.savefig("analogy.png")