visualize.py 文件源码

def plot_clusters():
    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_clusters = model.ndim_y
    num_plots_per_cluster = 11
    image_width = 28
    image_height = 28
    ndim_x = image_width * image_height
    pylab.gray()

    with chainer.no_backprop_mode() and chainer.using_config("train", False):
        # plot cluster head
        head_y = np.identity(model.ndim_y, dtype=np.float32)
        zero_z = np.zeros((model.ndim_y, model.ndim_z), dtype=np.float32)
        head_x = model.decode_yz_x(head_y, zero_z).data
        head_x = (head_x + 1.0) / 2.0
        for n in range(num_clusters):
            pylab.subplot(num_clusters, num_plots_per_cluster + 2, n * (num_plots_per_cluster + 2) + 1)
            pylab.imshow(head_x[n].reshape((image_width, image_height)), interpolation="none")
            pylab.axis("off")

        # plot elements in cluster
        counts = [0 for i in range(num_clusters)]
        indices = np.arange(len(images_test))
        np.random.shuffle(indices)
        batchsize = 500

        i = 0
        x_batch = np.zeros((batchsize, ndim_x), dtype=np.float32)
        for n in range(len(images_test) // batchsize):
            for b in range(batchsize):
                x_batch[b] = images_test[indices[i]]
                i += 1
            y_batch = model.encode_x_yz(x_batch)[0].data
            labels = np.argmax(y_batch, axis=1)
            for m in range(labels.size):
                cluster = int(labels[m])
                counts[cluster] += 1
                if counts[cluster] <= num_plots_per_cluster:
                    x = (x_batch[m] + 1.0) / 2.0
                    pylab.subplot(num_clusters, num_plots_per_cluster + 2, cluster * (num_plots_per_cluster + 2) + 2 + counts[cluster])
                    pylab.imshow(x.reshape((image_width, image_height)), interpolation="none")
                    pylab.axis("off")

        fig = pylab.gcf()
        fig.set_size_inches(num_plots_per_cluster, num_clusters)
        pylab.savefig("clusters.png")