f92dd441311594f80ee5be4a2d2a1d6b21b96c59,examples/visualize_layer.py,,visualize_multiple_same_filter,#,39

Before Change

    // 20 is the imagenet category for "ouzel"
    indices = [20] * num_runs
    images = [visualize_activation(model, layer_idx, filter_indices=idx,
                                   text=utils.get_imagenet_label(idx),
                                   max_iter=500) for idx in indices]

    // Easily stitch images via `utils.stitch_images`
    stitched = utils.stitch_images(images)
    if show:
        plt.axis("off")
        plt.imshow(stitched)

After Change

        num_runs: The number of times the same filter is visualized
    
    // Build the VGG16 network with ImageNet weights
    model = VGG16(weights="imagenet", include_top=True)
    print("Model loaded.")

    // The name of the layer we want to visualize
    // (see model definition in vggnet.py)
    layer_name = "predictions"
    layer_idx = [idx for idx, layer in enumerate(model.layers) if layer.name == layer_name][0]

    // 20 is the imagenet category for "ouzel"
    indices = [20] * num_runs
    images = []
    for idx in indices:
        img = visualize_activation(model, layer_idx, filter_indices=idx, max_iter=500)
        img = utils.draw_text(img, utils.get_imagenet_label(idx))
        images.append(img)

    // Easily stitch images via `utils.stitch_images`
    stitched = utils.stitch_images(images)
    if show:
        plt.axis("off")
        plt.imshow(stitched)

Instances