参数转化为图片数据

    # Visualize the learned weights for each class.
    # Depending on your choice of learning rate and regularization strength, these may
    # or may not be nice to look at.
    w = best_svm.W[:-1, :]  # strip out the bias
    w = w.reshape(32, 32, 3, 10)
    w_min, w_max = np.min(w), np.max(w)
    classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']
    for i in range(10):
        plt.subplot(2, 5, i + 1)

        # Rescale the weights to be between 0 and 255
        # squeeze将shape为1的维度去掉，比如32*32*1 --》 32*32 
        wimg = 255.0 * (w[:, :, :, i].squeeze() - w_min) / (w_max - w_min)
        plt.imshow(wimg.astype('uint8'))
        plt.axis('off')
        plt.title(classes[i])

    plt.show()