visualizations.py 文件源码

def plot_network(image, model, label=None):
    layer_names = [l.name for l in model.layers if isinstance(l,Conv2D)]
    n_conv = len(layer_names)
    n_axes = n_conv 
    prediction = model.predict(np.expand_dims(image,0))
    mng = plt.get_current_fig_manager()
    mng.full_screen_toggle()
    fig, [axlist1, axlist2] = plt.subplots(2,n_conv)
    diagnosis = ["negative", "positive"]
    for j in range(n_conv):
        plot_heatmap(image, model, layer_names[j],"abnormal",axlist1[j])
#        axlist1[j].set_xlabel(layer_names[j] + "ab")
    for j in range(n_conv):
        plot_heatmap(image, model, layer_names[j],"normal",axlist2[j],cmap=plt.cm.inferno)
    fig.suptitle("Prediction: {},  {}".format(prediction,label))
    fig.show()