gestureCNN.py 文件源码

def visualizeLayer(model, img, input_image, layerIndex):

    layer = model.layers[layerIndex]

    get_activations = K.function([model.layers[0].input, K.learning_phase()], [layer.output,])
    activations = get_activations([input_image, 0])[0]
    output_image = activations


    ## If 4 dimensional then take the last dimension value as it would be no of filters
    if output_image.ndim == 4:
        # Rearrange dimension so we can plot the result
        o1 = np.rollaxis(output_image, 3, 1)
        output_image = np.rollaxis(o1, 3, 1)

        print "Dumping filter data of layer{} - {}".format(layerIndex,layer.__class__.__name__)
        filters = len(output_image[0,0,0,:])

        fig=plt.figure(figsize=(8,8))
        # This loop will plot the 32 filter data for the input image
        for i in range(filters):
            ax = fig.add_subplot(6, 6, i+1)
            #ax.imshow(output_image[img,:,:,i],interpolation='none' ) #to see the first filter
            ax.imshow(output_image[0,:,:,i],'gray')
            #ax.set_title("Feature map of layer#{} \ncalled '{}' \nof type {} ".format(layerIndex,
            #                layer.name,layer.__class__.__name__))
            plt.xticks(np.array([]))
            plt.yticks(np.array([]))
        plt.tight_layout()
        #plt.show()
        fig.savefig("img_" + str(img) + "_layer" + str(layerIndex)+"_"+layer.__class__.__name__+".png")
        #plt.close(fig)
    else:
        print "Can't dump data of this layer{}- {}".format(layerIndex, layer.__class__.__name__)