keras CNN卷积核可视化,热度图

卷积核可视化

import matplotlib.pyplot as plt
import numpy as np
from keras import backend as K
from keras.models import load_model

# 将浮点图像转换成有效图像
def deprocess_image(x):
    # 对张量进行规范化
    x -= x.mean()
    x /= (x.std() + 1e-5)
    x *= 0.1
    x += 0.5
    x = np.clip(x, 0, 1)
    # 转化到RGB数组
    x *= 255
    x = np.clip(x, 0, 255).astype(uint8)
    return x


# 可视化滤波器
def kernelvisual(model, layer_target=1, num_iterate=100):
    # 图像尺寸和通道
    img_height, img_width, num_channels = K.int_shape(model.input)[1:4]
    num_out = K.int_shape(model.layers[layer_target].output)[-1]

    plt.suptitle([%s] convnet filters visualizing % model.layers[layer_target].name)

    print(第%d层有%d个通道 % (layer_target, num_out))
    for i_kernal in range(num_out):
        input_img = model.input
        # 构建一个损耗函数，使所考虑的层的第n个滤波器的激活最大化，-1层softmax层
        if layer_target == -1:
            loss = K.mean(model.output[:, i_kernal])
        else:
            loss = K.mean(model.layers[layer_target].output[:, :, :, i_kernal])  # m*28*28*128
        # 计算图像对损失函数的梯度
        grads = K.gradients(loss, input_img)[0]
        # 效用函数通过其L2范数标准化张量
        grads /= (K.sqrt(K.mean(K.square(grads))) + 1e-5)
        # 此函数返回给定输入图像的损耗和梯度
        iterate = K.function([input_img], [loss, grads])
        # 从带有一些随机噪声的灰色图像开始
        np.random.seed(0)
        # 随机图像
        # input_img_data = np.random.randint(0, 255, (1, img_height, img_width, num_channels))  # 随机
        # input_img_data = np.zeros((1, img_height, img_width, num_channels))   # 零值
        input_img_data = np.random.random((1, img_height, img_width, num_channels)) * 20 + 128.   # 随机灰度
        input_img_data = np.array(input_img_data, dtype=float)
        failed = False
        # 运行梯度上升
        print(####################################, i_kernal + 1)
        loss_value_pre = 0
        # 运行梯度上升num_iterate步
        for i in range(num_iterate):
            loss_value, grads_value = iterate([input_img_data])
            if i % int(num_iterate/5) == 0:
                print(Iteration %d/%d, loss: %f % (i, num_iterate, loss_value))
                print(Mean grad: %f % np.mean(grads_value))
                if all(np.abs(grads_val) < 0.000001 for grads_val in grads_value.flatten()):
                    failed = True
                    print(Failed)
                    break
                if loss_value_pre != 0 and loss_value_pre > loss_value:
                    break
                if loss_value_pre == 0:
                    loss_value_pre = loss_value
                # if loss_value > 0.99:
                #     break
            input_img_data += grads_value * 1  # e-3
        img_re = deprocess_image(input_img_data[0])
        if num_channels == 1:
            img_re = np.reshape(img_re, (img_height, img_width))
        else:
            img_re = np.reshape(img_re, (img_height, img_width, num_channels))
        plt.subplot(np.ceil(np.sqrt(num_out)), np.ceil(np.sqrt(num_out)), i_kernal + 1)
        plt.imshow(img_re)  # , cmap=gray
        plt.axis(off)

    plt.show()

运行

model = load_model(train3.h5)
kernelvisual(model,-1)	# 对最终输出可视化
kernelvisual(model,6)	# 对第二个卷积层可视化

热度图

运行

from keras.applications.vgg16 import VGG16
from keras.applications.vgg16 import preprocess_input

model = VGG16(weights=imagenet)
data_img = image.img_to_array(image.load_img(elephant.png))
# VGG16预处理:RGB转BGR,并对每一个颜色通道去均值中心化
data_img = preprocess_input(data_img)
img_show = image.img_to_array(image.load_img(elephant.png))

heatmaps(model, data_img, img_show)

elephant.png

结语

踩坑踩得我脚疼