keras CNN卷积核可视化,热度图

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

import cv2
import matplotlib.pyplot as plt
import numpy as np
from keras import backend as K
from keras.preprocessing import image


def heatmap(model, data_img, layer_idx, img_show=None, pred_idx=None):
    # 图像处理
    if data_img.shape.__len__() != 4:
        # 由于用作输入的img需要预处理,用作显示的img需要原图,因此分开两个输入
        if img_show is None:
            img_show = data_img
        # 缩放
        input_shape = K.int_shape(model.input)[1:3]     # (28,28)
        data_img = image.img_to_array(image.array_to_img(data_img).resize(input_shape))
        # 添加一个维度->(1, 224, 224, 3)
        data_img = np.expand_dims(data_img, axis=0)
    if pred_idx is None:
        # 预测
        preds = model.predict(data_img)
        # 获取最高预测项的index
        pred_idx = np.argmax(preds[0])
    # 目标输出估值
    target_output = model.output[:, pred_idx]
    # 目标层的输出代表各通道关注的位置
    last_conv_layer_output = model.layers[layer_idx].output
    # 求最终输出对目标层输出的导数(优化目标层输出),代表目标层输出对结果的影响
    grads = K.gradients(target_output, last_conv_layer_output)[0]
    # 将每个通道的导数取平均,值越高代表该通道影响越大
    pooled_grads = K.mean(grads, axis=(0, 1, 2))
    iterate = K.function([model.input], [pooled_grads, last_conv_layer_output[0]])
    pooled_grads_value, conv_layer_output_value = iterate([data_img])
    # 将各通道关注的位置和各通道的影响乘起来
    for i in range(conv_layer_output_value.shape[-1]):
        conv_layer_output_value[:, :, i] *= pooled_grads_value[i]

    # 对各通道取平均得图片位置对结果的影响
    heatmap = np.mean(conv_layer_output_value, axis=-1)
    # 规范化
    heatmap = np.maximum(heatmap, 0)
    heatmap /= np.max(heatmap)
    # plt.matshow(heatmap)
    # plt.show()
    # 叠加图片
    # 缩放成同等大小
    heatmap = cv2.resize(heatmap, (img_show.shape[1], img_show.shape[0]))
    heatmap = np.uint8(255 * heatmap)
    # 将热图应用于原始图像.由于opencv热度图为BGR,需要转RGB
    superimposed_img = img_show + cv2.applyColorMap(heatmap, cv2.COLORMAP_JET)[:,:,::-1] * 0.4
    # 截取转uint8
    superimposed_img = np.minimum(superimposed_img, 255).astype('uint8')
    return superimposed_img, heatmap
    # 显示图片
    # plt.imshow(superimposed_img)
    # plt.show()
    # 保存为文件
    # superimposed_img = img + cv2.applyColorMap(heatmap, cv2.COLORMAP_JET) * 0.4
    # cv2.imwrite('ele.png', superimposed_img)

# 生成所有卷积层的热度图
def heatmaps(model, data_img, img_show=None):
    if img_show is None:
        img_show = np.array(data_img)
    # Resize
    input_shape = K.int_shape(model.input)[1:3]  # (28,28,1)
    data_img = image.img_to_array(image.array_to_img(data_img).resize(input_shape))
    # 添加一个维度->(1, 224, 224, 3)
    data_img = np.expand_dims(data_img, axis=0)
    # 预测
    preds = model.predict(data_img)
    # 获取最高预测项的index
    pred_idx = np.argmax(preds[0])
    print("预测为:%d(%f)" % (pred_idx, preds[0][pred_idx]))
    indexs = []
    for i in range(model.layers.__len__()):
        if 'conv' in model.layers[i].name:
            indexs.append(i)
    print('模型共有%d个卷积层' % indexs.__len__())
    plt.suptitle('heatmaps for each conv')
    for i in range(indexs.__len__()):
        ret = heatmap(model, data_img, indexs[i], img_show=img_show, pred_idx=pred_idx)
        plt.subplot(np.ceil(np.sqrt(indexs.__len__()*2)), np.ceil(np.sqrt(indexs.__len__()*2)), i*2 + 1)\
            .set_title(model.layers[indexs[i]].name)
        plt.imshow(ret[0])
        plt.axis('off')
        plt.subplot(np.ceil(np.sqrt(indexs.__len__()*2)), np.ceil(np.sqrt(indexs.__len__()*2)), i*2 + 2)\
            .set_title(model.layers[indexs[i]].name)
        plt.imshow(ret[1])
        plt.axis('off')
    plt.show()

 

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