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

以上这篇keras CNN卷积核可视化,热度图教程就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持脚本之家。