kerasCNN卷积核可视化,热度图教程资源-CSDN文库

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keras CNN卷积核可视化卷积核可视化,热度图教程热度图教程

卷积核可视化卷积核可视化

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

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