keras CNN卷积核可视化,热度图教程

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
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