keras得到每层的系数方式

weights = np.array(model.get_weights())
print(weights)
print(weights[0].shape)
print(weights[1].shape)

from keras import models
#使用matlpotlib模块进行绘图的操作
import matplotlib.pylot as plt
#images是一个batch的输入图像，batch_input[batch图像数量，尺寸高，尺寸宽，3（rgb通道数量）]
#model是训练好的模型
#model = load_model('path')
nb_images = len(images)
batch_input = np.zeros((nb_images, net_h, net_w, 3))

# preprocess the input
for i in range(nb_images):
 batch_input[i] = preprocess_input(images[i], net_h, net_w)  

# run the prediction
#batch_output为一个样本的所有通道输出特征映射，本文应用特征金字塔结构，有三个维度的特征提取层
#batch_output[0]是第一个维度的特征提取层所有通道的输出特征映射，四维，本文例子中为[1, 52, 52, 72]
#[一个样本，尺寸，尺寸，通道数]

#也可以是batch_output = model.predict(batch_input)
batch_output = model.predict_on_batch(batch_input)
batch_boxes = [None]*nb_images
print(batch_output[0].shape)
#display feature map

#下面为归一化到0-255空间内
xx = batch_output[0]
max = np.max(xx)
print(max,"max value is :")
X_output = X_output .astype("float32") / max * 255

#下面的30为第30个通道
X_output = xx[0,:,:,30]

#使用matplotlib显示图像
plt.figure()
plt.imshow(X_output, cmap='viridis')
plt.show()

#输出结果

from keras import backend as k
from keras import models
import matplotlib.pylot as plt
model = load_model('...')
layer_1 =k.function([model.layers[0].input], [model.layers[1].output])

#第2个model,layers[]改成你想显示的层数
f1 = layer_1[input_image][0]
f1.image = f1[0,:,:,channel]
plt,matshow(f1.image, camp='viridis')
plt.show()

from keras import models
import matplotlib.pylot as plt
from keras import backend as k
#images是一个batch的输入图像，batch_input[batch图像数量，尺寸高，尺寸宽，3（rgb通道数量）]
#model是训练好的模型
#model = load_model('path')
nb_images = len(images)
batch_input = np.zeros((nb_images, net_h, net_w, 3))

# preprocess the input
for i in range(nb_images):
 batch_input[i] = preprocess_input(images[i], net_h, net_w)  
#display feature map

#可视化第一层的特征映射
layer_1 = K.function([model.layers[0].input], [model.layers[1].output])
f1 = layer_1([batch_input])[0]
print(f1.shape)
max = np.max(f1)
f1 =f1.astype("float32") / max * 255
plt.figure()

#显示第一层网络前5个通道的特征映射
for i in range(5):
 plt.subplot(2, 3, i+1)
 plt.imshow(f1[0,:,:,i], cmap='viridis')
plt.show()