keras中的模型加载_from keras.models import k

keras中的模型加载

from keras import backend as K
from keras.models import load_model
from PIL import Image
import numpy as np

source='/path/'

def get_layer_output(model, x, index=-1):
    layer = K.function([model.input], [model.layers[index].output])
    return layer([x])[0]

#加载模型
model = load_model(source + 'h5/文件名.h5')

#循环输出网络各层的参数
for i in range(len(model.layers)):
    print("i,model.layers[index].output:",i,model.layers[i].output)

names = [weight.name for layer in model.layers for weight in layer.weights]
#获取权重
weights = model.get_weights()
#输出各层权重值的尺寸
for name, weight in zip(names, weights):
    print(name, weight.shape)
#得到指定层的权重和偏置值
weight_dense_1,bias_dense_1 = model.get_layer('dense_1').get_weights()
#输出权重和偏置值
print(weight_dense_1)
print(bias_dense_1)
#存储权重值到本地
np.save(source+"result/weight_dense_1.npy", weight_dense_1)
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