7.keras-模型保存和载入

import numpy as np
from keras.datasets import mnist
from keras.utils import np_utils
from keras.models import Sequential,load_model
from keras.layers import *
from keras.optimizers import SGD

import os

import tensorflow as tf

# 载入数据
(x_train,y_train),(x_test,y_test) = mnist.load_data()

# 预处理
# 将（60000，28，28）转化为（600000，784），好输入展开层
x_train = x_train.reshape(x_train.shape[0],-1)/255.0
x_test= x_test.reshape(x_test.shape[0],-1)/255.0
# 将输出转化为one_hot编码
y_train = np_utils.to_categorical(y_train,num_classes=10)
y_test = np_utils.to_categorical(y_test,num_classes=10)

# 加载模型
if os.path.exists(‘model.h5‘):
    print(‘--------load model-----------‘)
    model = load_model(‘model.h5‘)
else:
    # 创建网络
    model = Sequential([
        # 输入784输出10个
        Dense(units=10,input_dim=784,bias_initializer=‘one‘,activation=‘softmax‘)
    ])

    # 编译
    # 自定义优化器
    sgd = SGD(lr=0.1)
    model.compile(optimizer=sgd,
                  loss=‘mse‘,
                  # 得到训练过程中的准确率
                  metrics=[‘accuracy‘])

    model.fit(x_train,y_train,batch_size=32,epochs=10,validation_split=0.2)



    # 保存模型
    model.save(‘model.h5‘)


# 评估模型
loss,acc = model.evaluate(x_test,y_test,)
print(‘\ntest loss‘,loss)
print(‘test acc‘,acc)

# 保存参数
model.save_weights(‘my_model_weights.h5‘)
model.load_weights(‘my_model_weights.h5‘)

# 保存模型结构
from keras.models import  model_from_json
json_string = model.to_json()
model = model_from_json(json_string)
print(json_string)