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Keras保存模型并載入模型繼續(xù)訓(xùn)練的實(shí)現(xiàn)

更新時(shí)間：2021年02月20日 09:41:14 作者：凌逆戰(zhàn)

這篇文章主要介紹了Keras保存模型并載入模型繼續(xù)訓(xùn)練的實(shí)現(xiàn)，文中通過示例代碼介紹的非常詳細(xì)，對大家的學(xué)習(xí)或者工作具有一定的參考學(xué)習(xí)價(jià)值，需要的朋友們下面隨著小編來一起學(xué)習(xí)學(xué)習(xí)吧

我們以MNIST手寫數(shù)字識別為例

import numpy as np
from keras.datasets import mnist
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import SGD
 
# 載入數(shù)據(jù)
(x_train,y_train),(x_test,y_test) = mnist.load_data()
# (60000,28,28)
print('x_shape:',x_train.shape)
# (60000)
print('y_shape:',y_train.shape)
# (60000,28,28)->(60000,784)
x_train = x_train.reshape(x_train.shape[0],-1)/255.0
x_test = x_test.reshape(x_test.shape[0],-1)/255.0
# 換one hot格式
y_train = np_utils.to_categorical(y_train,num_classes=10)
y_test = np_utils.to_categorical(y_test,num_classes=10)
 
# 創(chuàng)建模型，輸入784個神經(jīng)元，輸出10個神經(jīng)元
model = Sequential([
    Dense(units=10,input_dim=784,bias_initializer='one',activation='softmax')
  ])
 
# 定義優(yōu)化器
sgd = SGD(lr=0.2)
 
# 定義優(yōu)化器，loss function，訓(xùn)練過程中計(jì)算準(zhǔn)確率
model.compile(
  optimizer = sgd,
  loss = 'mse',
  metrics=['accuracy'],
)
 
# 訓(xùn)練模型
model.fit(x_train,y_train,batch_size=64,epochs=5)
 
# 評估模型
loss,accuracy = model.evaluate(x_test,y_test)
 
print('\ntest loss',loss)
print('accuracy',accuracy)
 
# 保存模型
model.save('model.h5')  # HDF5文件，pip install h5py

載入初次訓(xùn)練的模型，再訓(xùn)練

import numpy as np
from keras.datasets import mnist
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import SGD
from keras.models import load_model
# 載入數(shù)據(jù)
(x_train,y_train),(x_test,y_test) = mnist.load_data()
# (60000,28,28)
print('x_shape:',x_train.shape)
# (60000)
print('y_shape:',y_train.shape)
# (60000,28,28)->(60000,784)
x_train = x_train.reshape(x_train.shape[0],-1)/255.0
x_test = x_test.reshape(x_test.shape[0],-1)/255.0
# 換one hot格式
y_train = np_utils.to_categorical(y_train,num_classes=10)
y_test = np_utils.to_categorical(y_test,num_classes=10)
 
# 載入模型
model = load_model('model.h5')
 
# 評估模型
loss,accuracy = model.evaluate(x_test,y_test)
 
print('\ntest loss',loss)
print('accuracy',accuracy)
 
# 訓(xùn)練模型
model.fit(x_train,y_train,batch_size=64,epochs=2)
 
# 評估模型
loss,accuracy = model.evaluate(x_test,y_test)
 
print('\ntest loss',loss)
print('accuracy',accuracy)
 
# 保存參數(shù)，載入?yún)?shù)
model.save_weights('my_model_weights.h5')
model.load_weights('my_model_weights.h5')
# 保存網(wǎng)絡(luò)結(jié)構(gòu)，載入網(wǎng)絡(luò)結(jié)構(gòu)
from keras.models import model_from_json
json_string = model.to_json()
model = model_from_json(json_string)
 
print(json_string)