使用model.save(filepath)将Keras模型和权重保存在一个HDF5文件中，该文件将包含：

模型的结构，以便重构该模型
模型的权重
训练配置（损失函数，优化器等）
优化器的状态，以便于从上次训练中断的地方开始

使用keras.models.load_model(filepath)来重新实例化你的模型，如果文件中存储了训练配置的话，该函数还会同时完成模型的编译

只保存模型结构,而不包含其权重或配置信息

#保存成json格式的文件
# save as JSONjson_string = model.to_json()
open('my_model_architecture.json','w').write(json_string)
from keras.models import model_from_json
model = model_from_json(open('my_model_architecture.json').read())  #保存成yaml文件
# save as YAML
yaml_string = model.to_yaml()
open('my_model_architectrue.yaml','w').write(yaml_string)
from keras.models import model_from_yaml
model = model_from_yaml(open('my_model_architecture.yaml').read())#这项操作将把模型序列化为json或yaml文件，这些文件对人而言也是友好的，如果需要的话你甚至可以手动打开这些文件并进行编辑。当然，你也可以从保存好的json文件或yaml文件中载入模型：# model reconstruction from JSON:
from keras.modelsimport model_from_json
model = model_from_json(json_string)  # model reconstruction from YAML
model =model_from_yaml(yaml_string)

需要保存模型的权重

import keras.models import load_model
model.save_weights('my_model_weights.h5')
#需要在代码中初始化一个完全相同的模型
model.load_weights('my_model_weights.h5')
#需要加载权重到不同的网络结构（有些层一样）中，例如fine-tune或transfer-learning，可以通过层名字来加载模型
model.load_weights('my_model_weights.h5', by_name=True)

 open('my_model_architecture.json','w').write(json_string)
model.save_weights('my_model_weights.h5')
model = model_from_json(open('my_model_architecture.json').read())
model.load_weights('my_model_weights.h5')

实时保存模型结构、训练出来的权重、及优化器状态并调用

keras 的callback参数可以帮助我们实现在训练过程中的适当时机被调用。实现实时保存训练模型以及训练参数

keras.callbacks.ModelCheckpoint(filepath, monitor='val_loss', verbose=0, save_best_only=False, save_weights_only=False, mode='auto', period=1
)1. filename：字符串，保存模型的路径
2. monitor：需要监视的值
3. verbose：信息展示模式，0或1
4. save_best_only：当设置为True时，将只保存在验证集上性能最好的模型
5. mode：‘auto’，‘min’，‘max’之一，在save_best_only=True时决定性能最佳模型的评判准则，例如，当监测值为val_acc时，模式应为max，当检测值为val_loss时，模式应为min。在auto模式下，评价准则由被监测值的名字自动推断。
6. save_weights_only：若设置为True，则只保存模型权重，否则将保存整个模型（包括模型结构，配置信息等）
7. period：CheckPoint之间的间隔的epoch数

示例

"""
假如原模型为：model = Sequential()model.add(Dense(2, input_dim=3, name="dense_1"))model.add(Dense(3, name="dense_2"))...model.save_weights(fname)
"""
# new model
model = Sequential()
model.add(Dense(2, input_dim=3, name="dense_1"))  # will be loaded
model.add(Dense(10, name="new_dense"))  # will not be loaded# load weights from first model; will only affect the first layer, dense_1.
model.load_weights(fname, by_name=True)

How to Check-Point Deep Learning Models in Keras

Checkpoint Neural Network Model Improvements

# Checkpoint the weights when validation accuracy improves
from keras.models import Sequential
from keras.layers import Dense
from keras.callbacks import ModelCheckpoint
import matplotlib.pyplot as plt
import numpy
# fix random seed for reproducibility
seed = 7
numpy.random.seed(seed)
# load pima indians dataset
dataset = numpy.loadtxt("pima-indians-diabetes.csv", delimiter=",")
# split into input (X) and output (Y) variables
X = dataset[:,0:8]
Y = dataset[:,8]
# create model
model = Sequential()
model.add(Dense(12, input_dim=8, kernel_initializer='uniform', activation='relu'))
model.add(Dense(8, kernel_initializer='uniform', activation='relu'))
model.add(Dense(1, kernel_initializer='uniform', activation='sigmoid'))
# Compile model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# checkpoint
filepath="weights-improvement-{epoch:02d}-{val_acc:.2f}.hdf5"
checkpoint = ModelCheckpoint(filepath, monitor='val_acc', verbose=1, save_best_only=True, mode='max')
callbacks_list = [checkpoint]
# Fit the model
model.fit(X, Y, validation_split=0.33, epochs=150, batch_size=10, callbacks=callbacks_list, verbose=0)

Running the example produces the following output (truncated for brevity):

...
Epoch 00134: val_acc did not improve
Epoch 00135: val_acc did not improve
Epoch 00136: val_acc did not improve
Epoch 00137: val_acc did not improve
Epoch 00138: val_acc did not improve
Epoch 00139: val_acc did not improve
Epoch 00140: val_acc improved from 0.83465 to 0.83858, saving model to weights-improvement-140-0.84.hdf5
Epoch 00141: val_acc did not improve
Epoch 00142: val_acc did not improve
Epoch 00143: val_acc did not improve
Epoch 00144: val_acc did not improve
Epoch 00145: val_acc did not improve
Epoch 00146: val_acc improved from 0.83858 to 0.84252, saving model to weights-improvement-146-0.84.hdf5
Epoch 00147: val_acc did not improve
Epoch 00148: val_acc improved from 0.84252 to 0.84252, saving model to weights-improvement-148-0.84.hdf5
Epoch 00149: val_acc did not improve

You will see a number of files in your working directory containing the network weights in HDF5 format. For example:

...
weights-improvement-53-0.76.hdf5
weights-improvement-71-0.76.hdf5
weights-improvement-77-0.78.hdf5
weights-improvement-99-0.78.hdf5

Checkpoint Best Neural Network Model Only

# Checkpoint the weights for best model on validation accuracy
from keras.models import Sequential
from keras.layers import Dense
from keras.callbacks import ModelCheckpoint
import matplotlib.pyplot as plt
import numpy
# fix random seed for reproducibility
seed = 7
numpy.random.seed(seed)
# load pima indians dataset
dataset = numpy.loadtxt("pima-indians-diabetes.csv", delimiter=",")
# split into input (X) and output (Y) variables
X = dataset[:,0:8]
Y = dataset[:,8]
# create model
model = Sequential()
model.add(Dense(12, input_dim=8, kernel_initializer='uniform', activation='relu'))
model.add(Dense(8, kernel_initializer='uniform', activation='relu'))
model.add(Dense(1, kernel_initializer='uniform', activation='sigmoid'))
# Compile model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# checkpoint
filepath="weights.best.hdf5"
checkpoint = ModelCheckpoint(filepath, monitor='val_acc', verbose=1, save_best_only=True, mode='max')
callbacks_list = [checkpoint]
# Fit the model
model.fit(X, Y, validation_split=0.33, epochs=150, batch_size=10, callbacks=callbacks_list, verbose=0)

Running this example provides the following output (truncated for brevity):

...
Epoch 00139: val_acc improved from 0.79134 to 0.79134, saving model to weights.best.hdf5
Epoch 00140: val_acc did not improve
Epoch 00141: val_acc did not improve
Epoch 00142: val_acc did not improve
Epoch 00143: val_acc did not improve
Epoch 00144: val_acc improved from 0.79134 to 0.79528, saving model to weights.best.hdf5
Epoch 00145: val_acc improved from 0.79528 to 0.79528, saving model to weights.best.hdf5
Epoch 00146: val_acc did not improve
Epoch 00147: val_acc did not improve
Epoch 00148: val_acc did not improve
Epoch 00149: val_acc did not improve

You should see the weight file in your local directory.


weights.best.hdf5

Loading a Check-Pointed Neural Network Model

# How to load and use weights from a checkpoint
from keras.models import Sequential
from keras.layers import Dense
from keras.callbacks import ModelCheckpoint
import matplotlib.pyplot as plt
import numpy
# fix random seed for reproducibility
seed = 7
numpy.random.seed(seed)
# create model
model = Sequential()
model.add(Dense(12, input_dim=8, kernel_initializer='uniform', activation='relu'))
model.add(Dense(8, kernel_initializer='uniform', activation='relu'))
model.add(Dense(1, kernel_initializer='uniform', activation='sigmoid'))
# load weights
model.load_weights("weights.best.hdf5")
# Compile model (required to make predictions)
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
print("Created model and loaded weights from file")
# load pima indians dataset
dataset = numpy.loadtxt("pima-indians-diabetes.csv", delimiter=",")
# split into input (X) and output (Y) variables
X = dataset[:,0:8]
Y = dataset[:,8]
# estimate accuracy on whole dataset using loaded weights
scores = model.evaluate(X, Y, verbose=0)
print("%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))

Running the example produces the following output

Created model and loaded weights from file
acc: 77.73%

参考文献

How to Check-Point Deep Learning Models in Keras

http://blog.csdn.net/u010159842/article/details/54602217

用Keras搞一个阅读理解机器人
Keras中文文档
如何保存Keras模型
人工神经网络(三) –keras模型的保存和使用

keras如何保存模型相关推荐

Keras：保存模型并载入模型继续训练
参考:https://blog.csdn.net/qq_34218078/article/details/101663882
Keras框架下的保存模型和加载模型
在Keras框架下训练深度学习模型时,一般思路是在训练环境下训练出模型,然后拿训练好的模型(即保存模型相应信息的文件)到生产环境下去部署.在训练过程中我们可能会遇到以下情况: 需要运行很长时间的程序在 ...
python模型保存save_浅谈keras保存模型中的save()和save_weights()区别
今天做了一个关于keras保存模型的实验,希望有助于大家了解keras保存模型的区别. 我们知道keras的模型一般保存为后缀名为h5的文件,比如final_model.h5.同样是h5文件用save ...
keras保存模型_TF2 8.模型保存与加载
举个例子:先训练出一个模型 import 接下来第一种方法:只保留模型的参数:这个有2种方法: model.save_weights("adasd.h5")model.load_w ...
keras中的模型保存和加载
tensorflow中的模型常常是protobuf格式,这种格式既可以是二进制也可以是文本.keras模型保存和加载与tensorflow不同,keras中的模型保存和加载往往是保存成hdf5格式. ...
Keras中保存和加载权重及模型结构
微信公众号 1. 保存和加载模型结构 (1)保存为JSON字串 json_string = model.to_json() (2)从JSON字串重构模型 from keras.models impor ...
Keras如何保存、加载Keras模型
链接 Keras中文文档一.如何保存 Keras 模型? 1.保存/加载整个模型(结构 + 权重 + 优化器状态) 不建议使用 pickle 或 cPickle 来保存 Keras 模型. 你可以使 ...
[Keras] 使用Keras调用多GPU时出现无法保存模型的解决方法
在使用keras 的并行多路GPU时出现了模型无法保存,在使用单个GPU时运行完全没有问题.运行出现can't pickle的问题随后在网上找了很多解决方法.下面列举一些我实验成功的方法. 方法一 ...
Tensorflow 2.x(keras)源码详解之第十章：keras中的模型保存与加载(详解Checkpointmd5模型序列化)
大家好,我是爱编程的喵喵.双985硕士毕业,现担任全栈工程师一职,热衷于将数据思维应用到工作与生活中.从事机器学习以及相关的前后端开发工作.曾在阿里云.科大讯飞.CCF等比赛获得多次Top名次.现 ...

keras如何保存模型

Checkpoint Neural Network Model Improvements

Checkpoint Best Neural Network Model Only

Loading a Check-Pointed Neural Network Model

keras如何保存模型相关推荐

最新文章

热门文章