如何为每个时期保存keras模型的权重? [英] How to save weights of keras model for each epoch?

问题描述

我想保存keras模型，并且想保存每个历元的权重以具有最佳权重.我该怎么做?

I want to save keras model and I want to save weights of each epoch to have best weights. How I do that?

任何帮助将不胜感激.

代码:

def createModel():
    input_shape=(1, 22, 5, 3844)
    model = Sequential()
    #C1
    model.add(Conv3D(16, (22, 5, 5), strides=(1, 2, 2), padding='same',activation='relu',data_format= "channels_first", input_shape=input_shape))
    model.add(keras.layers.MaxPooling3D(pool_size=(1, 2, 2),data_format= "channels_first",  padding='same'))
    model.add(BatchNormalization())
    #C2
    model.add(Conv3D(32, (1, 3, 3), strides=(1, 1,1), padding='same',data_format= "channels_first",  activation='relu'))#incertezza se togliere padding
    model.add(keras.layers.MaxPooling3D(pool_size=(1,2, 2),data_format= "channels_first", ))
    model.add(BatchNormalization())

     #C3
    model.add(Conv3D(64, (1,3, 3), strides=(1, 1,1), padding='same',data_format= "channels_first",  activation='relu'))#incertezza se togliere padding
    model.add(keras.layers.MaxPooling3D(pool_size=(1,2, 2),data_format= "channels_first",padding='same' ))
    model.add(Dense(64, input_dim=64, kernel_regularizer=regularizers.l2(0.01), activity_regularizer=regularizers.l1(0.01)))
    model.add(BatchNormalization())

    model.add(Flatten())
    model.add(Dropout(0.5))
    model.add(Dense(256, activation='sigmoid'))
    model.add(Dropout(0.5))
    model.add(Dense(2, activation='softmax'))

    opt_adam = keras.optimizers.Adam(lr=0.00001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)
    model.compile(loss='categorical_crossentropy', optimizer=opt_adam, metrics=['accuracy'])

    return model

推荐答案

您应该同时使用model.get_weights()和LambdaCallback函数:

You should use model.get_weights() and LambdaCallback function together:

1. model.get_weights():以Numpy数组的形式返回模型中所有权重张量的列表.

1. model.get_weights(): Returns a list of all weight tensors in the model, as Numpy arrays.

model = Sequential()
weights = model.get_weights()

LambdaCallback :此回调由匿名函数构成，将在适当的时间调用

LambdaCallback: This callback is constructed with anonymous functions that will be called at the appropriate time

import json
json_log = open('loss_log.json', mode='wt', buffering=1)
json_logging_callback = LambdaCallback(
            on_epoch_end=lambda epoch, logs: json_log.write(
                json.dumps({'epoch': epoch, 'loss': logs['loss']}) + '\n'),
            on_train_end=lambda logs: json_log.close()
)

model.fit(...,
          callbacks=[json_logging_callback])

考虑代码时，您应该编写 callback 函数并将其添加到您的 model 中:

When your code is considered, you should write callback function and add to your model:

import json
from keras.callbacks import LambdaCallback

json_log = open('loss_log.json', mode='wt', buffering=1)
json_logging_callback = LambdaCallback(
            on_epoch_end=lambda epoch, logs: json_log.write(
                json.dumps({'epoch': epoch, 
                            'loss': logs['loss'],
                            'weights': model.get_weights()}) + '\n'),
            on_train_end=lambda logs: json_log.close()
)

model.compile(loss='categorical_crossentropy',
              optimizer=opt_adam, 
              metrics=['accuracy'])

model.fit_generator(..., callbacks=[json_logging_callback])

此代码将所有图层的所有权重写入json文件.如果要在特定图层中保存权重，只需使用

This code write your all weights in all layers to json file. If you want to save weights in specific layer, just change the code with

model.layers[0].get_weights()

这篇关于如何为每个时期保存keras模型的权重?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！