Keras:如何保存模型并继续培训? [英] Keras: How to save model and continue training?

问题描述

我有一个已经训练了40个时期的模型.我为每个纪元保留了检查点，并用model.save()保存了模型.培训代码是

I have a model that I've trained for 40 epochs. I kept checkpoints for each epochs, also saved the model with model.save(). The code for training is

n_units = 1000
model = Sequential()
model.add(LSTM(n_units, input_shape=(None, vec_size), return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(n_units, return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(n_units))
model.add(Dropout(0.2))
model.add(Dense(vec_size, activation='linear'))
model.compile(loss='mean_squared_error', optimizer='adam')
# define the checkpoint
filepath="word2vec-{epoch:02d}-{loss:.4f}.hdf5"
checkpoint = ModelCheckpoint(filepath, monitor='loss', verbose=1, save_best_only=True, mode='min')
callbacks_list = [checkpoint]
# fit the model
model.fit(x, y, epochs=40, batch_size=50, callbacks=callbacks_list)

但是，当加载模型并再次训练时，它会像以前从未训练过一样重新开始.损失不是从上一次训练开始的.

However, When load the model and train again, it starts all over as if it hasn't been trained before. The loss doesn't start from the last training.

令我困惑的是，当我使用重新定义模型结构和load_weight的模型加载模型时，model.predict()可以很好地工作.因此，我相信模型权重已加载.

What confuses me is, when I load model with redefining model structure and load_weight, the model.predict() works well. Thus I believe the model weights are loaded.

model = Sequential()
model.add(LSTM(n_units, input_shape=(None, vec_size), return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(n_units, return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(n_units))
model.add(Dropout(0.2))
model.add(Dense(vec_size, activation='linear'))
filename = "word2vec-39-0.0027.hdf5"
model.load_weights(filename)
model.compile(loss='mean_squared_error', optimizer='adam')

但是，当我继续使用

filepath="word2vec-{epoch:02d}-{loss:.4f}.hdf5"
checkpoint = ModelCheckpoint(filepath, monitor='loss', verbose=1, save_best_only=True, mode='min')
callbacks_list = [checkpoint]
# fit the model
model.fit(x, y, epochs=40, batch_size=50, callbacks=callbacks_list)

损失高达初始状态.

我搜索并找到了一些保存和加载模型的示例: http://machinelearningmastery.com/save-load-keras-deep-learning-型号/ https://github.com/fchollet/keras/issues/1872

I searched and found some examples of saving and loading models: http://machinelearningmastery.com/save-load-keras-deep-learning-models/ https://github.com/fchollet/keras/issues/1872

但是它们都不起作用.谁能帮我?谢谢.

But none of them works. Can anyone help me? Thanks.

更新

加载已训练的Keras模型并继续训练

我尝试了

model.save('partly_trained.h5')
del model
load_model('partly_trained.h5')

有效.但是，当我关闭python时，请重新打开并再次load_model.它失败.损失与初始状态一样高.

it works. But when I closed python, reopen and load_model again. It fails. The loss is as high as the initial state.

更新

我尝试了Yu-Yang的示例代码.有用.但是回到我的代码，我仍然失败了. 这是原始的训练.第二个时期应从损失= 3.1 ***开始.

I tried Yu-Yang's example code. It works. But back to my code, I still failed. This is the original training. The second epoch should start with loss = 3.1***.

13700/13846 [============================>.] - ETA: 0s - loss: 3.0519
13750/13846 [============================>.] - ETA: 0s - loss: 3.0511
13800/13846 [============================>.] - ETA: 0s - loss: 3.0512Epoch 00000: loss improved from inf to 3.05101, saving model to LPT-00-3.0510.h5

13846/13846 [==============================] - 81s - loss: 3.0510    
Epoch 2/60

   50/13846 [..............................] - ETA: 80s - loss: 3.1754
  100/13846 [..............................] - ETA: 78s - loss: 3.1174
  150/13846 [..............................] - ETA: 78s - loss: 3.0745

我关闭了Python，然后重新打开它.使用model = load_model("LPT-00-3.0510.h5")加载模型，然后使用

I closed Python and reopen it. Loaded model with model = load_model("LPT-00-3.0510.h5") then train with

filepath="LPT-{epoch:02d}-{loss:.4f}.h5"
checkpoint = ModelCheckpoint(filepath, monitor='loss', verbose=1, save_best_only=True, mode='min')
callbacks_list = [checkpoint]
# fit the model
model.fit(x, y, epochs=60, batch_size=50, callbacks=callbacks_list)

损失从4.54开始.

Epoch 1/60
   50/13846 [..............................] - ETA: 162s - loss: 4.5451
   100/13846 [..............................] - ETA: 113s - loss: 4.3835

推荐答案

由于很难弄清问题出在哪里，我从您的代码中创建了一个玩具示例，看来还可以.

As it's quite difficult to clarify where the problem is, I created a toy example from your code, and it seems to work alright.

import numpy as np
from numpy.testing import assert_allclose
from keras.models import Sequential, load_model
from keras.layers import LSTM, Dropout, Dense
from keras.callbacks import ModelCheckpoint

vec_size = 100
n_units = 10

x_train = np.random.rand(500, 10, vec_size)
y_train = np.random.rand(500, vec_size)

model = Sequential()
model.add(LSTM(n_units, input_shape=(None, vec_size), return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(n_units, return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(n_units))
model.add(Dropout(0.2))
model.add(Dense(vec_size, activation='linear'))
model.compile(loss='mean_squared_error', optimizer='adam')

# define the checkpoint
filepath = "model.h5"
checkpoint = ModelCheckpoint(filepath, monitor='loss', verbose=1, save_best_only=True, mode='min')
callbacks_list = [checkpoint]

# fit the model
model.fit(x_train, y_train, epochs=5, batch_size=50, callbacks=callbacks_list)

# load the model
new_model = load_model(filepath)
assert_allclose(model.predict(x_train),
                new_model.predict(x_train),
                1e-5)

# fit the model
checkpoint = ModelCheckpoint(filepath, monitor='loss', verbose=1, save_best_only=True, mode='min')
callbacks_list = [checkpoint]
new_model.fit(x_train, y_train, epochs=5, batch_size=50, callbacks=callbacks_list)

模型加载后，损耗继续减少. (重新启动python也没有问题)

The loss continues to decrease after model loading. (restarting python also gives no problem)

Using TensorFlow backend.
Epoch 1/5
500/500 [==============================] - 2s - loss: 0.3216     Epoch 00000: loss improved from inf to 0.32163, saving model to model.h5
Epoch 2/5
500/500 [==============================] - 0s - loss: 0.2923     Epoch 00001: loss improved from 0.32163 to 0.29234, saving model to model.h5
Epoch 3/5
500/500 [==============================] - 0s - loss: 0.2542     Epoch 00002: loss improved from 0.29234 to 0.25415, saving model to model.h5
Epoch 4/5
500/500 [==============================] - 0s - loss: 0.2086     Epoch 00003: loss improved from 0.25415 to 0.20860, saving model to model.h5
Epoch 5/5
500/500 [==============================] - 0s - loss: 0.1725     Epoch 00004: loss improved from 0.20860 to 0.17249, saving model to model.h5

Epoch 1/5
500/500 [==============================] - 0s - loss: 0.1454     Epoch 00000: loss improved from inf to 0.14543, saving model to model.h5
Epoch 2/5
500/500 [==============================] - 0s - loss: 0.1289     Epoch 00001: loss improved from 0.14543 to 0.12892, saving model to model.h5
Epoch 3/5
500/500 [==============================] - 0s - loss: 0.1169     Epoch 00002: loss improved from 0.12892 to 0.11694, saving model to model.h5
Epoch 4/5
500/500 [==============================] - 0s - loss: 0.1097     Epoch 00003: loss improved from 0.11694 to 0.10971, saving model to model.h5
Epoch 5/5
500/500 [==============================] - 0s - loss: 0.1057     Epoch 00004: loss improved from 0.10971 to 0.10570, saving model to model.h5

顺便说一句，重新定义模型，然后再按load_weight()绝对不起作用，因为save_weight()和load_weight()不会保存/加载优化器.

BTW, redefining the model followed by load_weight() definitely won't work, because save_weight() and load_weight() does not save/load the optimizer.

这篇关于Keras:如何保存模型并继续培训?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！