凯拉斯：如何保存模型并继续训练？

Question

我有一个模型，我已经训练了40个时代。我为每个时代保留了检查点，还用model.save()保存了模型。训练代码是

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)

然而，当装载模型并再次训练时，它会像以前没有训练过一样从头开始。输球不是从上次训练开始的。

让我困惑的是，当我用重新定义的模型结构和load_weight加载模型时，model.predict()工作得很好。因此，我相信模型权重是加载的。

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，当我继续训练

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)

损失和初始状态一样高。

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-models/https://github.com/fchollet/keras/issues/1872

但都不管用。有人能帮我吗？谢谢。

更新

Loading a trained Keras model and continue training

我试过了

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

它起作用了。但当我关闭python时，重新打开并再次load_model。它失败了。损失和初始状态一样高。

更新

我试过于洋的示例代码。它起作用了。但回到我的代码，我还是失败了。 这是最初的训练。第二个纪元应该以损失=3.1开始。

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并重新打开它。使用model = load_model("LPT-00-3.0510.h5")加载模型，然后使用

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

Answer 1

以下是kera保存模型的官方文档：

https://keras.io/getting-started/faq/#how-can-i-save-a-keras-model

在this post中，作者提供了两个将模型保存和加载到文件的示例：

• JSON格式。
• 山药孔。

Answer 2

我将代码与此示例http://machinelearningmastery.com/text-generation-lstm-recurrent-neural-networks-python-keras/ 小心地一行一行地封锁，然后再跑。一整天之后，我终于发现了问题所在。

在进行char int映射时，我使用

# title_str_reduced is a string
chars = list(set(title_str_reduced))
# make char to int index mapping
char2int = {}
for i in range(len(chars)):
    char2int[chars[i]] = i    

集合是无序的数据结构。在python中，当一个集合被转换成一个有序的列表时，这个顺序是随机给定的。因此，每当我重新打开python时，我的char2int字典都是随机的。 我通过添加排序（）

chars = sorted(list(set(title_str_reduced)))

这将强制转换为固定顺序。

Answer 3

由于很难弄清楚问题所在，我从您的代码中创建了一个玩具示例，它看起来工作正常。

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("model.h5")
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，损失继续减小。（重启python也没有问题）

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

顺便说一下，重新定义后面跟着load_weight()的模型肯定行不通，因为save_weight()和load_weight()不会保存/加载优化器。