<p>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。</p>
<pre><code>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)
</code></pre>
<p>模型加载后,损失继续减小。(重启python也没有问题)</p>
<pre><code>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
</code></pre>
<p>顺便说一下,重新定义后面跟着<code>load_weight()</code>的模型肯定行不通,因为<code>save_weight()</code>和<code>load_weight()</code>不会保存/加载优化器。</p>