保存和加载模型优化器状态 [英] Save and load model optimizer state

问题描述

我正在训练一组相当复杂的模型，我正在寻找一种方法来保存和加载模型优化器状态.训练器模型"由其他几种权重模型"的不同组合组成，其中一些具有共享权重，一些具有取决于训练器的冻结权重等.示例过于复杂，无法共享，但总之, 我在停止和开始训练时无法使用 model.save('model_file.h5') 和 keras.models.load_model('model_file.h5').

I have a set of fairly complicated models that I am training and I am looking for a way to save and load the model optimizer states. The "trainer models" consist of different combinations of several other "weight models", of which some have shared weights, some have frozen weights depending on the trainer, etc. It is a bit too complicated of an example to share, but in short, I am not able to use model.save('model_file.h5') and keras.models.load_model('model_file.h5') when stopping and starting my training.

如果训练已经完成，使用 model.load_weights('weight_file.h5') 可以很好地测试我的模型，但是如果我尝试使用这种方法继续训练模型，损失不会甚至接近返回到它的最后位置.我读过这是因为优化器状态没有使用这种方法保存，这是有道理的.但是，我需要一种方法来保存和加载训练器模型优化器的状态.好像 keras 曾经有一个 model.optimizer.get_sate() 和 model.optimizer.set_sate() 可以完成我所追求的，但这似乎不是情况不再如此(至少对于 Adam 优化器而言).目前的 Keras 是否有其他解决方案?

Using model.load_weights('weight_file.h5') works fine for testing my model if the training has finished, but if I attempt to continue training the model using this method, the loss does not come even close to returning to its last location. I have read that this is because the optimizer state is not saved using this method which makes sense. However, I need a method for saving and loading the states of the optimizers of my trainer models. It seems as though keras once had a model.optimizer.get_sate() and model.optimizer.set_sate() that would accomplish what I am after, but that does not seem to be the case anymore (at least for the Adam optimizer). Are there any other solutions with the current Keras?

推荐答案

您可以从 load_model 和 save_model 函数中提取重要的行.

You can extract the important lines from the load_model and save_model functions.

为了保存优化器状态，​​在save_model中:

For saving optimizer states, in save_model:

# Save optimizer weights.
symbolic_weights = getattr(model.optimizer, 'weights')
if symbolic_weights:
    optimizer_weights_group = f.create_group('optimizer_weights')
    weight_values = K.batch_get_value(symbolic_weights)

对于加载优化器状态，​​在load_model:

For loading optimizer states, in load_model:

# Set optimizer weights.
if 'optimizer_weights' in f:
    # Build train function (to get weight updates).
    if isinstance(model, Sequential):
        model.model._make_train_function()
    else:
        model._make_train_function()

    # ...

    try:
        model.optimizer.set_weights(optimizer_weight_values)

结合上面的几行，这是一个例子:

Combining the lines above, here's an example:

1. 首先拟合 5 个时期的模型.

X, y = np.random.rand(100, 50), np.random.randint(2, size=100)
x = Input((50,))
out = Dense(1, activation='sigmoid')(x)
model = Model(x, out)
model.compile(optimizer='adam', loss='binary_crossentropy')
model.fit(X, y, epochs=5)

Epoch 1/5
100/100 [==============================] - 0s 4ms/step - loss: 0.7716
Epoch 2/5
100/100 [==============================] - 0s 64us/step - loss: 0.7678
Epoch 3/5
100/100 [==============================] - 0s 82us/step - loss: 0.7665
Epoch 4/5
100/100 [==============================] - 0s 56us/step - loss: 0.7647
Epoch 5/5
100/100 [==============================] - 0s 76us/step - loss: 0.7638

1. 现在保存权重和优化器状态.

model.save_weights('weights.h5')
symbolic_weights = getattr(model.optimizer, 'weights')
weight_values = K.batch_get_value(symbolic_weights)
with open('optimizer.pkl', 'wb') as f:
    pickle.dump(weight_values, f)

1. 在另一个 Python 会话中重建模型，并加载权重.

x = Input((50,))
out = Dense(1, activation='sigmoid')(x)
model = Model(x, out)
model.compile(optimizer='adam', loss='binary_crossentropy')

model.load_weights('weights.h5')
model._make_train_function()
with open('optimizer.pkl', 'rb') as f:
    weight_values = pickle.load(f)
model.optimizer.set_weights(weight_values)

1. 继续模型训练.

model.fit(X, y, epochs=5)

Epoch 1/5
100/100 [==============================] - 0s 674us/step - loss: 0.7629
Epoch 2/5
100/100 [==============================] - 0s 49us/step - loss: 0.7617
Epoch 3/5
100/100 [==============================] - 0s 49us/step - loss: 0.7611
Epoch 4/5
100/100 [==============================] - 0s 55us/step - loss: 0.7601
Epoch 5/5
100/100 [==============================] - 0s 49us/step - loss: 0.7594

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