我如何在每个时期保存检查点并加载随机保存的检查点以继续训练

Question

您能否帮我编写代码以：在每个时期保存模型（架构和权重），以及如何从第 5 个检查点继续训练我的模型，例如从 1 到 25 的训练时期而不进行检查点（第 5 个模型 I已保存）。

classifier = Sequential()

classifier.add(Conv2D(6, (3, 3), input_shape = (30, 30, 3), data_format="channels_last", activation = 'relu'))
classifier.add(MaxPooling2D(pool_size = (2, 2)))

classifier.add(Conv2D(6, (3, 3), activation = 'relu'))
classifier.add(MaxPooling2D(pool_size = (2, 2)))

classifier.add(Flatten())

classifier.add(Dense(units = 128, activation = 'relu'))
classifier.add(Dense(units = 64, activation = 'relu'))
classifier.add(Dense(units = 1, activation = 'sigmoid'))

opt = Adam(learning_rate = 0.001, beta_1 = 0.9, beta_2 = 0.999, epsilon = 1e-08, decay = 0.0)
classifier.compile(optimizer = opt, loss = 'binary_crossentropy', metrics = ['accuracy', precision, recall, fmeasure])

from keras.preprocessing.image import ImageDataGenerator

train_datagen = ImageDataGenerator(rescale = 1./255,
                                   horizontal_flip = True,
                                   vertical_flip = True,
                                   rotation_range = 180)

validation_datagen = ImageDataGenerator(rescale = 1./255)

training_set = train_datagen.flow_from_directory('/home/dataset/training_set',
                                                 target_size = (30, 30),
                                                 batch_size = 32,
                                                 class_mode = 'binary')

validation_set = validation_datagen.flow_from_directory('/home/dataset/validation_set',
                                                        target_size = (30, 30),
                                                        batch_size = 32,
                                                        class_mode = 'binary')

history = classifier.fit_generator(training_set,
                                   steps_per_epoch = 208170,
                                   epochs = 15,
                                   validation_data = validation_set,
                                   validation_steps = 89140)

Answer 1

我假设你的意思是你想在每个 epoch 之后保存你的模型和权重，然后在稍后的阶段，加载第五个 epoch 之后保存的模型和权重。

您通常可以像这样在 TensorFlow 中使用 SaveModel 格式：

classifier.save()

这将保存架构、权重、有关优化器的信息以及您在 compile() 中设置的配置

由于您使用的是 fit_generator，因此您可以使用 ModelCheckpoint() 来保存您的模型，如下所示：

from keras.callbacks import ModelCheckpoint

checkpoint = ModelCheckpoint(path_to_save_to, save_freq = 'epoch', 
                             save_weights_only = False)

history = classifier.fit_generator(training_set,
                                   steps_per_epoch = 208170,
                                   epochs = 15,
                                   validation_data = validation_set,
                                   validation_steps = 89140,
                                    callbacks = [checkpoint])

您可以格式化路径，以便它使用像 path_name + '-{epoch:02d}-{val_loss:.2f}.h5' 这样的纪元/损失详细信息保存模型

要加载第五个检查点，请执行以下操作：

from keras.models import load_model
classifier = load_model(path_to_fifth_checkpoint)