Question

您能否在代码上帮助我：保存每个时期的模型（体系结构和权重），以及如何继续从第5个检查点训练我的模型，例如从1到25的训练时期而不建立检查点（第5个模型，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

我假设您的意思是要在每个时期之后保存模型和权重，然后在以后的阶段中加载在第五个时期之后保存的模型和权重。

您通常可以在TensorFlow中使用SaveModel格式，如下所示：

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，因此可以使用ModelCheckpoint()这样保存模型：

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])

您可以设置路径的格式，以便保存带有{/ {1}}这样的历元/损失详细信息的模型

要加载第五个检查点，请执行以下操作：

path_name + '-{epoch:02d}-{val_loss:.2f}.h5'

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

1 个答案: