callbacks_list = [tb_call, lr_scheduler_call, lr_plateau_call, chkpt_call]
时间: 2023-12-12 08:03:51 浏览: 35
这是一个回调函数列表,用于在训练神经网络模型时执行一些特定的操作。 回调函数可以在每个训练批次或每个训练周期之后执行,以监控模型的性能并进行调整。
在这个列表中,`tb_call` 是用于在TensorBoard中可视化训练进度的回调函数,`lr_scheduler_call` 是用于调整学习率的回调函数,`lr_plateau_call` 是用于在验证集的性能停止提升时降低学习率的回调函数,`chkpt_call` 是用于保存模型检查点的回调函数。
这些回调函数可以根据需要进行修改或替换,以执行特定的操作或监控模型的不同方面。
相关问题
reduce_lr = LearningRateScheduler(scheduler)
Sorry, as an AI language model, I cannot provide the complete code for this question as the scheduler function needs to be defined first. However, I can explain what the code does.
The code creates a "reduce_lr" object which is an instance of the LearningRateScheduler class. This object is used to reduce the learning rate during training of a neural network model. The "scheduler" function is passed as an argument to the LearningRateScheduler constructor. This function takes the current epoch number and current learning rate as inputs and returns a new learning rate to be used for the next epoch.
The LearningRateScheduler class is part of the keras.callbacks module which provides a way to monitor and control the training process of a neural network model. The reduce_lr object can be passed as an argument to the "callbacks" parameter when compiling a keras model.
filepath = "model.h5" checkpoint = ModelCheckpoint(filepath, monitor='val_top_3_accuracy', verbose=1, save_best_only=True, mode='max') reduce_lr = ReduceLROnPlateau(monitor='val_top_3_accuracy', factor=0.5, patience=2, verbose=1, mode='max', min_lr=0.00001) callbacks_list = [checkpoint, reduce_lr] #datagen.flow(x_train,y_train, batch_size=batch_size) history = model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size, target_size=(100, 125)), class_weight=class_weights, validation_data=(x_validate,y_validate),steps_per_epoch=x_train.shape[0] // batch_size, epochs=10, verbose=1, callbacks=callbacks_list)这一段代码报错TypeError: flow() got an unexpected keyword argument 'target_size'
很抱歉,我之前的回答并不正确,`target_size`参数适用于`ImageDataGenerator`,而不是`DataGenerator`。对于`DataGenerator`,你可以在实例化时指定输入数据的形状,例如:
```
train_generator = DataGenerator(x_train, y_train, batch_size=batch_size, input_shape=(100, 125, 1))
val_generator = DataGenerator(x_validate, y_validate, batch_size=batch_size, input_shape=(100, 125, 1))
```
这里我假设输入数据的形状是`(100, 125, 1)`,并使用`input_shape`参数在实例化`DataGenerator`时指定。请注意,这里的`batch_size`应该与你在`fit_generator()`中使用的`batch_size`一致。
然后,将`fit_generator()`中的生成器修改为`train_generator`和`val_generator`:
```
history = model.fit_generator(train_generator,
class_weight=class_weights,
validation_data=val_generator,
steps_per_epoch=x_train.shape[0] // batch_size,
epochs=10,
verbose=1,
callbacks=callbacks_list)
```
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