validate_generator

`validate_generator`是用于对训练好的模型进行验证的函数。与`fit_generator`类似，它也使用了生成器来加载数据。`validate_generator`会根据给定的验证集生成一批数据，并将其输入到训练好的模型中进行验证。具体来说，它会调用模型的`predict`方法来得到模型对验证数据的预测结果，然后根据预测结果和真实标签计算模型的性能指标，如准确率、精确率、召回率等。最后，它会返回一个包含所有性能指标的字典。与`fit_generator`类似，`validate_generator`也支持使用`steps`参数来指定验证过程中要处理的批次数。

相关问题

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)

Training Begin e:/B19060708share/ENDING/spectrum_anomaly_detection-master/LSTM.py:177: UserWarning: `model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators. steps_per_epoch=t_len, callbacks=callbacks, validation_data=valid_generator, validation_steps=v_len) 2023-05-16 00:50:29.234124: W tensorflow/c/c_api.cc:291] Operation '{name:'batch_normalization_1/moving_variance/Assign' id:578 op device:{requested: '', assigned: ''} def:{{{node batch_normalization_1/moving_variance/Assign}} = AssignVariableOp[_has_manual_control_dependencies=true, dtype=DT_FLOAT, validate_shape=false](batch_normalization_1/moving_variance, batch_normalization_1/moving_variance/Initializer/ones)}}' was changed by setting attribute after it was run by a session. This mutation will have no effect, and will trigger an error in the future. Either don't modify nodes after running them or create a new session. 我在训练LSTM模型，但是运行文件后终端一直卡在前面这句话，这是运行失败了吗？怎么知道我的模型有没有在训练？

这个警告不是训练失败的原因，只是TensorFlow给出的一个提示。如果你想看到训练过程中的输出，你可以在代码中添加打印语句或者使用TensorBoard来可视化训练过程。在训练过程中，你应该能够看到每个epoch的损失值和准确率等信息。如果训练成功，你会看到损失值逐渐下降，准确率逐渐提高。如果训练失败，你可以看到损失值无法下降或者准确率一直很低。