每次训练后，并没有训练好的模型文件hdf5存在指定位置中，是缺少什么保存模型的代码我没写么？新手上路，对比了很多训练模型的案例看不出自己少哪部分代码，求指点！保存记录loss的CSV文件也没能成功保存到文件夹下，很苦恼
【训练模型部分代码】
model_path = "bone_age_model_inception.hdf5"
checkpoint = ModelCheckpoint(model_path, monitor='val_loss', verbose=1, save_best_only=True, mode='min',
save_weights_only=True)
optim = optimizers.Nadam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, schedule_decay=0.0003)
reduceLROnPlat = ReduceLROnPlateau(monitor='val_loss', factor=0.8, patience=3, verbose=1, mode='auto', epsilon=0.0001,
cooldown=5, min_lr=0.0006)
early = EarlyStopping(monitor="val_loss", mode="min",
patience=10) # probably needs to be more patient, but kaggle time is limited
callbacks_list = [checkpoint, early, reduceLROnPlat]
bone_age_model_history = bone_age_model.fit_generator(generator=train_flow,
steps_per_epoch=STEP_SIZE_TRAIN,
validation_data=valid_flow,
validation_steps=STEP_SIZE_VALID,
epochs=EPOCHS,
callbacks=callbacks_list)
loss_history = bone_age_model_history.history['loss'] # 保存训练的loss
history_df = pd.DataFrame.from_dict(bone_age_model_history.history)
history_df.to_csv('./csv/incption_v3_loss_history1r.csv') # 这个csv文件也没有成功保存
print("Training complete !!!\n")
bone_age_model.save('bone_age_model_inception.hdf5') # 保存HDF5模型文件
bone_age_model.load_weights("bone_age_model_inception.hdf5") #加载模型
test_X, test_Y = next(test_flow)
pred_Y = mu + sigma * bone_age_model.predict(x=test_X, batch_size=16, verbose=1)
运行结果及报错内容
运行都正常，没报错，就是hdf5不知道怎么保存到目录里，明明也用了save函数了
我的解答思路和尝试过的方法
尝试过把model_path换成绝对路径，还是无济于事
