作者您好 请问如何添加新的预测模型

[Nilebaba]

作者，您好，我在您的代码的基础上添加新模型BiGRU
def build_model(self, trainset_shape, model_name='Keras model', model_file=None):
"""
Build Keras model, eg. 'GRU', 'LSTM', 'DNN', 'BPNN', 'CUDNNLSTM', 'CUDNNGRU', model = Sequential(), or load_model.
"""
if model_file is not None and os.path.exists(str(model_file)):
print('Load Keras model:', model_file)
return load_model(model_file) # load user's saving custom model
elif isinstance(self.KERAS_MODEL, Sequential): # if not load a model
return self.KERAS_MODEL
elif self.KERAS_MODEL == 'LSTM':
model = Sequential(name=model_name)
model.add(LSTM(self.units4, input_shape=(trainset_shape[1], trainset_shape[2]), recurrent_activation='sigmoid',
activation=self.activation, return_sequences=True))
model.add(Dropout(self.dropout))
model.add(LSTM(self.units2, recurrent_activation='sigmoid', activation=self.activation, return_sequences=True))
model.add(Dropout(self.dropout))
model.add(LSTM(self.units, recurrent_activation='sigmoid', activation=self.activation, return_sequences=False))
model.add(Dropout(self.dropout))
model.add(Dense(1, activation=self.activation))
model.compile(loss=self.opt_loss, optimizer=self.opt)
return model
elif self.KERAS_MODEL == 'GRU':
model = Sequential(name=model_name)
model.add(GRU(self.units4, input_shape=(trainset_shape[1], trainset_shape[2]), recurrent_activation='sigmoid',
reset_after=True, activation=self.activation, return_sequences=True))
model.add(Dropout(self.dropout))
model.add(GRU(self.units2, recurrent_activation='sigmoid', reset_after=True, activation=self.activation, return_sequences=True))
model.add(Dropout(self.dropout))
model.add(GRU(self.units, recurrent_activation='sigmoid', reset_after=True, activation=self.activation, return_sequences=False))
model.add(Dropout(self.dropout))
model.add(Dense(1, activation=self.activation))
model.compile(loss=self.opt_loss, optimizer=self.opt)
return model
elif self.KERAS_MODEL == 'DNN':
model = Sequential(name=model_name)
model.add(Flatten(input_shape=(trainset_shape[1], trainset_shape[2])))
for _ in range(8):
model.add(Dense(self.units4, activation=self.activation))
# model.add(BatchNormalization())
model.add(Dropout(self.dropout))
model.add(Dense(self.units2, activation=self.activation))
model.add(Dropout(self.dropout))
model.add(Dense(self.units, activation=self.activation))
model.add(Dropout(self.dropout))
model.add(Dense(1, activation=self.activation))
model.compile(loss=self.opt_loss, optimizer=self.opt)
return model
elif self.KERAS_MODEL == 'BPNN':
model = Sequential(name=model_name)
model.add(Dense(self.units4, input_shape=(trainset_shape[1], trainset_shape[2]), activation=self.activation))
model.add(Dropout(self.dropout))
model.add(Flatten())
model.add(Dense(1, activation=self.activation))
model.compile(loss=self.opt_loss, optimizer=self.opt)
return model
elif self.KERAS_MODEL == 'BiGRU':
model = Sequential(name=model_name)
model.add(Bidirectional(GRU(self.units4, input_shape=(trainset_shape[1], trainset_shape[2]), recurrent_activation='sigmoid',
reset_after=True, activation=self.activation, return_sequences=True)))
model.add(Dropout(self.dropout))
model.add(Flatten())
model.add(Dense(1, activation=self.activation))
model.compile(loss=self.opt_loss, optimizer=self.opt)
return model

但是并不能被识别到 出现如下报错：
Traceback (most recent call last):
File "c:\Users\GGBond\Desktop\CAN_LSTM\main.py", line 34, in
df_result = kr.hybrid_keras_predict(data=series, show=True, plot=True, save=True)
File "c:\Users\GGBond\Desktop\CAN_LSTM\CEEMDAN_LSTM_library\keras_predictor.py", line 599, in hybrid_keras_predict
df_result = self.keras_predict(data=df, show_model=show, **kwargs) # the ensemble method with matrix input
File "c:\Users\GGBond\Desktop\CAN_LSTM\CEEMDAN_LSTM_library\keras_predictor.py", line 327, in keras_predict
else: model = self.build_model(x_train.shape, data.name, model_file)
File "c:\Users\GGBond\Desktop\CAN_LSTM\CEEMDAN_LSTM_library\keras_predictor.py", line 247, in build_model
else: raise ValueError("%s is an invalid input for KERAS_MODEL! eg. 'GRU', 'LSTM', or model = Sequential()"%self.KERAS_MODEL)
ValueError: BIGRU is an invalid input for KERAS_MODEL! eg. 'GRU', 'LSTM', or model = Sequential()

请问在代码的哪个地方能修改使之可以添加新的模型呢

[FateMurphy]

1、你这样设置使用新的模型是可以的，可能是导入的包不是你修改后的。
2、你还可以尝试直接修改KERAS_MODEL这个全局变量。
例如：
model = Sequential(name='BiGRU')
model.add(Bidirectional(GRU(self.units4, input_shape=(trainset_shape[1], trainset_shape[2]), recurrent_activation='sigmoid',
reset_after=True, activation=self.activation, return_sequences=True)))
model.add(Dropout(self.dropout))
model.add(Flatten())
model.add(Dense(1, activation=self.activation))
model.compile(loss=self.opt_loss, optimizer=self.opt)

KERAS_MODEL = model
注意这种方式需要预先定义dropout，units4等变量

[Nilebaba]

1、你这样设置使用新的模型是可以的，可能是导入的包不是你修改后的。 2、你还可以尝试直接修改KERAS_MODEL这个全局变量。 例如： model = Sequential(name='BiGRU') model.add(Bidirectional(GRU(self.units4, input_shape=(trainset_shape[1], trainset_shape[2]), recurrent_activation='sigmoid', reset_after=True, activation=self.activation, return_sequences=True))) model.add(Dropout(self.dropout)) model.add(Flatten()) model.add(Dense(1, activation=self.activation)) model.compile(loss=self.opt_loss, optimizer=self.opt)

KERAS_MODEL = model 注意这种方式需要预先定义dropout，units4等变量

很感谢您的回答 我解决了问题 发现是要把模型名称都设置成大写的 中间有些转换 导致小写的检测不到

[Nilebaba]

1、你这样设置使用新的模型是可以的，可能是导入的包不是你修改后的。 2、你还可以尝试直接修改KERAS_MODEL这个全局变量。 例如： model = Sequential(name='BiGRU') model.add(Bidirectional(GRU(self.units4, input_shape=(trainset_shape[1], trainset_shape[2]), recurrent_activation='sigmoid', reset_after=True, activation=self.activation, return_sequences=True))) model.add(Dropout(self.dropout)) model.add(Flatten()) model.add(Dense(1, activation=self.activation)) model.compile(loss=self.opt_loss, optimizer=self.opt)

KERAS_MODEL = model 注意这种方式需要预先定义dropout，units4等变量

此外还有一个问题问您 就是添加TCN模型的时候 好像检测不到这个模型 我使用的是keras-tcn的api 然后就报错
ValueError: Unknown layer: TCN. Please ensure this object is passed to the custom_objects argument. See https://www.tensorflow.org/guide/keras/save_and_serialize#registering_the_custom_object for details.
我根据报错修改代码了 if self.PATH is not None: model = model = load_model(self.PATH+data.name)
添加了custom_objects={'TCN': TCN}
if self.PATH is not None: model = model = load_model(self.PATH+data.name,custom_objects={'TCN': TCN})
但是并不能预测 我个人感觉是没检测到正常的层 在您之前的代码中我看到有使用TCN 但又放弃了 请问有解决的办法吗

[FateMurphy]

因为之前在使用TCN没有得出正确的结果，所以没有启用tcn的包。tcn不是keras内置的模型所以无法导入，需要在安装好的添包keras_predictor文件中加form keras-tcn import TCN

[Nilebaba]

因为之前在使用TCN没有得出正确的结果，所以没有启用tcn的包。tcn不是keras内置的模型所以无法导入，需要在安装好的添包keras_predictor文件中加form keras-tcn import TCN

是的 我就是导入 但是结果就是根本没有预测 很奇怪 不知道怎么回事

[FateMurphy]

具体原因可能比较难找，可以先用这个简化版的试试
CEEMDAN-VMD-GRU

[Nilebaba]

具体原因可能比较难找，可以先用这个简化版的试试 CEEMDAN-VMD-GRU

好的 谢谢您 简版上上面我是测试成功的