keras-anomaly-detection 代码分析——本质上就是SAE、LSTM时间序列预测

keras-anomaly-detection

Anomaly detection implemented in Keras

The source codes of the recurrent, convolutional and feedforward networks auto-encoders for anomaly detection can be found in keras_anomaly_detection/library/convolutional.py and keras_anomaly_detection/library/recurrent.py and keras_anomaly_detection/library/feedforward.py

The the anomaly detection is implemented using auto-encoder with convolutional, feedforward, and recurrent networks and can be applied to:

  • timeseries data to detect timeseries time windows that have anomaly pattern
  • structured data (i.e., tabular data) to detect anomaly in data records
    • Conv1DAutoEncoder in keras_anomaly_detection/library/convolutional.py
    • FeedforwardAutoEncoder in keras_anomaly_detection/library/feedforward.py
       
      看LSTM的模型吧:
          def create_model(time_window_size, metric):
      model = Sequential()
      model.add(LSTM(units=128, input_shape=(time_window_size, 1), return_sequences=False)) model.add(Dense(units=time_window_size, activation='linear')) model.compile(optimizer='adam', loss='mean_squared_error', metrics=[metric])
      print(model.summary())
      return model

      再看feedforward的模型:

          def create_model(self, input_dim):
      encoding_dim = 14
      input_layer = Input(shape=(input_dim,)) encoder = Dense(encoding_dim, activation="tanh",
      activity_regularizer=regularizers.l1(10e-5))(input_layer)
      encoder = Dense(encoding_dim // 2, activation="relu")(encoder) decoder = Dense(encoding_dim // 2, activation='tanh')(encoder)
      decoder = Dense(input_dim, activation='relu')(decoder) model = Model(inputs=input_layer, outputs=decoder)
      model.compile(optimizer='adam',
      loss='mean_squared_error',
      metrics=['accuracy'])

      CNN的:

          def create_model(time_window_size, metric):
      model = Sequential()
      model.add(Conv1D(filters=256, kernel_size=5, padding='same', activation='relu',
      input_shape=(time_window_size, 1)))
      model.add(GlobalMaxPool1D()) model.add(Dense(units=time_window_size, activation='linear')) model.compile(optimizer='adam', loss='mean_squared_error', metrics=[metric])
      print(model.summary())
      return model

      都是将输出设置成自己,异常点就是查看偏离那90%的预测error较大的点。

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