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
- LstmAutoEncoder in keras_anomaly_detection/library/recurrent.py
- Conv1DAutoEncoder in keras_anomaly_detection/library/convolutional.py
- CnnLstmAutoEncoder in keras_anomaly_detection/library/recurrent.py
- BidirectionalLstmAutoEncoder in keras_anomaly_detection/library/recurrent.py

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。

巴特西

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

keras-anomaly-detection

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