Keras-anomaly-detection code analysis-essentially SAE and lstm time series prediction

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 anomaly detection is implemented using auto-Encoder with convolutional, feedforward, and recurrent networks and can be applied:

• 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:
        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

    Let's look at the feedforward model:

        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:

        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

    Set the output to your own. The exception points are the points with a larger predicted error deviation of the 90%.

Keras-anomaly-detection code analysis-essentially SAE and lstm time series prediction