CODEWITHSUNDEEP
kerasdeep-learninglstmlstm-stateful
ab.sharma
ab.sharma

Reputation: 190

LSTM Autoencoder producing poor results in test data

I'm applying LSTM autoencoder for anomaly detection. Since anomaly data are very few as compared to normal data, only normal instances are used for the training. Testing data consists of both anomalies and normal instances. During the training, the model loss seems good. However, in the test the data the model produces poor accuracy. i.e. anomaly and normal points are not well separated.
The snippet of my code is below:

.............
.............
X_train = X_train.reshape(X_train.shape[0], lookback, n_features)
X_valid = X_valid.reshape(X_valid.shape[0], lookback, n_features)
X_test = X_test.reshape(X_test.shape[0], lookback, n_features)
.....................
......................
N = 1000
batch = 1000
lr = 0.0001
timesteps = 3
encoding_dim = int(n_features/2)
lstm_model = Sequential()
lstm_model.add(LSTM(N, activation='relu', input_shape=(timesteps, n_features), return_sequences=True))
lstm_model.add(LSTM(encoding_dim, activation='relu', return_sequences=False))
lstm_model.add(RepeatVector(timesteps))
# Decoder
lstm_model.add(LSTM(timesteps, activation='relu', return_sequences=True))
lstm_model.add(LSTM(encoding_dim, activation='relu', return_sequences=True))
lstm_model.add(TimeDistributed(Dense(n_features)))
lstm_model.summary()
adam = optimizers.Adam(lr)
lstm_model.compile(loss='mse', optimizer=adam)

cp = ModelCheckpoint(filepath="lstm_classifier.h5",
                     save_best_only=True,
                     verbose=0)

tb = TensorBoard(log_dir='./logs',
                 histogram_freq=0,
                 write_graph=True,
                 write_images=True)

lstm_model_history = lstm_model.fit(X_train, X_train,
                                    epochs=epochs,
                                    batch_size=batch,
                                    shuffle=False,
                                    verbose=1,
                                    validation_data=(X_valid, X_valid),
                                    callbacks=[cp, tb]).history


.........................
test_x_predictions = lstm_model.predict(X_test)
mse = np.mean(np.power(preprocess_data.flatten(X_test) - preprocess_data.flatten(test_x_predictions), 2), axis=1)

error_df = pd.DataFrame({'Reconstruction_error': mse,
                         'True_class': y_test})

# Confusion Matrix
pred_y = [1 if e > threshold else 0 for e in error_df.Reconstruction_error.values]
conf_matrix = confusion_matrix(error_df.True_class, pred_y)

plt.figure(figsize=(5, 5))
sns.heatmap(conf_matrix, xticklabels=LABELS, yticklabels=LABELS, annot=True, fmt="d")
plt.title("Confusion matrix")
plt.ylabel('True class')
plt.xlabel('Predicted class')
plt.show()

Please suggest what can be done in the model to improve the accuracy.

Upvotes: 0

Views: 1138

Answers (1)

aksoym
aksoym

Reputation: 91

If your model is not performing good on the test set I would make sure to check certain things;

  • Training set is not contaminated with anomalies or any information from the test set. If you use scaling, make sure you did not fit the scaler to training and test set combined.
  • Based on my experience; if an autoencoder cannot discriminate well enough on the test data but has low training loss, provided your training set is pure, it means that the autoencoder did learn about the underlying details of the training set but not about the generalized idea.
  • Your threshold value might be off and you may need to come up with a better thresholding procedure. One example can be found here: https://dl.acm.org/citation.cfm?doid=3219819.3219845

If the problem is 2nd one, the solution is to increase generalization. With autoencoders, one of the most efficient generalization tool is the dimension of the bottleneck. Again based on my experience with anomaly detection in flight radar data; lowering the bottleneck dimension significantly increased my multi-class classification accuracy. I was using 14 features with an encoding_dim of 7, but encoding_dim of 4 provided even better results. The value of the training loss was not important in my case because I was only comparing reconstruction errors, but since you are making a classification with a threshold value of RE, a more robust thresholding may be used to improve accuracy, just as in the paper I've shared.

Upvotes: 1

Related Questions