LSTM Autoencoder using Keras

lstm_autoencoder.py

from keras.layers import LSTM, Dense, RepeatVector, TimeDistributed
from keras.models import Sequential

class LSTM_Autoencoder:
  def __init__(self, optimizer='adam', loss='mse'):
    self.optimizer = optimizer
    self.loss = loss
    self.n_features = 1
    
  def build_model(self):
    timesteps = self.timesteps
    n_features = self.n_features
    model = Sequential()
    
    # Encoder
    model.add(LSTM(timesteps, activation='relu', input_shape=(timesteps, n_features), return_sequences=True))
    model.add(LSTM(16, activation='relu', return_sequences=True))
    model.add(LSTM(1, activation='relu'))
    model.add(RepeatVector(timesteps))
    
    # Decoder
    model.add(LSTM(timesteps, activation='relu', return_sequences=True))
    model.add(LSTM(16, activation='relu', return_sequences=True))
    model.add(TimeDistributed(Dense(n_features)))
    
    model.compile(optimizer=self.optimizer, loss=self.loss)
    model.summary()
    self.model = model
    
  def fit(self, X, epochs=3, batch_size=32):
    self.timesteps = X.shape[1]
    self.build_model()
    
    input_X = np.expand_dims(X, axis=2)
    self.model.fit(input_X, input_X, epochs=epochs, batch_size=batch_size)
    
  def predict(self, X):
    input_X = np.expand_dims(X, axis=2)
    output_X = self.model.predict(input_X)
    reconstruction = np.squeeze(output_X)
    return np.linalg.norm(X - reconstruction, axis=-1)
  
  def plot(self, scores, timeseries, threshold=0.95):
    sorted_scores = sorted(scores)
    threshold_score = sorted_scores[round(len(scores) * threshold)]
    
    plt.title("Reconstruction Error")
    plt.plot(scores)
    plt.plot([threshold_score]*len(scores), c='r')
    plt.show()
    
    anomalous = np.where(scores > threshold_score)
    normal = np.where(scores <= threshold_score)
    
    plt.title("Anomalies")
    plt.scatter(normal, timeseries[normal][:,-1], s=3)
    plt.scatter(anomalous, timeseries[anomalous][:,-1], s=5, c='r')
    plt.show()
    
lstm_autoencoder = LSTM_Autoencoder(optimizer='adam', loss='mse')
lstm_autoencoder.fit(normal_timeseries, epochs=3, batch_size=32)
scores = lstm_autoencoder.predict(test_timeseries)
lstm_autoencoder.plot(scores, test_timeseries, threshold=0.95)

Can you tell me what time series data you are using with your model? Thanks!

Can you tell me what time series data you are using with your model? Thanks!

Hi, you may refer to my repository here where I used the Numenta Anomaly Benchmark (machine_temperature_system_failure.csv), for temperature sensor data of an internal component of a large, industrial machine.

Hi, I want to use LSTM-Autoencoder to compress input data (dimension reduction), do you know how I can retrieve the compressed sequence (time-series)?
Thank you so much in advance.

Hi @miladgoodarzi, you can consider iterating through model.layers. Reference: https://stackoverflow.com/questions/50151157/keras-how-to-get-layer-index-when-already-know-layer-name. Cheers!