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)

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!