keras_lstm_multi_build_and_train.py

# build network 
model = Sequential()
# add number of layer specified
for layer in range(N_LAYERS):
    model.add(LSTM(N_LSTM_UNITS,input_shape=(N_TIMESTEPS,N_FEATURES),return_sequences=True))
model.add(Dense(N_FEATURES))
model.compile(loss='mae', optimizer='adam')
# print model summary
print(model.summary())
# reshape data for training
print(f'... reshaping data for training ...')
data_train = data_reshape_for_model(data,N_TIMESTEPS,N_FEATURES)
# begin training iterations
for i in range(N_TRAIN_ITERATIONS):
    print(f'... training iteration {i} ...')
    model = train(model,data_train,callbacks=[LossHistory()])
    
# get predictions on healthy data using final trained model
yhat = predict(model,data_train)