train.py

#for data loading
import etl
#for machine learning
import keras

#load data
dl = etl.ETL()

#build model
def build_network(layers):
    model = Sequential()

    model.add(LSTM(
        input_dim=layers[0],
        output_dim=layers[1],
        return_sequences=True))
    model.add(Dropout(0.2))

    model.add(LSTM(
        layers[2],
        return_sequences=False))
    model.add(Dropout(0.2))

    model.add(Dense(
        output_dim=layers[3]))
    model.add(Activation("tanh"))

    start = time.time()
    model.compile(
        loss=configs['model']['loss_function'],
        optimizer=configs['model']['optimiser_function'])

    print("> Compilation Time : ", time.time() - start)
    return model
 
#generate training data
data_gen_train = dl.generate_clean_data(
    configs['data']['filename_clean'],
    batch_size=configs['data']['batch_size']
)

with h5py.File(configs['data']['filename_clean'], 'r') as hf:
    nrows = hf['x'].shape[0]
    ncols = hf['x'].shape[2]
    
#train model
model = lstm.build_network([ncols, 150, 150, 1])
model.fit_generator(
        data_gen_train,
        steps_per_epoch=steps_per_epoch,
        epochs=configs['model']['epochs']
    )

#generate testing data
data_gen_test = dl.generate_clean_data(
    configs['data']['filename_clean'],
    batch_size=configs['data']['batch_size'],
    start_index=ntrain
)
#make predictions
predictions = model.predict_generator(
    generator_strip_xy(data_gen_test, true_values),
    steps=steps_test
  
#plot results
plot_results(predictions[:800], true_values[:800])
)