def getModelCNNLSTM(sequence, window_size=3, n_features=1, epochs=100):
model = Sequential()
model.add(
Conv1D(filters=24,
kernel_size=2,
activation='relu',
input_shape=(window_size, n_features)))
model.add(MaxPooling1D(pool_size=2))
model.add(
LSTM(units=10,
activation='tanh',
recurrent_activation='hard_sigmoid',
use_bias=True,
kernel_initializer='glorot_uniform'))
model.add(Dense(50, activation='relu'))
model.add(Dense(1))
model.compile(optimizer='adam', loss='mse')
X, y = rollingwindow.split_sequence(sequence, window_size)
X = X.reshape((X.shape[0], X.shape[1], n_features))
es = EarlyStopping(monitor='val_loss',
min_delta=0.001,
patience=0,
verbose=0,
mode='auto',
baseline=None,
restore_best_weights=False)
model.fit(X, y, epochs=epochs, callbacks=[es])
return model
def getModelCNNWindow24(sequence,
window_size=int(24),
activation='relu',
n_features=1,
epochs=200):
kernel_size = [2, 2]
filters = [6, 6]
pool_size = [2, 2]
model = Sequential()
model.add(
Conv1D(filters=filters[0],
kernel_size=kernel_size[0],
activation='relu',
input_shape=(window_size, n_features)))
model.add(MaxPooling1D(pool_size=pool_size[0]))
model.add(Activation('relu'))
model.add(
Conv1D(filters=filters[1],
kernel_size=kernel_size[1],
activation='relu',
input_shape=(window_size, n_features)))
model.add(MaxPooling1D(pool_size=pool_size[1]))
model.add(Activation('relu'))
model.add(Flatten())
model.add(Dense(50, activation='relu'))
model.add(Dense(1))
model.compile(optimizer='adam', loss='mse')
X, y = rollingwindow.split_sequence(sequence, window_size)
X = X.reshape((X.shape[0], X.shape[1], n_features))
es = EarlyStopping(monitor='val_loss',
min_delta=0,
patience=0,
verbose=0,
mode='auto',
baseline=None,
restore_best_weights=False)
model.fit(X, y, epochs=epochs, callbacks=[es])
return model
def main():
if len(sys.argv) != 5:
quit("ERROR:\nParameters: <direction> <interfaceID> <algorithm> <length_series>")
inOrOut = sys.argv[1]#options: "in", "out"
interfaceid = int(sys.argv[2])#option: whatever is in your orion database for interfaceids
algorithm = sys.argv[3]#options: cnn3, cnn24, lstm, lstmcnn
length_series = int(sys.argv[4])#option: integer(above 400 in length)
ins, insTrains, outs, outsTrains = orionconnection.getInterface(length_series, interfaceid)
sequence = ins
trains = insTrains
n_features = 1
window_size = 3
if inOrOut == "out":
sequence = outs
trains = outsTrains
if algorithm == "cnn3":
model = forecaster.getModelCNNWindow3(sequence)
elif algorithm == "cnn24":
window_size = 24
model = forecaster.getModelCNNWindow24(sequence, window_size, n_features)
elif algorithm == "lstm":
model = forecaster.getModelLSTM(sequence)
elif algorithm == "lstmcnn":
model = forecaster.getModelLSTM(sequence)
filename, weightfile = createFilenames(inOrOut, interfaceid, algorithm)
forecaster.saveModel(model, filename, weightfile)
X, y = rollingwindow.split_sequence(trains, window_size)
X = X.reshape((X.shape[0], X.shape[1], n_features))
print(model.evaluate(X, y))