INNER CODE UNIT · Python
ModelLSTM
adminho/trading-stock-thailand · DeepQ_trade.py:60
class ModelLSTM:
def __init__(self, num_features, num_output):
windows = 5 # windows slide
self.num_sequence = windows
self.model = self.buil_model(self.num_seq, num_features, num_output)
# This is neural network model for the the Q-function
def builModel(self, num_seq, num_features, num_output):
model = Sequential()
# I used GRU network
model.add(GRU(input_shape=(num_seq, num_features), units=20, return_sequences=True))
model.add(BatchNormalization(trainable = True))
model.add(GRU(units=20, return_sequences=False))
model.add(BatchNormalization(trainable = True))
model.add(Dense(units=20, activation='relu'))
model.add(BatchNormalization(trainable = True))
model.add(Dense(num_output, activation='linear'))