INNER CODE UNIT · Python
optm
adminho/trading-stock-thailand · LSTM_predict_trend.py:68
optm=RMSprop(lr=0.001, rho=0.9, epsilon=1e-08, decay=0.0001)
start = time.time()
# algorithim to train models use
# compute loss with function: binary_crossentropy or loss crossentropy (Also known as logloss)
model.compile(optimizer=optm,
loss='binary_crossentropy',
metrics=['accuracy'])
sec = datetime.timedelta(seconds=int(time.time() - start))
print ("Compilation Time : ", str(sec))
return model
def trainModel(symbolName, Xtrain, Ytrain, Xtest, Ytest, epochs):
history = his.TrainingHistory()
h5_file = os.path.join(MODEL_PATH,'%s_weights.h5' % (symbolName))
json_file = os.path.join(MODEL_PATH,'%s_structure.json' % (symbolName))
global_start_time = time.time()