r, rmse, _ = learnLib.assess_model(model, X_val, Y_val)
models[args] = r, rmse
print("Model r: ", r)
print("Model rmse: ", rmse)
if rmse < prevLoss:
prevLoss = rmse
maxModel = model
while kb.kbhit():
try:
if "q" in kb.getch():
print("quiting due to user pressing q")
stop = True
except UnicodeDecodeError:
pass
if stop:
break
del X_train
X_test, Y_test = ns.getTestData()
X_test = sliceToTimeSeries(X_test)
learnLib.printModels(models)
r, rmse, preds = learnLib.assess_model(maxModel, X_test, Y_test)
predicted_bpm = np.array(list(map(ns.unnormalize_bpm, preds)))
print("Model r: ", r)
print("Model rmse: ", rmse)
code.interact(local=locals())
# most recent loss hist.history["loss"][-1]
r, rmse, _ = learnLib.assess_model(model, X_validate, Y_validate)
models[args] = r,rmse
print("Model r: ", r)
print("Model rmse: ", rmse)
if rmse < prevLoss:
prevLoss = rmse
maxModel = model
while kb.kbhit():
try:
if "q" in kb.getch():
print("quiting due to user pressing q")
stop = True
except UnicodeDecodeError:
pass
if stop:
break
del X_train
X_test, Y_test = ns.getTestData()
learnLib.printModels(models)
r, rmse, preds = learnLib.assess_model(maxModel, X_test, Y_test)
predicted_bpm = np.array(list(map(ns.unnormalize_bpm, preds)))
print("Model r: ", r)
print("Model rmse: ", rmse)
code.interact(local=locals())
