"""
return K.sqrt(K.sum(K.square(y_pred - y_true), axis=-1))
skipRow = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
columnSkip = [1]
target = [12, 13]
trSet = DataSet("Z:\Matteo\Desktop\Machine Learning\ML-CUP18-TR.csv", ",",
ModeInput.TR_INPUT, target, None, skipRow, columnSkip)
trSet.restrict(0, 1)
list_of_input_arrays = list()
list_of_target_arrays = list()
for i in range(0, 1016):
elem = trSet.getInputs()[i]
list_of_input_arrays.append(elem.getInput())
list_of_target_arrays.append(elem.getTarget())
inputs = np.array(list_of_input_arrays)
targets = np.array(list_of_target_arrays)
print("TrSet completo")
lr = 2e-5
decay = 0
momentum = 0.9
regularization = 0
units = 300
model = Sequential()
trSet = DataSet("Z:\Matteo\Desktop\Machine Learning\ML-CUP18-TR.csv", ",",
ModeInput.TR_INPUT, target, None, skipRow, columnSkip)
trSet.restrict(0, 1)
learnRates = [0.001]
momRates = [0.7]
regRates = [0]
ValMaxs = [0.4]
HiddenUnitss = [50]
OutputUnitss = [1]
MaxIterss = [1000]
Tolerances = [0.03]
start = time.time()
e = cross_validation(1,
2,
ModeLearn.BATCH,
trSet.getInputs(),
outputF,
MEE,
learnRates,
momRates,
regRates,
ValMaxs,
HiddenUnitss,
OutputUnitss,
MaxIterss,
Tolerances,
start,
None,
errorVlFunct=MEE,
hiddenF=hiddenf)
stop = time.time()
columnSkip = [8]
targetPos = 1
datasetFileName = "monks-2.train"
logFileName = datasetFileName + ".log"
trainingSet = DataSet(datasetFileName, " ", ModeInput.ONE_OF_K_TR_INPUT, targetPos, domains, None, columnSkip)
learnRates = [0.1, 0.05, 0.01]
momRates = [0.7, 0.6, 0.5]
regRates = [0.1, 0.01, 0.005, 0]
ValMaxs = [0.7]
HiddenUnitss = [4, 5]
OutputUnitss = [1]
MaxIterss = [600]
Tolerances = [0.001]
start = time.time()
e = cross_validation(300, 2, ModeLearn.BATCH, trainingSet.getInputs(), f, None, learnRates, momRates, regRates, ValMaxs, HiddenUnitss, OutputUnitss, MaxIterss, Tolerances, start, None)
stop = time.time()
secdiff = stop - start
log = open(logFileName,'a')
log.write("***\n")
log.write("File: " + datasetFileName + ", con configurazioni di iperparametri seguenti \n")
log.write("learnRates: " + str(learnRates))
log.write('\n')
log.write("momRates: " + str(momRates))
log.write('\n')
log.write("regRates: " + str(regRates))
log.write('\n')
log.write("ValMaxs: " + str(ValMaxs))
log.write('\n')
log.write("HiddenUnitss: " + str(HiddenUnitss))
