scaler = preprocessing.StandardScaler().fit(Xtrain)
Xtrain = scaler.transform(Xtrain)
Xtest = scaler.transform(Xtest)
#################################################################
#################### Neural Net Using #########################
#################################################################
nH = 80
mySLFN = SLFN.CSLFN(nH=nH, fh="tanh", fo="tanh", errFunc="EXP")
mySLFN.set_Train(Xtrain, Ytrain)
mySLFN.set_Val(Xtest, Ytest)
# WEIGHT INITIALIZATION
initDistrib = paC.Init_Distrib("default",
["uniform", -1, 1]) # Define initialization
#initDistrib = paC.Init_Distrib("default", ["normal",0,1])# Define initialization
mySLFN.set_initDistrib(initDistrib) # Set the initialization
mySLFN.init_Weights() # Initialize
BP_F = 0
ELM_F = 1
BMBP_F = 0
FT_F = 0
# DEFINE TRAINING ALGORITHM
if (BP_F == 1):
# Step and number of epochs
trainingAlg = paC.Training_Alg("BP", [1000, 0.0003])
nP = 10
################################################################
#################### NEURAL NETWORM PARAMETERS ##################
#################################################################
nH = 20
fh_name = "tanh"
fo_name = "tanh"
errFunc = "MSE"
visual = 0
######## WEIGHT INITIALIZATION ########
#initDistrib = paC.Init_Distrib("default", ["uniform",-1,1])
#initDistrib = paC.Init_Distrib("default", ["normal",0,1])
initDistrib = paC.Init_Distrib("deepLearning", ["DL1"])
######## STOP CRITERION ########
stopCriterion = paC.Stop_Criterion("Nmax", [])
######## REGULARIZATION ########
regularization = paC.Regularization("NoL2", [0.000005])
######## TRAINING ALGORITHM ########
# Parameters of the algorithms
N_epochs = 200
Learning_Rate = 0.01
BatchSize = 2
######## CLUSTER PARAMETERS !!!!! ########
""" If we execute in da cluuster (spanish u) we overwritte the parameters """
fh_name = "tanh"
fo_name = "linear"
fg_name = "linear"
errFunc = "MSE"
Nruns = 1
CV = 1
InitRandomSeed = -1
GSLFN_visual = [1]
print "Number of neurons: " + str(nH)
######## WEIGHT INITIALIZATION ########
initDistrib = paC.Init_Distrib("default", ["uniform", -1, 1])
#initDistrib = paC.Init_Distrib("default", ["normal",0,1])
#initDistrib = paC.Init_Distrib("deepLearning", ["DL1"])
######## STOP CRITERION ########
stopCriterion = paC.Stop_Criterion("Nmax", [])
######## REGULARIZATION ########
regularization = paC.Regularization("L2", [0.000005])
######## TRAINING ALGORITHM ########
BP_F = 0
BMBP_F = 1
ELM_F = 0