)
#if (loadFile != ""):
#net1.load_params_from(loadFile)
net1.max_epochs = 50
net1.update_learning_rate = ln;
return net1
generations, generationsToInputs, generationsToOutputs = dataParser.parse(fname = "whole_population_0.txt")
iters = 150
saveFile = "LasagneWeights400_2Layer"
trainingInputs, trainingOutputs, testInputs, testOutputs = dataParser.makeSets(generationsToInputs, generationsToOutputs, generations[0:200], 1, 0.25)
ln = 0.01
X = Normalizers.gaussNormalize(trainingInputs)
Xtest = Normalizers.gaussNormalize(testInputs)
#
Y = Normalizers.gaussNormalize(trainingOutputs)
Ytest = Normalizers.gaussNormalize(testOutputs)
X = np.asarray(X, np.float32)
Y = np.asarray(Y, np.float32)
Xtest= np.asarray(Xtest, np.float32)
Ytest = np.asarray(Ytest, np.float32)
net = createNet(X, Y, ln, saveFile)
for n in range(iters):
net.fit(X, Y) # This thing try to do the fit itself
y_pred = net.predict(Xtest)
errors = Normalizers.deGauss(trainingOutputs, Ytest) - Normalizers.deGauss(trainingOutputs, y_pred)
print(np.mean(abs(errors),axis = 0));
xTraining, yTraining, xTest, yTest = makeSets(X, Y, trainingPercent)
xTraining = array(xTraining).astype(float32)
yTraining = array(yTraining).astype(float32)
xTest = array(xTest).astype(float32)
yTest = array(yTest).astype(float32)
numInputs = 1
numOutputs = 1
X = Normalizers.minMaxNormalize(xTraining)
Xtest = Normalizers.minMaxNormalize(xTest)
#Y = Normalizers.gaussNormalize(trainingOutputs)
#Ytest = Normalizers.gaussNormalize(testOutputs)
#
#X = trainingInputs
#Xtest = testInputs
Y = yTraining
Ytest = yTest
hiddenSize = 40;
model = Sequential()
model.add(Dense(numInputs, hiddenSize, init='lecun_uniform'))
model.add(Activation('sigmoid'))
