def validateTitanic():
# Data
X, Y = getDataValidation()
# Network
inputSize = 7
layerCount = 2
networkConf = nnConf.NeuralNetworkConf(inputSize, layerCount)
networkConf.layerConf[0].neuronCount = 20
networkConf.layerConf[0].activationFn = "relu"
networkConf.layerConf[0].weightInitializerMethod = "random"
networkConf.layerConf[1].neuronCount = 1
networkConf.layerConf[1].activationFn = "sigmoid"
networkConf.layerConf[1].weightInitializerMethod = "random"
networkConf.Lambda = .00009 # 0.0001
networkConf.maxIter = 500
accuracyList = nnUtils.validateNN(X, Y, networkConf, 5, showLearning=False)
print(accuracyList)
print("Mean Accuracy: ", np.mean(accuracyList))
# Store Neural network settings and state
# print(NN.getGlobalConf())
# print(networkConf)
nnUtils.saveConfig(networkConf, nn.getGlobalConf())
nnUtils.restoreConfig()
accuracyList = []
return (accuracyList)
def findBestInitialWeights():
netConf, wrightsBias = runAndMeasure()
netConfOld, wrightsBiasOld = nnUtils.restoreConfig("nn.json")
print("New accuracy: ", netConf.accuracy)
print("Old accuracy: ", netConfOld.accuracy)
if netConf.accuracy > netConfOld.accuracy:
# Store Neural network settings and state
print("Got Better accuracy")
nnUtils.saveConfig(netConf, wrightsBias, "nn.json")
def loadAndValidate(trainX, trainY):
netConf, wrightsBias = nnUtils.restoreConfig("nn.json")
accuracyList = nnUtils.validateNN(trainX,
trainY,
netConf,
5,
showLearning=False,
wtReuse=True,
wtAndBias=wrightsBias)
print("Best accuracy on validation: ", np.mean(accuracyList))
def loadAndRun():
# Data
X, Y = getDataValidation()
netConf, wrightsBias = nnUtils.restoreConfig("nn.json")
accuracyList = nnUtils.validateNN(X, Y, netConf, 5,
showLearning=False,
wtReuse=True,
wtAndBias=wrightsBias)
print(accuracyList)
print("Mean Accuracy: ", np.mean(accuracyList))
showLearning=False)
print("Initial Mean Accuracy: ", np.mean(accuracyList))
# * Run evaluation varying different network parameters with
# the fixed initial weights.
# netConf.Lambda, accuracy = getBestLambda(trainX, trainY, netConf)
# netConf.maxIter, accuracy = getBestMaxIter(trainX, trainY, netConf)
netConf.Lambda = 0.0000946
netConf.maxIter = 500
print("Best Lambda: ", netConf.Lambda)
print("Best MaxIter: ", netConf.maxIter)
print("Best Config Mean Accuracy: ", np.mean(accuracyList))
# 3. With the best network configuration get best set of Weights.
# * Run and store the best weights for the network configuration
# in above step.
# 4. Goto step 3 until maximum accuracy is achieved.
# findAndStoreBestInitialWeights(trainX, trainY, netConf, 100)
# 4.5. Validate with the best config
loadAndValidate(trainX, trainY)
# 5. Train the best model with training data and predict on test data.
testX, PassengerId = getTestData()
netConf, wrightsBias = nnUtils.restoreConfig("nn.json")
trainAndPredict(trainX, trainY, testX, PassengerId, netConf, wrightsBias)
# That's all