data = []
for line in lines:
values = line.split(',')
# Divide by 15 for Min Max Normalisation
input = [(int(x.replace('\n', '')) / 15) for x in values[1:]]
label = charToOneHot(values[0])
data.append([input, label])
dataSet = DataSet(data, 0.8)
# Load or insantiate new MLP
if len(sys.argv) == 1:
mlp = MLP(dataSet.numInputs, numHidden, dataSet.numOutputs, outputActivation='SOFTMAX', hiddenActivation=hiddenActivation)
train = True
elif sys.argv[1] == 'load':
mlp = MLP(loadModel=True)
train = False
numEpochs = 1
# Train and test
for epoch in range(numEpochs):
trainLoss, trainAccuracy = mlp.process(dataSet.trainData, train=train, learningRate=learningRate, updateFreq=updateFreq)
if epoch % printFreq == 0 or epoch == numEpochs - 1:
testLoss, testAccuracy = mlp.process(dataSet.testData, train=False)
print("EPOCH: ", epoch)
print("TRAIN LOSS: ", trainLoss)
print("TRAIN ACC: ", trainAccuracy, "%")
print("TEST LOSS: ", testLoss)
print("TEST ACC: ", testAccuracy, "%\n")
[[1, 1], [1, 0]]])
# Best learning rates for each determined through experimenting
if outputActivation == 'SIGMOID':
dataSet = dataSet1
learningRate = 1
if outputActivation == 'TANH':
dataSet = dataSet2
learningRate = 1
if outputActivation == 'RELU':
dataSet = dataSet1
learningRate = 0.01
if outputActivation == 'SOFTMAX':
dataSet = dataSet3
learningRate = 0.1
if 'dataSet' not in list(locals()) + list(globals()):
print("\'" + outputActivation + "\' is not a valid activation function.")
sys.exit()
mlp = MLP(dataSet.numInputs, 2, dataSet.numOutputs, outputActivation,
hiddenActivation)
for epoch in range(numEpochs):
epochLoss, epochAccuracy = mlp.process(dataSet.trainData,
train=True,
learningRate=learningRate)
if epoch % printFreq == 0:
print("EPOCH: ", epoch)
print("TRAIN LOSS: ", epochLoss)
print("TRAIN ACC: ", epochAccuracy, "%\n")