def RunNeuralNetDriver(nil, maxNetworkIterations, theta, numTrain,
numValidation, numTest, improveTheta):
#See if a pretrained weight set is given
if improveTheta:
if theta == 0:
print(
"!!!ERROR: Theta is set to zero, but improveTheta is set to one. Ser improveTheta to 0 or load a valid theta weight matrix"
)
#Train a network, if applicable
if theta == 0 or improveTheta:
#Load data from MNIST
print(">>>Loading Data From MNIST")
trainData, validationData, testData, trainLabels, validationLabels, testLabels = GenNeuralNetData.loadMNIST(
numTrain, numValidation, numTest)
print(">>>Data Loaded")
print(">>>Training Neural Network")
timer = time.time()
theta, validationErrors = TrainNeuralNet.RunTrainNeuralNet(
trainData, trainLabels, validationData, validationLabels, nil,
maxNetworkIterations, theta, improveTheta)
timer = time.time() - timer
print(">>>Training Complete. Train Time: " + str(timer) + " Seconds")
print(">>>Testing Neural Network Accuracy")
timer = time.time()
#Calculate accuracy on test set
testShape = testData.shape
testSamples = testShape[0]
testPixels = testShape[1]
L = nil.size #Number of layers in FFANN
#Initialize misclassifications
misclassifications = 0
#Calculate predictions
testErr = 0
for m in np.arange(testSamples):
#Forward propagation
activations = ANNSupport.forwardProp(testData[m, 0:testPixels],
testPixels, nil, L, theta)
#Backpropagation for final layer to calculate error
isIncorrect = ANNSupport.checkPrediction(activations[-1, 0:nil[-1]],
testLabels[m], nil, 1)
misclassifications += isIncorrect
percentAccuracy = (testSamples - misclassifications) / testSamples
timer = time.time() - timer
print(">>>Testing Complete. Test Time: " + str(timer) +
" Seconds. Misclassified " + str(misclassifications) +
" Samples. Dataset Size: " + str(testSamples) +
" Samples. Test Accuracy: " + str(100 * percentAccuracy) +
" Percent.")
return theta, validationErrors, percentAccuracy
def RunNeuralNetDriver(nil, maxNetworkIterations, theta, numTrain, numValidation, numTest, improveTheta):
#See if a pretrained weight set is given
if improveTheta:
if theta==0:
print("!!!ERROR: Theta is set to zero, but improveTheta is set to one. Ser improveTheta to 0 or load a valid theta weight matrix")
#Train a network, if applicable
if theta==0 or improveTheta:
#Load data from MNIST
print(">>>Loading Data From MNIST")
trainData, validationData, testData, trainLabels, validationLabels, testLabels = GenNeuralNetData.loadMNIST(numTrain, numValidation, numTest)
print(">>>Data Loaded")
print(">>>Training Neural Network")
timer=time.time()
theta, validationErrors = TrainNeuralNet.RunTrainNeuralNet(trainData, trainLabels, validationData, validationLabels, nil, maxNetworkIterations, theta, improveTheta);
timer=time.time()-timer
print(">>>Training Complete. Train Time: "+str(timer) + " Seconds")
print(">>>Testing Neural Network Accuracy")
timer=time.time()
#Calculate accuracy on test set
testShape=testData.shape
testSamples=testShape[0]
testPixels=testShape[1]
L=nil.size #Number of layers in FFANN
#Initialize misclassifications
misclassifications=0
#Calculate predictions
testErr=0
for m in np.arange(testSamples):
#Forward propagation
activations = ANNSupport.forwardProp(testData[m, 0:testPixels], testPixels, nil, L, theta)
#Backpropagation for final layer to calculate error
isIncorrect=ANNSupport.checkPrediction(activations[-1, 0:nil[-1]], testLabels[m], nil, 1)
misclassifications+=isIncorrect
percentAccuracy = (testSamples-misclassifications)/testSamples
timer=time.time()-timer
print(">>>Testing Complete. Test Time: "+ str(timer) + " Seconds. Misclassified "+str(misclassifications) + " Samples. Dataset Size: " + str(testSamples) + " Samples. Test Accuracy: " + str(100*percentAccuracy) + " Percent.")
return theta, validationErrors, percentAccuracy
def RunTrainNeuralNet(trainData, trainLabels, validationData, validationLabels, nil, maxNetworkIterations, theta, improveTheta):
trainShape=trainData.shape
trainSamples=trainShape[0]
trainPixels=trainShape[1]
validationShape=validationData.shape
validationSamples=validationShape[0]
validationPixels=validationShape[1]
validationErrors=[]
validationWeights=0
validationLookback=2
validationSampleRate=20
L=nil.size #Number of layers. Note that there are L-1 layers in theta
alpha=1 #Training constant information
alphaTol=0.00000001
if theta==0 and (not improveTheta):
theta=np.zeros((np.max(nil[0:-1]), np.max(nil[1:]), L-1))
for n in np.arange(L-1):
#Initialize weight layers between -0.5 and 0.5
theta[0:nil[n], 0:nil[n+1], n]=np.random.rand(nil[n], nil[n+1])-0.5
#Initialize activation error matrix (2D).
#Note that entries are stored row-wise in activations
#and column-wise in deltaLC
deltaLC=np.zeros((np.max(nil),L))
#Begin main loop
error=999999999
iters=-1
while iters<maxNetworkIterations and error>0.00001 and alpha>alphaTol:
iters=iters+1;
#Initialize 3D weight update matrix. Must be done each iteration
deltaUC=np.zeros(theta.shape)
#Reset additional iteration specific variables
error=0
print("Iterations: "+str(iters)) #Useful feedback for long training sessions
for m in np.arange(trainSamples):
#Forward propagation
activations = ANNSupport.forwardProp(trainData[m, 0:trainPixels], trainPixels, nil, L, theta)
#Backpropagation
#Output layer is easy but must be done separately
errorVec, squaredError=ANNSupport.checkPrediction(activations[-1, 0:nil[-1]], trainLabels[m], nil, 0)
deltaLC[0:nil[L-1],L-1]=errorVec.T
deltaUC[0:nil[-2],0:nil[-1],-1] += np.outer(activations[-2, 0:nil[-2]], deltaLC[0:nil[-1],-1])
#Harvest squared error in final layer
error += squaredError
#Hidden layers. No update to input layer (no error)
for layer in np.arange(L-2, 0, -1):
activationsInPrevious = activations[layer, 0:nil[layer]]
deltaLC[0:nil[layer], layer] = np.dot(theta[0:nil[layer],0:nil[layer+1],layer],deltaLC[0:nil[layer+1],layer+1]).T * activationsInPrevious * (1-activationsInPrevious)
deltaUC[0:nil[layer-1],0:nil[layer],layer-1] += np.outer(activations[layer-1, 0:nil[layer-1]], deltaLC[0:nil[layer],layer])
#Scale weights by layer
for layer in np.arange(L-1):
tempDeltaUCLayer = deltaUC[0:nil[layer],0:nil[layer+1], layer]
deltaUC[0:nil[layer],0:nil[layer+1],layer] = tempDeltaUCLayer/np.max(np.abs(tempDeltaUCLayer))
#Update theta. Error always decreases in batch mode
done=0
while not done:
newTheta = theta - alpha*deltaUC
newError=0
for m in np.arange(trainSamples):
#Forward propagation
activations = ANNSupport.forwardProp(trainData[m, 0:trainPixels], trainPixels, nil, L, newTheta)
#Backpropagation for final layer to calculate error
errorVec, squaredError=ANNSupport.checkPrediction(activations[-1, 0:nil[-1]], trainLabels[m], nil, 0)
newError += squaredError
#Implementation of momentum
if newError<error:
theta=newTheta;
alpha*=2;
error=newError;
done=1;
print('new error = '+str(error))
print('alpha = '+str(alpha));
else: #Error has increased
alpha/=2
#Evaluation of error on validation set
if validationSamples>0 and iters%validationSampleRate==0:
validationErr=0
for m in np.arange(validationSamples):
#Forward propagation
activations = ANNSupport.forwardProp(validationData[m, 0:validationPixels], validationPixels, nil, L, theta)
#Backpropagation for final layer to calculate error
squaredError=ANNSupport.checkPrediction(activations[-1, 0:nil[-1]], validationLabels[m], nil, 1)
validationErr+=squaredError
#Validation errors should be saved for early stopping and plotting
validationErrors.append(validationErr)
#Save the set of weights that performs better on the validations set
if len(validationErrors)>1 and validationErrors[-1]<validationErrors[-2]:
validationWeights=theta
#Check for overfitting
if len(validationErrors)>validationLookback and np.max(validationErrors[-validationLookback:])==validationErrors[-1]:
#Stop execution
print("Overfitting Detected. Returning Weights")
break
#End of outer iteration loop
if iters==maxNetworkIterations:
print('Max Iterations Reached. Returning Weights')
elif error<=0.00001:
print('Error Tolerance Reached')
elif alpha<=alphaTol:
print('Learning Rate Tolerance Reached')
if np.sum(validationWeights)==0:
print('Training Weights Returned')
return theta, validationErrors
else:
print('Validation Weights Returned')
return validationWeights, validationErrors
