def train( self, trainingData, trainingLabels, validationData, validationLabels, showPlot=True, showPacmanPlot=True):
"""
Stochastic gradient descent to learn self.weights
"""
numDimensions = trainingData[0].size
if showPlot:
# Initialize list to store loss per iteration for plotting later
trainingLossPerIteration = []
self.weights = []
for i in xrange(len(self.legalLabels)):
self.weights.append(np.zeros(len(trainingData[0])))
# Stochastic gradient descent
for itr in xrange(self.max_iterations):
for (datum, label) in zip(trainingData, trainingLabels):
# We have a list of arrays of weights here, instead of a matrix,
# so we end up looping over labels
dw = self.der_loss_dw(datum, label, self.weights)
for j in range(len(self.legalLabels)):
self.weights[j] = self.weights[j] - self.alpha*dw[j]
if showPlot:
predictions = self.classify(validationData)
accuracyCount = [predictions[i] == validationLabels[i] for i in range(len(validationLabels))].count(True)
print "Performance on validation set for iteration= %d: (%.1f%%)" % (itr, 100.0*accuracyCount/len(validationLabels))
trainingLoss = self.classificationLoss(trainingData, trainingLabels)
trainingLossPerIteration.append(trainingLoss)
plotUtil.plotCurve(range(len(trainingLossPerIteration)), trainingLossPerIteration, 2, "Training Loss")
def train( self, trainingData, trainingLabels, validationData, validationLabels, showPlot=True, showPacmanPlot=True):
"""
Stochastic gradient descent to learn self.weights
"""
numDimensions = trainingData[0].size
# Initializes weights to zero
self.weights = np.zeros(numDimensions)
if showPlot:
# Initialize list to store loss per iteration for plotting later
trainingLossPerIteration = []
# Initial loss
trainingLoss = self.classificationLoss(trainingData, trainingLabels)
trainingLossPerIteration.append(trainingLoss)
# Check for offset term
plotDims = numDimensions-1
for datum in trainingData:
if datum[-1] != 1:
plotDims += 1
break
if showPacmanPlot and plotDims <=2:
if plotDims == 2:
pacmanDisplay = pacmanPlot.PacmanPlotClassification2D();
pacmanDisplay.plot(trainingData[:,:plotDims], trainingLabels)
else:
pacmanDisplay = pacmanPlot.PacmanPlotLogisticRegression1D();
pacmanDisplay.plot(trainingData[:,0], trainingLabels)
graphicsUtils.sleep(0.1)
# Stochastic gradient descent
for itr in xrange(self.max_iterations):
for (datum, label) in zip(trainingData, trainingLabels):
self.weights = stochasticGradientDescentUpdate(datum, label, self.weights, self.alpha, self.der_loss_dw)
if showPlot:
predictions = self.classify(validationData)
accuracyCount = [predictions[i] == validationLabels[i] for i in range(len(validationLabels))].count(True)
print "Performance on validation set for iteration= %d: (%.1f%%)" % (itr, 100.0*accuracyCount/len(validationLabels))
trainingLoss = self.classificationLoss(trainingData, trainingLabels)
trainingLossPerIteration.append(trainingLoss)
if plotDims <= 2:
if showPacmanPlot:
pacmanDisplay.setWeights(self.weights)
graphicsUtils.sleep(0.1)
else:
if plotDims == 2:
plotUtil.plotClassification2D(trainingData[:,:plotDims],trainingLabels, self.weights, 1)
else:
plotUtil.plotLogisticRegression1D(trainingData[:,:plotDims],trainingLabels, self.weights, 1)
plotUtil.plotCurve(range(len(trainingLossPerIteration)), trainingLossPerIteration, 2, "Training Loss")
if showPlot and showPacmanPlot:
graphicsUtils.end_graphics()
def train( self, trainingData, trainingLabels, validationData, validationLabels, showPlot=True, showPacmanPlot=True):
"""
Stochastic gradient descent to learn self.weights
"""
numDimensions = trainingData[0].size
if showPlot:
# Initialize list to store loss per iteration for plotting later
trainingLossPerIteration = []
self.weights = []
for i in xrange(len(self.legalLabels)):
self.weights.append(np.zeros(len(trainingData[0])))
# Stochastic gradient descent
for itr in xrange(self.max_iterations):
for (datum, label) in zip(trainingData, trainingLabels):
# We have a list of arrays of weights here, instead of a matrix,
# so we end up looping over labels
dw = self.der_loss_dw(datum, label, self.weights)
for j in range(len(self.legalLabels)):
self.weights[j] = self.weights[j] - self.alpha*dw[j]
if showPlot:
predictions = self.classify(validationData)
accuracyCount = [predictions[i] == validationLabels[i] for i in range(len(validationLabels))].count(True)
print "Performance on validation set for iteration= %d: (%.1f%%)" % (itr, 100.0*accuracyCount/len(validationLabels))
trainingLoss = self.classificationLoss(trainingData, trainingLabels)
trainingLossPerIteration.append(trainingLoss)
plotUtil.plotCurve(range(len(trainingLossPerIteration)), trainingLossPerIteration, 2, "Training Loss")
def train( self, trainingData, trainingLabels, validationData, validationLabels, showPlot=True, showPacmanPlot=True):
"""
Stochastic gradient descent to learn self.weights
"""
numDimensions = trainingData[0].size
# Initializes weights to zero
self.weights = np.zeros(numDimensions)
if showPlot:
# Initialize list to store loss per iteration for plotting later
trainingLossPerIteration = []
# Initial loss
trainingLoss = self.classificationLoss(trainingData, trainingLabels)
trainingLossPerIteration.append(trainingLoss)
# Check for offset term
plotDims = numDimensions-1
for datum in trainingData:
if datum[-1] != 1:
plotDims += 1
break
if showPacmanPlot and plotDims <=2:
if plotDims == 2:
pacmanDisplay = pacmanPlot.PacmanPlotClassification2D();
pacmanDisplay.plot(trainingData[:,:plotDims], trainingLabels)
else:
pacmanDisplay = pacmanPlot.PacmanPlotLogisticRegression1D();
pacmanDisplay.plot(trainingData[:,0], trainingLabels)
graphicsUtils.sleep(0.1)
# Stochastic gradient descent
for itr in xrange(self.max_iterations):
for (datum, label) in zip(trainingData, trainingLabels):
self.weights = stochasticGradientDescentUpdate(datum, label, self.weights, self.alpha, self.der_loss_dw)
if showPlot:
predictions = self.classify(validationData)
accuracyCount = [predictions[i] == validationLabels[i] for i in range(len(validationLabels))].count(True)
print "Performance on validation set for iteration= %d: (%.1f%%)" % (itr, 100.0*accuracyCount/len(validationLabels))
trainingLoss = self.classificationLoss(trainingData, trainingLabels)
trainingLossPerIteration.append(trainingLoss)
if plotDims <= 2:
if showPacmanPlot:
pacmanDisplay.setWeights(self.weights)
graphicsUtils.sleep(0.1)
else:
if plotDims == 2:
plotUtil.plotClassification2D(trainingData[:,:plotDims],trainingLabels, self.weights, 1)
else:
plotUtil.plotLogisticRegression1D(trainingData[:,:plotDims],trainingLabels, self.weights, 1)
plotUtil.plotCurve(range(len(trainingLossPerIteration)), trainingLossPerIteration, 2, "Training Loss")
if showPlot and showPacmanPlot:
graphicsUtils.end_graphics()
def trainGradient(self,
trainingData,
regressionData,
numIterations,
showPlot=True,
showPacmanPlot=True):
print 'Training with gradient ...'
if showPlot:
# Initialize list to store loss per iteration for plotting later
trainingLossPerIteration = []
if showPacmanPlot:
pacmanDisplay = pacmanPlot.PacmanPlotRegression()
pacmanDisplay.plot(trainingData, regressionData)
graphicsUtils.sleep(0.1)
# Initializes weights to zero
numDimensions = trainingData[0].size
self.weights = np.zeros(numDimensions)
# Stochastic gradient descent
for i in xrange(numIterations):
if i + 1 % 10 == 0:
print "Iteration " + str(i + 1) + " of " + str(numIterations)
for (datum, label) in zip(trainingData, regressionData):
self.weights = stochasticGradientDescentUpdate(
datum, label, self.weights, self.alpha, self.der_loss_dw)
if showPlot:
trainingLoss = self.regressionLoss(trainingData,
regressionData)
trainingLossPerIteration.append(trainingLoss)
if showPacmanPlot:
pacmanDisplay.setWeights(self.weights)
graphicsUtils.sleep(0.05)
else:
plotUtil.plotRegression(trainingData, regressionData,
self.weights, 1)
plotUtil.plotCurve(range(len(trainingLossPerIteration)),
trainingLossPerIteration, 2,
"Training Loss")
if showPlot and showPacmanPlot:
graphicsUtils.end_graphics()
def trainGradient(self, trainingData, regressionData, numIterations, showPlot=True, showPacmanPlot=True):
print 'Training with gradient ...'
if showPlot:
# Initialize list to store loss per iteration for plotting later
trainingLossPerIteration = []
if showPacmanPlot:
pacmanDisplay = pacmanPlot.PacmanPlotRegression();
pacmanDisplay.plot(trainingData, regressionData)
graphicsUtils.sleep(0.1)
# Initializes weights to zero
numDimensions = trainingData[0].size
self.weights = np.zeros(numDimensions)
# Stochastic gradient descent
for i in xrange(numIterations):
if i+1 % 10 == 0:
print "Iteration " + str(i+1) + " of "+ str(numIterations)
for (datum, label) in zip(trainingData, regressionData):
self.weights = stochasticGradientDescentUpdate(datum, label, self.weights, self.alpha, self.der_loss_dw)
if showPlot:
trainingLoss = self.regressionLoss(trainingData, regressionData)
trainingLossPerIteration.append(trainingLoss)
if showPacmanPlot:
pacmanDisplay.setWeights(self.weights)
graphicsUtils.sleep(0.05)
else:
plotUtil.plotRegression(trainingData,regressionData, self.weights, 1)
plotUtil.plotCurve(range(len(trainingLossPerIteration)), trainingLossPerIteration, 2, "Training Loss")
if showPlot and showPacmanPlot:
graphicsUtils.end_graphics()
