def verifyFirstIterationCostFunction():
theta = np.zeros([10, trainX.shape[1]])
cost = computeCostFunction(trainX, trainY, theta, lambdaFactor)
if abs(cost - 2.30258509299) < 1.0e-6:
print "Verifying cost during first iteration: Passed"
else:
print "Verifying cost during first iteration: Failed"
def verifySecondIterationCostFunction():
theta = np.zeros([10, trainX.shape[1]])
for i in xrange(2):
cost = computeCostFunction(trainX, trainY, theta, lambdaFactor)
theta = runGradientDescentIteration(trainX, trainY, theta, alpha,
lambdaFactor)
if abs(cost - 0.896839929358) < 1.0e-6:
print "Verifying cost during second iteration: Passed"
else:
print "Verifying cost during second iteration: Failed"
def verifyInputOutputTypesSoftmax():
trainX, trainY, testX, testY = getMNISTData()
trainX = augmentFeatureVector(trainX[0:10])
trainY = trainY[0:10]
alpha = 0.3
lambdaFactor = 1.0e-4
theta = np.zeros([10, trainX.shape[1]])
tempParameter = 1
# check computeCostFunction
cost = computeCostFunction(trainX, trainY, theta, lambdaFactor,
tempParameter)
print('PASSED: computeCostFunction appears to handle correct input type.')
if isinstance(cost, float):
print('PASSED: computeCostFunction appears to return the right type.')
else:
print(
'FAILED: computeCostFunction appears to return the wrong type. Expected {0} but got {1}'
.format(float, type(cost)))
# check computeProbabilities
probabilities = computeProbabilities(trainX, theta, tempParameter)
print('PASSED: computeProbabilities appears to handle correct input type.')
if isinstance(probabilities, np.ndarray):
if probabilities.shape == (10, 10):
print(
'PASSED: computeProbabilities return value appears to return a numpy array of the right shape.'
)
else:
print('FAILED: computeProbabilities return value appears to return a numpy array but with the wrong size. ' + \
'Expected a shape of {0} but got {1}.'.format((10,10), probabilities.shape))
else:
print('FAILED: computeProbabilities appears to be the wrong type. ' + \
'Expected {0} but got {1}.'.format(type(np.array(range(4))), type(probabilities)))
# check gradient descent
theta = runGradientDescentIteration(trainX, trainY, theta, alpha,
lambdaFactor, tempParameter)
print(
'PASSED: runGradientDescentIteration appears to handle correct input type.'
)
if isinstance(theta, np.ndarray):
if theta.shape == (10, 785):
print(
'PASSED: runGradientDescentIteration return value appears to return a numpy array of the right shape.'
)
else:
print('FAILED: runGradientDescentIteration return value appears to return a numpy array but with the wrong size. ' + \
'Expected {0} but got {1}.'.format((10, 785), theta.shape))
else:
print('FAILED: runGradientDescentIteration appears to return the wrong type. ' + \
'Expected {0} but got {1}'.format(type(np.array(range(4))), type(theta)))