dtype=float) # Input: [HoursSleep, HoursStudy]
testY_orig = np.array([[70], [89], [85], [75]],
dtype=float) # Output: [Test Score]
#Nomalize data:
trainX = trainX_orig / np.amax(trainX_orig, axis=0)
trainY = trainY_orig / 100 #Max test score is 100
#Nomalize data by max of training data:
testX = testX_orig / np.amax(trainX, axis=0)
testY = testY_orig / 100 #Max test score is 100
NN = NeuralNetwork(0.0001)
# # Make sure our gradients are correct:
numgrad = util.computeNumericalGradient(NN, trainX, trainY)
grad = NN.computeGradients(trainX, trainY)
norm = np.linalg.norm(grad - numgrad) / np.linalg.norm(grad + numgrad)
print(norm) # Should be less than 1e-8:
T = Trainer(NN)
T.train(trainX, trainY, testX, testY)
# util.showCostPlot(T.J, T.testJ)
#Generate Test Data
hoursSleep = np.linspace(0, 10, 100)
hoursStudy = np.linspace(0, 5, 100)
# Normalize data
hoursSleepNorm = hoursSleep / 10.
hoursStudyNorm = hoursStudy / 5.
#Create 2-d versions of input for plotting
a, b = np.meshgrid(hoursSleepNorm, hoursStudyNorm)
