# create the data

Nclass = 500

D = 2 # dimensionality of input

M = 3 # hidden layer size

K = 3 # number of classes

X1 = np.random.randn(Nclass, D) + np.array([0, -2])

X2 = np.random.randn(Nclass, D) + np.array([2, 2])

X3 = np.random.randn(Nclass, D) + np.array([-2, 2])

X = np.vstack([X1, X2, X3])

Y = np.array([0]*Nclass + [1]*Nclass + [2]*Nclass)

N = len(Y)

# turn Y into an indicator matrix for training

T = np.zeros((N, K))

for i in range(N):

T[i, Y[i]] = 1

After T = np.zeros((N, K))

for i in range(N):

T[i, Y[i]] = 1 step: What would t look like?

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