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Engineering · Computer Science
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# 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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