Using torch to confirm answers to CSC321 Winter 2018, Hw3, Q1

torch_vs_hw3_q1.md

import torch

W1 = torch.tensor([[1,-1,0,0],[0,1,-1,0],[0,0,1,-1]])
W1

tensor([[ 1, -1,  0,  0],
        [ 0,  1, -1,  0],
        [ 0,  0,  1, -1]])

b1 = torch.zeros(3)
b1

tensor([0., 0., 0.])

w2 = torch.tensor([-1,-1,-1])
w2

tensor([-1, -1, -1])

b2 = torch.tensor([0])
b2

tensor([0])

def activation(z):
    return torch.where(z >= 0, torch.ones_like(z), torch.zeros_like(z))

tensor([[1, 0, 1, 1],
        [1, 1, 0, 1],
        [1, 1, 1, 0]])

# xs should be a 1D tensor of length 4

def eval(xs):
    layer1_result = activation(torch.matmul(W1, xs))
    layer2_result = activation(torch.matmul(w2, layer1_result))
    return layer2_result
    

eval(torch.tensor([1,2,3,4]))

tensor(1)

eval(torch.tensor([1,1,3,4]))

tensor(0)

eval(torch.tensor([1,3,5,14]))

tensor(1)

eval(torch.tensor([3,2,1,0]))

tensor(0)

eval(torch.tensor([-10,-9,-8,0]))

tensor(1)