def __init__(self, in_size, out_size):
super().__init__()
self.weights = minitorch.Parameter(
2 * (minitorch.rand((in_size, out_size)) - 0.5)
)
self.bias = minitorch.Parameter(2 * (minitorch.rand((out_size,)) - 0.5))
self.out_size = out_size
def test_mm2():
a = minitorch.rand((2, 3), backend=FastTensorBackend)
b = minitorch.rand((3, 4), backend=FastTensorBackend)
c = a @ b
c2 = (a.view(2, 3, 1) * b.view(1, 3, 4)).sum(1).view(2, 4)
for ind in c._tensor.indices():
assert_close(c[ind], c2[ind])
def test_mm():
a = minitorch.rand((2, 3))
b = minitorch.rand((3, 4))
c = minitorch.matmul(a, b)
c2 = (a.view(2, 3, 1) * b.view(1, 3, 4)).sum(1).view(2, 4)
print(c)
print(c2)
for ind in c._tensor.indices():
assert_close(c[ind], c2[ind])
def test_max(t):
out = minitorch.nn.max(t, 2)
assert out[0, 0, 0] == max(t[0, 0, i] for i in range(4))
out = minitorch.nn.max(t, 1)
assert out[0, 0, 0] == max(t[0, i, 0] for i in range(3))
out = minitorch.nn.max(t, 0)
assert out[0, 0, 0] == max(t[i, 0, 0] for i in range(2))
rand_tensor = minitorch.rand(t.shape) * 1e-5
t = t + rand_tensor
minitorch.grad_check(lambda t: minitorch.nn.max(t, 2), t)
def test_max(t):
t_np = t.to_numpy()
compare_0 = t_np.max(axis=0)
out_0 = minitorch.Max.apply(t, 0)
assert out_0.shape == (1, 3, 4)
assert np.array_equal(compare_0.reshape(-1), out_0.to_numpy().reshape(-1))
minitorch.grad_check(lambda t: minitorch.Max.apply(t, 0),
t + (minitorch.rand(t.shape) * 1e-5))
compare_1 = t_np.max(axis=1)
out_1 = minitorch.Max.apply(t, 1)
assert out_1.shape == (2, 1, 4)
assert np.array_equal(compare_1.reshape(-1), out_1.to_numpy().reshape(-1))
minitorch.grad_check(lambda t: minitorch.Max.apply(t, 1),
t + (minitorch.rand(t.shape) * 1e-5))
compare_2 = t_np.max(axis=2)
out_2 = minitorch.Max.apply(t, 2)
assert out_2.shape == (2, 3, 1)
assert np.array_equal(compare_2.reshape(-1), out_2.to_numpy().reshape(-1))
minitorch.grad_check(lambda t: minitorch.Max.apply(t, 0),
t + (minitorch.rand(t.shape) * 1e-5))
def RParam(*shape):
r = 2 * (minitorch.rand(shape) - 0.5)
return minitorch.Parameter(r)
import minitorch
import minitorch.nn as nn
input = minitorch.rand(2, 3)
linear = nn.Linear(3, 5, bias=True)
output = linear(input)
print(f"output: {output}")
class Model(nn.Module):
def __init__(self):
super().__init__()
self.linear_1 = nn.Linear(3, 5, bias=True)
self.linear_2 = nn.Linear(5, 6)
def forward(self, input):
output = self.linear_1(input)
output = self.linear_2(output)
return output
input = minitorch.rand(2, 3)
model = Model()
output = model(input)
print(f"output: {output}")
for name, parameter in model.named_parameters():
print(f"{name}: {parameter}")
for name, module in model.named_modules(prefix='model'):
print(f"{name}: {module}")
def RParam(*shape):
r = 0.1 * (minitorch.rand(shape, backend=BACKEND) - 0.5)
return minitorch.Parameter(r)
def RParam(*shape):
p = 1.0
for s in shape:
p += s
r = 2 * (minitorch.rand(shape, backend=BACKEND) - 0.5)
return minitorch.Parameter(r)
def RParam(*shape, backend):
r = minitorch.rand(shape, backend=backend) - 0.5
return minitorch.Parameter(r)
