def test_two_args(fn, ts):
t1, t2 = ts
t3 = fn[1](t1, t2)
for ind in t3._tensor.indices():
assert (
t3[ind] == fn[1](minitorch.Scalar(t1[ind]), minitorch.Scalar(t2[ind])).data
)
def __init__(self, in_size, out_size):
super().__init__()
self.weights = []
self.bias = []
for i in range(in_size):
self.weights.append([])
for j in range(out_size):
self.weights[i].append(
self.add_parameter(
f"weight_{i}_{j}",
minitorch.Scalar(2 * (random.random() - 0.5))))
for j in range(out_size):
self.bias.append(
self.add_parameter(
f"bias_{j}",
minitorch.Scalar(2 * (random.random() - 0.5))))
def test_nn_size():
model = Network2()
assert len(model.parameters()) == (len(model.layer1.parameters()) +
len(model.layer2.parameters()))
assert model.layer2.bias[0].value.data != 0
assert model.layer1.bias[0].value.data != 0
assert model.layer1.weights[0][0].value.data != 0
for p in model.parameters():
p.update(minitorch.Scalar(0))
assert model.layer2.bias[0].value.data == 0
assert model.layer1.bias[0].value.data == 0
assert model.layer1.weights[0][0].value.data == 0
def scalars(draw, min_value=-100000, max_value=100000):
val = draw(floats(min_value=min_value, max_value=max_value))
return minitorch.Scalar(val)
def test_one_args(fn, t1):
t2 = fn[1](t1)
for ind in t2._tensor.indices():
assert_close(t2[ind], fn[1](minitorch.Scalar(t1[ind])).data)
def test_scalar_name():
x = minitorch.Scalar(10, name="x")
y = (x + 10.0) * 20
y.name = "y"
hash(y)
return y