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 __init__(self, extra=0):
super().__init__()
self.parameter_a = minitorch.Parameter(VAL_A)
self.parameter_b = minitorch.Parameter(VAL_B)
self.non_parameter = 10
for i in range(extra):
self.add_parameter(f"extra_parameter_{i}", None)
def __init__(self, in_size, out_size, backend):
super().__init__()
self.weights = RParam(in_size, out_size, backend=backend)
s = minitorch.zeros((out_size, ), backend=backend)
s = s + 0.1
self.bias = minitorch.Parameter(s)
self.out_size = out_size
def test_parameter():
t = MockParam()
q = minitorch.Parameter(t)
print(q)
assert t.x
t2 = MockParam()
q.update(t2)
assert t2.x
def RParam(*shape):
r = 2 * (minitorch.rand(shape) - 0.5)
return minitorch.Parameter(r)
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 __init__(self):
super().__init__()
self.parameter_a = minitorch.Parameter(VAL_A)
def __init__(self, size_a, size_b, val):
super().__init__()
self.module_a = Module2(size_a)
self.module_b = Module2(size_b)
self.parameter_a = minitorch.Parameter(val)
def __init__(self):
super().__init__()
self.p3 = minitorch.Parameter(15)
def __init__(self):
super().__init__()
self.p1 = minitorch.Parameter(5)
self.non_param = 10
self.a = ModuleA2()
self.b = ModuleA3()
def __init__(self):
super().__init__()
self.module_a = Module2(5)
self.module_b = Module2(10)
self.parameter_a = minitorch.Parameter(VAL)
def __init__(self, extra=0):
super().__init__()
self.parameter_a = minitorch.Parameter(VAL_A + 100)
def RParam(*shape, backend):
r = minitorch.rand(shape, backend=backend) - 0.5
return minitorch.Parameter(r)
