def Q8(A, b):
A_node = nn.Input("A")
x = nn.Input("x")
b_node = nn.Input("b")
expression = A_node * x + b_node
cc = expression.compile(golden.Builder())
return cc(A=A, b=b)
def grade_Q9():
x = np.random.rand(100, 100)
y = prj01.Q9(8).compile(golden.Builder())(x=x)
x2 = prj01.Q3(x, x)
x4 = prj01.Q3(x2, x2)
x8 = prj01.Q3(x4, x4)
return np.allclose(y, x8)
def grade_Q8():
relu = nn.ReLU()
flatten = nn.Flatten()
f1 = nn.Linear("f1", 28 * 28, 100)
f2 = nn.Linear("f2", 100, 10)
x = nn.Input2d("images", 28, 28, 1)
x = flatten(x)
x = f2(relu(f1(x)))
x.resolve(np.load("msimple_params.npz"))
mnist_test = np.load("mnist_test.npz")
images = mnist_test["images"][:1000]
infer0 = x.compile(golden.Builder())
infer1 = x.compile(cpp.Builder())
label0 = infer0(images=images).argmax(axis=1)
label1 = infer1(images=images).argmax(axis=1)
return np.allclose(label0, label1)
def grade_Q4():
relu = nn.ReLU()
flatten = nn.Flatten()
f1 = nn.Linear("f1", 28 * 28, 32)
f2 = nn.Linear("f2", 32, 10)
x = nn.Input2d("images", 28, 28, 1)
x = flatten(x)
x = f2(relu(f1(x)))
x.resolve(np.load("p5_params.npz"))
mnist_test = np.load("mnist_test.npz")
images = mnist_test["images"]
labels = mnist_test["labels"]
infer = x.compile(golden.Builder())
pred_labels = infer(images=images).argmax(axis=1)
count = sum(labels == pred_labels)
return count > 9500
def grade_Q4():
pool = nn.MaxPool2d(2, 2)
relu = nn.ReLU()
flatten = nn.Flatten()
x = nn.Input2d("images", 28, 28, 1)
c1 = nn.Conv2d("c1", 1, 8, 5) # 28->24
x = pool(relu(c1(x))) # 24->12
c2 = nn.Conv2d("c2", 8, 8, 5) # 12->8
x = pool(relu(c2(x))) # 8->4
f = nn.Linear("f", 8*4*4, 10)
x = f(flatten(x))
x.resolve(np.load("p6_params.npz"))
mnist_test = np.load("mnist_test.npz")
images = mnist_test["images"]
labels = mnist_test["labels"]
infer = x.compile(golden.Builder())
pred_labels = infer(images = images).argmax(axis = 1)
count = sum(labels == pred_labels)
return count > 9500
def grade_Q10():
pool = nn.MaxPool2d(2, 2)
relu = nn.ReLU()
flatten = nn.Flatten()
x = nn.Input2d("images", 28, 28, 1)
c1 = nn.Conv2d("c1", 1, 8, 3) # 28->26
c2 = nn.Conv2d("c2", 8, 8, 3) # 26->24
x = pool(relu(c2(relu(c1(x))))) # 24->12
c3 = nn.Conv2d("c3", 8, 16, 3) # 12->10
c4 = nn.Conv2d("c4", 16, 16, 3) # 10->8
x = pool(relu(c4(relu(c3(x))))) # 8->4
f = nn.Linear("f", 16 * 4 * 4, 10)
x = f(flatten(x))
x.resolve(np.load("mnist_params.npz"))
mnist_test = np.load("mnist_test.npz")
images = mnist_test["images"][:1000]
infer0 = x.compile(golden.Builder())
infer1 = x.compile(cpp.Builder())
label0 = infer0(images=images).argmax(axis=1)
label1 = infer1(images=images).argmax(axis=1)
return np.allclose(label0, label1)
def is_same(p, n, **kwargs):
e0 = p.compile(golden.Builder())
e1 = p.compile(cpp.Builder())
nkwargs = [random_kwargs(kwargs) for i in range(n)]
return all(
[np.allclose(e0(**nkwargs[i]), e1(**nkwargs[i])) for i in range(n)])
def grade_Q8():
A = np.random.rand(100, 100)
x = np.random.rand(100, 1)
b = np.random.rand(100, 1)
y = prj01.Q8(A, b).compile(golden.Builder())(x=x)
return np.allclose(y, prj01.Q2(prj01.Q3(A, x), b))
def grade_Q7():
a = np.random.rand(100, 50)
b = np.random.rand(100, 10)
c = np.random.rand(10, 50)
d = prj01.Q7().compile(golden.Builder())(a=a, b=b, c=c)
return np.allclose(d, prj01.Q2(a, prj01.Q3(b, c)))
def grade_Q6():
a = np.random.rand(100, 10)
b = np.random.rand(100, 10)
c = prj01.Q6().compile(golden.Builder())(a=a, b=b)
return np.allclose(c, prj01.Q2(a, b))
def grade_Q10():
x = np.random.randn(100, 100)
y = prj01.Q10().compile(golden.Builder())(x=x)
return np.allclose(y, np.abs(x))
