def activ_fns():
x = Double('x')
z = ops.sigmoid(x)
graph = GradGraph(z)
graph.getOutput({x: 110.5})
graph.getGradients(wrt=x)
return x.gradient == 0
def testOps():
x = Integer('x')
y = ops.log(x)
z = ops.exp(y)
graph = GradGraph(z)
graph.getOutput({x: 1})
graph.getGradients(wrt=x)
return x.gradient == 1
def gradTestSimple():
a = Integer("a")
b = Integer("b")
e = (a + b) * (b + 1)
graph = GradGraph(e)
graph.getOutput({a: 2,
b: 1})
graph.getGradients(wrt=b)
return b.gradient == 5
def dotProduct():
x = DoubleTensor("Tensor1")
y = x.dot([3, 4])
z = y.dot([4, 5])
graph = GradGraph(z)
output = graph.getOutput({x: [3, 4]})
graph.getGradients(wrt=x)
flag1 = np.all(output == [100, 125])
flag2 = np.all(x.gradient == [[12., 16.], [15., 20.]])
return flag1 and flag2
def TensorOp():
x = DoubleTensor("Tensor1")
y = x - [3, 4]
z = ops.log(y * x)
graph = GradGraph(z)
output = graph.getOutput({x: [10]})
assert(np.all(np.isclose(output, np.log(10 * (10 - np.asarray([3, 4]))))))
graph.getGradients(wrt=x)
a = 2 * 10 - np.asarray([3, 4])
b = 1.0 / np.exp(np.asarray(output))
return np.all(np.isclose(x.gradient, a * b))
def gradTestLong():
x = Integer("Int1x")
y = Integer("Int2y")
z = Integer("Int3z")
p = Integer("Int4p")
k = p * z
n = (k + (y * p * z)) * z
graph = GradGraph(n)
graph.getOutput({x: 9,
y: 9,
z: 9,
p: 2})
graph.getGradients(wrt=z)
return True
def gradTestShort():
x = Integer("Int1x")
y = Integer("Int2y")
z = Integer("Int3z")
p = Integer("Int4p")
k = p * z
t = y * k
m = k + t
n = m * z
graph = GradGraph(n)
graph.getOutput({x: 9,
y: 9,
z: 9,
p: 2})
graph.getGradients(wrt=z)
return z.gradient == 360