def test_class(self):
class Test(object):
def f(self, x):
return x + 100.0
@classmethod
def g(cls, x):
return x + 100.0
@staticmethod
def h(x):
return x + 100.0
test = Test()
t = Tracer()
x = 1.0
o = t.trace(test.f, x)
f = t.compile_function(x, o)
assert(f(2.0) == 102.0)
o = t.trace(test.g, x)
f = t.compile_function(x, o)
assert(f(2.0) == 102.0)
o = t.trace(test.h, x)
f = t.compile_function(x, o)
assert(f(2.0) == 102.0)
def test_class(self):
class Test(object):
def f(self, x):
return x + 100.0
@classmethod
def g(cls, x):
return x + 100.0
@staticmethod
def h(x):
return x + 100.0
test = Test()
t = Tracer()
x = 1.0
o = t.trace(test.f, x)
f = t.compile_function(x, o)
assert(f(2.0) == 102.0)
o = t.trace(test.g, x)
f = t.compile_function(x, o)
assert(f(2.0) == 102.0)
o = t.trace(test.h, x)
f = t.compile_function(x, o)
assert(f(2.0) == 102.0)
def test_access_attribute(self):
class Test(object):
def __init__(self):
self.x = np.arange(5.) - 10.0
def getx(self):
return self.x
test = Test()
def f(x):
return np.dot(x, test.x)
x = np.arange(5.)
t = Tracer()
o = t.trace(f, x)
g = t.compile_gradient(x, o, wrt=test.x)
self.assertTrue(np.allclose(g(x), x))
def test_access_attribute(self):
class Test(object):
def __init__(self):
self.x = np.arange(5.) - 10.0
def getx(self):
return self.x
test = Test()
def f(x):
return np.dot(x, test.x)
x = np.arange(5.)
t = Tracer()
o = t.trace(f, x)
g = t.compile_gradient(x, o, wrt=test.x)
self.assertTrue(np.allclose(g(x), x))
def test_readme(self):
""" the original README example"""
# -- a vanilla function
def f1(x):
return x + 2
# -- a function referencing a global variable
y = np.random.random(10)
def f2(x):
return x * y
# -- a function with a local variable
def f3(x):
z = tag(np.ones(10), 'local_var')
return (x + z) ** 2
# -- create a general symbolic tracer and apply
# it to the three functions
x = np.random.random(10)
tracer = Tracer()
out1 = tracer.trace(f1, x)
out2 = tracer.trace(f2, out1)
out3 = tracer.trace(f3, out2)
# -- compile a function representing f(x, y, z) = out3
new_fn = tracer.compile_function(inputs=[x, y, 'local_var'],
outputs=out3)
# -- compile the gradient of f(x) = out3, with respect to y
fn_grad = tracer.compile_gradient(inputs=x,
outputs=out3,
wrt=y,
reduction=theano.tensor.sum)
assert fn_grad # to stop flake error
self.assertTrue(np.allclose(new_fn(x, y, np.ones(10)), f3(f2(f1(x)))))
def test_multiple_trace(self):
def f1(x):
return x + 1.0
def f2(x):
return x * 2.0
def f3(x):
return x ** 2
t = Tracer()
x = np.random.random((3, 4))
o1 = t.trace(f1, x)
o2 = t.trace(f2, o1)
o3 = t.trace(f3, o2)
# test function
f = t.compile_function(x, o3)
self.assertTrue(np.allclose(f(x), f3(f2(f1(x)))))
# test gradient
o4 = t.trace(lambda x: x.sum(), o3)
g = t.compile_gradient(x, o4, wrt=x)
self.assertTrue(np.allclose(g(x), 8 * (x+1)))
def test_readme(self):
""" the original README example"""
# -- a vanilla function
def f1(x):
return x + 2
# -- a function referencing a global variable
y = np.random.random(10)
def f2(x):
return x * y
# -- a function with a local variable
def f3(x):
z = tag(np.ones(10), 'local_var')
return (x + z) ** 2
# -- create a general symbolic tracer and apply
# it to the three functions
x = np.random.random(10)
tracer = Tracer()
out1 = tracer.trace(f1, x)
out2 = tracer.trace(f2, out1)
out3 = tracer.trace(f3, out2)
# -- compile a function representing f(x, y, z) = out3
new_fn = tracer.compile_function(inputs=[x, y, 'local_var'],
outputs=out3)
# -- compile the gradient of f(x) = out3, with respect to y
fn_grad = tracer.compile_gradient(inputs=x,
outputs=out3,
wrt=y,
reduction=theano.tensor.sum)
assert fn_grad # to stop flake error
self.assertTrue(np.allclose(new_fn(x, y, np.ones(10)), f3(f2(f1(x)))))
def test_multiple_trace(self):
def f1(x):
return x + 1.0
def f2(x):
return x * 2.0
def f3(x):
return x ** 2
t = Tracer()
x = np.random.random((3, 4))
o1 = t.trace(f1, x)
o2 = t.trace(f2, o1)
o3 = t.trace(f3, o2)
# test function
f = t.compile_function(x, o3)
self.assertTrue(np.allclose(f(x), f3(f2(f1(x)))))
# test gradient
o4 = t.trace(lambda x: x.sum(), o3)
g = t.compile_gradient(x, o4, wrt=x)
self.assertTrue(np.allclose(g(x), 8 * (x+1)))