def test_op_removed(self):
x = theano.tensor.matrix("x")
y = x * gradient.consider_constant(x)
f = theano.function([x], y)
# need to refer to theano.gradient.consider_constant_ here,
# theano.gradient.consider_constant is a wrapper function!
assert gradient.consider_constant_ not in [node.op for node in f.maker.fgraph.toposort()]
def test_op_removed(self):
x = theano.tensor.matrix('x')
y = x * gradient.consider_constant(x)
f = theano.function([x], y)
# need to refer to theano.gradient.consider_constant_ here,
# theano.gradient.consider_constant is a wrapper function!
assert gradient.consider_constant_ not in \
[node.op for node in f.maker.fgraph.toposort()]
def test_grad(self):
T = theano.tensor
a = np.asarray(self.rng.randn(5, 5), dtype=config.floatX)
x = T.matrix("x")
expressions_gradients = [
(x * gradient.consider_constant(x), x),
(x * gradient.consider_constant(T.exp(x)), T.exp(x)),
(gradient.consider_constant(x), T.constant(0.0)),
(x**2 * gradient.consider_constant(x), 2 * x**2),
]
for expr, expr_grad in expressions_gradients:
g = gradient.grad(expr.sum(), x)
# gradient according to theano
f = theano.function([x], g, on_unused_input="ignore")
# desired gradient
f2 = theano.function([x], expr_grad, on_unused_input="ignore")
assert np.allclose(f(a), f2(a))
def test_grad(self):
T = theano.tensor
a = np.asarray(self.rng.randn(5, 5), dtype=config.floatX)
x = T.matrix("x")
expressions_gradients = [
(x * gradient.consider_constant(x), x),
(x * gradient.consider_constant(T.exp(x)), T.exp(x)),
(gradient.consider_constant(x), T.constant(0.0)),
(x ** 2 * gradient.consider_constant(x), 2 * x ** 2),
]
for expr, expr_grad in expressions_gradients:
g = gradient.grad(expr.sum(), x)
# gradient according to theano
f = theano.function([x], g, on_unused_input="ignore")
# desired gradient
f2 = theano.function([x], expr_grad, on_unused_input="ignore")
assert np.allclose(f(a), f2(a))
