def test_local_sigm_times_exp(self):
"""
Test the `local_sigm_times_exp` optimization.
exp(x) * sigm(-x) -> sigm(x)
exp(-x) * sigm(x) -> sigm(-x)
"""
def match(func, ops):
# print [node.op.scalar_op for node in func.maker.fgraph.toposort()]
assert [node.op for node in func.maker.fgraph.toposort()] == ops
m = self.get_mode(excluding=['local_elemwise_fusion', 'inplace'])
x, y = tensor.vectors('x', 'y')
f = theano.function([x], sigmoid(-x) * tensor.exp(x), mode=m)
match(f, [sigmoid])
assert check_stack_trace(f, ops_to_check=sigmoid)
f = theano.function([x], sigmoid(x) * tensor.exp(-x), mode=m)
match(f, [tensor.neg, sigmoid])
assert check_stack_trace(f, ops_to_check=sigmoid)
f = theano.function([x], -(-(-(sigmoid(x)))) * tensor.exp(-x), mode=m)
match(f, [tensor.neg, sigmoid, tensor.neg])
# assert check_stack_trace(f, ops_to_check=sigmoid)
f = theano.function([x, y],
(sigmoid(x) * sigmoid(-y) * -tensor.exp(-x) *
tensor.exp(x * y) * tensor.exp(y)),
mode=m)
topo = f.maker.fgraph.toposort()
for op, nb in [(sigmoid, 2), (tensor.mul, 2), (tensor.neg, 1),
(tensor.exp, 1)]:
assert sum([n.op == op for n in topo]) == nb
def test_compute_mul(self):
x, y, z = tensor.vectors('x', 'y', 'z')
tree = (x * y) * -z
mul_tree = parse_mul_tree(tree)
assert parse_mul_tree(compute_mul(mul_tree)) == mul_tree
assert theano.gof.graph.is_same_graph(
compute_mul(parse_mul_tree(tree)), tree)
def test_compute_mul(self):
x, y, z = tensor.vectors('x', 'y', 'z')
tree = (x * y) * -z
mul_tree = parse_mul_tree(tree)
assert parse_mul_tree(compute_mul(mul_tree)) == mul_tree
assert theano.gof.graph.is_same_graph(
compute_mul(parse_mul_tree(tree)), tree)
def test_local_sigm_times_exp(self):
"""
Test the `local_sigm_times_exp` optimization.
exp(x) * sigm(-x) -> sigm(x)
exp(-x) * sigm(x) -> sigm(-x)
"""
def match(func, ops):
# print [node.op.scalar_op for node in func.maker.fgraph.toposort()]
assert [node.op for node in func.maker.fgraph.toposort()] == ops
m = self.get_mode(excluding=['local_elemwise_fusion', 'inplace'])
x, y = tensor.vectors('x', 'y')
f = theano.function([x], sigmoid(-x) * tensor.exp(x), mode=m)
match(f, [sigmoid])
f = theano.function([x], sigmoid(x) * tensor.exp(-x), mode=m)
match(f, [tensor.neg, sigmoid])
f = theano.function([x], -(-(-(sigmoid(x)))) * tensor.exp(-x), mode=m)
match(f, [tensor.neg, sigmoid, tensor.neg])
f = theano.function(
[x, y],
(sigmoid(x) * sigmoid(-y) * -tensor.exp(-x) *
tensor.exp(x * y) * tensor.exp(y)),
mode=m)
match(f, [sigmoid, tensor.mul, tensor.neg, tensor.exp, sigmoid,
tensor.mul])
def test_local_sigm_times_exp(self):
"""
Test the `local_sigm_times_exp` optimization.
exp(x) * sigm(-x) -> sigm(x)
exp(-x) * sigm(x) -> sigm(-x)
"""
def match(func, ops):
# print [node.op.scalar_op for node in func.maker.fgraph.toposort()]
assert [node.op for node in func.maker.fgraph.toposort()] == ops
m = self.get_mode(excluding=['local_elemwise_fusion', 'inplace'])
x, y = tensor.vectors('x', 'y')
f = theano.function([x], sigmoid(-x) * tensor.exp(x), mode=m)
match(f, [sigmoid])
assert check_stack_trace(f, ops_to_check=sigmoid)
f = theano.function([x], sigmoid(x) * tensor.exp(-x), mode=m)
match(f, [tensor.neg, sigmoid])
assert check_stack_trace(f, ops_to_check=sigmoid)
f = theano.function([x], -(-(-(sigmoid(x)))) * tensor.exp(-x), mode=m)
match(f, [tensor.neg, sigmoid, tensor.neg])
# assert check_stack_trace(f, ops_to_check=sigmoid)
f = theano.function(
[x, y],
(sigmoid(x) * sigmoid(-y) * -tensor.exp(-x) *
tensor.exp(x * y) * tensor.exp(y)), mode=m)
topo = f.maker.fgraph.toposort()
for op, nb in [(sigmoid, 2), (tensor.mul, 2),
(tensor.neg, 1), (tensor.exp, 1)]:
assert sum([n.op == op for n in topo]) == nb
def test_parse_mul_tree(self):
x, y, z = tensor.vectors("x", "y", "z")
assert parse_mul_tree(x * y) == [False, [[False, x], [False, y]]]
assert parse_mul_tree(-(x * y)) == [True, [[False, x], [False, y]]]
assert parse_mul_tree(-x * y) == [False, [[True, x], [False, y]]]
assert parse_mul_tree(-x) == [True, x]
assert parse_mul_tree((x * y) * -z) == [False, [[False, [[False, x], [False, y]]], [True, z]]]
def test_perform_sigm_times_exp(self):
"""
Test the core function doing the `sigm_times_exp` optimization.
It is easier to test different graph scenarios this way than by
compiling a theano function.
"""
x, y, z, t = tensor.vectors("x", "y", "z", "t")
exp = tensor.exp
def ok(expr1, expr2):
trees = [parse_mul_tree(e) for e in (expr1, expr2)]
perform_sigm_times_exp(trees[0])
trees[0] = simplify_mul(trees[0])
good = theano.gof.graph.is_same_graph(compute_mul(trees[0]), compute_mul(trees[1]))
if not good:
print trees[0]
print trees[1]
print "***"
theano.printing.debugprint(compute_mul(trees[0]))
print "***"
theano.printing.debugprint(compute_mul(trees[1]))
assert good
ok(sigmoid(x) * exp(-x), sigmoid(-x))
ok(-x * sigmoid(x) * (y * (-1 * z) * exp(-x)), -x * sigmoid(-x) * (y * (-1 * z)))
ok(
-sigmoid(-x)
* (exp(y) * (-exp(-z) * 3 * -exp(x)) * (y * 2 * (-sigmoid(-y) * (z + t) * exp(z)) * sigmoid(z)))
* -sigmoid(x),
sigmoid(x) * (-sigmoid(y) * (-sigmoid(-z) * 3) * (y * 2 * ((z + t) * exp(z)))) * -sigmoid(x),
)
ok(exp(-x) * -exp(-x) * (-sigmoid(x) * -sigmoid(x)), -sigmoid(-x) * sigmoid(-x))
ok(-exp(x) * -sigmoid(-x) * -exp(-x), -sigmoid(-x))
def test_parse_mul_tree(self):
x, y, z = tensor.vectors('x', 'y', 'z')
assert parse_mul_tree(x * y) == [False, [[False, x], [False, y]]]
assert parse_mul_tree(-(x * y)) == [True, [[False, x], [False, y]]]
assert parse_mul_tree(-x * y) == [False, [[True, x], [False, y]]]
assert parse_mul_tree(-x) == [True, x]
assert parse_mul_tree((x * y) * -z) == [
False, [[False, [[False, x], [False, y]]], [True, z]]]
def test_perform_sigm_times_exp(self):
# Test the core function doing the `sigm_times_exp` optimization.
#
# It is easier to test different graph scenarios this way than by
# compiling a theano function.
x, y, z, t = tensor.vectors("x", "y", "z", "t")
exp = tensor.exp
def ok(expr1, expr2):
trees = [parse_mul_tree(e) for e in (expr1, expr2)]
perform_sigm_times_exp(trees[0])
trees[0] = simplify_mul(trees[0])
good = theano.gof.graph.is_same_graph(
compute_mul(trees[0]), compute_mul(trees[1])
)
if not good:
print(trees[0])
print(trees[1])
print("***")
theano.printing.debugprint(compute_mul(trees[0]))
print("***")
theano.printing.debugprint(compute_mul(trees[1]))
assert good
ok(sigmoid(x) * exp(-x), sigmoid(-x))
ok(
-x * sigmoid(x) * (y * (-1 * z) * exp(-x)),
-x * sigmoid(-x) * (y * (-1 * z)),
)
ok(
-sigmoid(-x)
* (
exp(y)
* (-exp(-z) * 3 * -exp(x))
* (y * 2 * (-sigmoid(-y) * (z + t) * exp(z)) * sigmoid(z))
)
* -sigmoid(x),
sigmoid(x)
* (-sigmoid(y) * (-sigmoid(-z) * 3) * (y * 2 * ((z + t) * exp(z))))
* (-sigmoid(x)),
)
ok(exp(-x) * -exp(-x) * (-sigmoid(x) * -sigmoid(x)), -sigmoid(-x) * sigmoid(-x))
ok(-exp(x) * -sigmoid(-x) * -exp(-x), -sigmoid(-x))
