def check(self, expected, debug=True):
"""
Core function to perform comparison.
:param expected: A list of tuples (v1, v2, ((g1, o1), ..., (gN, oN)))
with:
- `v1` and `v2` two Variables (the graphs to be compared)
- `gj` a `givens` dictionary to give as input to `is_same_graph`
- `oj` the expected output of `is_same_graph(v1, v2, givens=gj)`
:param debug: If True, then we make sure we are testing both
implementations of `is_same_graph`.
This function also tries to call `is_same_graph` by inverting `v1` and
`v2`, and ensures the output remains the same.
"""
for v1, v2, go in expected:
for gj, oj in go:
r1 = is_same_graph(v1, v2, givens=gj, debug=debug)
assert r1 == oj
r2 = is_same_graph(v2, v1, givens=gj, debug=debug)
assert r2 == oj
def check(self, expected, debug=True):
"""
Core function to perform comparison.
:param expected: A list of tuples (v1, v2, ((g1, o1), ..., (gN, oN)))
with:
- `v1` and `v2` two Variables (the graphs to be compared)
- `gj` a `givens` dictionary to give as input to `is_same_graph`
- `oj` the expected output of `is_same_graph(v1, v2, givens=gj)`
:param debug: If True, then we make sure we are testing both
implementations of `is_same_graph`.
This function also tries to call `is_same_graph` by inverting `v1` and
`v2`, and ensures the output remains the same.
"""
for v1, v2, go in expected:
for gj, oj in go:
r1 = is_same_graph(v1, v2, givens=gj, debug=debug)
assert r1 == oj
r2 = is_same_graph(v2, v1, givens=gj, debug=debug)
assert r2 == oj
def test_saved_inner_graph():
"""Make sure that the original inner graph is saved."""
x = tensor.tensor3()
recurrent = SimpleRecurrent(dim=3, activation=Tanh())
y = recurrent.apply(x)
application_call = get_application_call(y)
assert application_call.inner_inputs
assert application_call.inner_outputs
cg = ComputationGraph(application_call.inner_outputs)
# Check that the inner scan graph is annotated
# with `recurrent.apply`
assert len(VariableFilter(applications=[recurrent.apply])(cg)) == 3
# Check that the inner graph is equivalent to the one
# produced by a stand-alone of `recurrent.apply`
assert is_same_graph(application_call.inner_outputs[0],
recurrent.apply(*application_call.inner_inputs,
iterate=False))