def test_log1msigm_to_softplus(self):
x = T.matrix()
out = T.log(1 - sigmoid(x))
f = theano.function([x], out, mode=self.m)
topo = f.maker.fgraph.toposort()
assert len(topo) == 2
assert isinstance(topo[0].op.scalar_op,
theano.tensor.nnet.sigm.ScalarSoftplus)
assert isinstance(topo[1].op.scalar_op, theano.scalar.Neg)
f(numpy.random.rand(54, 11).astype(config.floatX))
# Same test with a flatten
out = T.log(1 - T.flatten(sigmoid(x)))
f = theano.function([x], out, mode=self.m)
assert hasattr(f.maker.fgraph.outputs[0].tag, 'trace')
topo = f.maker.fgraph.toposort()
assert len(topo) == 3
assert tensor.is_flat(topo[0].outputs[0])
assert isinstance(topo[1].op.scalar_op,
theano.tensor.nnet.sigm.ScalarSoftplus)
assert isinstance(topo[2].op.scalar_op, theano.scalar.Neg)
f(numpy.random.rand(54, 11).astype(config.floatX))
# Same test with a reshape
out = T.log(1 - sigmoid(x).reshape([x.size]))
f = theano.function([x], out, mode=self.m)
topo = f.maker.fgraph.toposort()
#assert len(topo) == 3
assert any(isinstance(node.op, T.Reshape) for node in topo)
assert any(isinstance(getattr(node.op, 'scalar_op', None),
theano.tensor.nnet.sigm.ScalarSoftplus)
for node in topo)
f(numpy.random.rand(54, 11).astype(config.floatX))
def test_log1msigm_to_softplus(self):
x = T.matrix()
out = T.log(1 - sigmoid(x))
f = theano.function([x], out, mode=self.m)
topo = f.maker.fgraph.toposort()
assert len(topo) == 2
assert isinstance(topo[0].op.scalar_op,
theano.tensor.nnet.sigm.ScalarSoftplus)
assert isinstance(topo[1].op.scalar_op, theano.scalar.Neg)
f(numpy.random.rand(54, 11).astype(config.floatX))
# Same test with a flatten
out = T.log(1 - T.flatten(sigmoid(x)))
f = theano.function([x], out, mode=self.m)
assert hasattr(f.maker.fgraph.outputs[0].tag, 'trace')
topo = f.maker.fgraph.toposort()
assert len(topo) == 3
assert tensor.is_flat(topo[0].outputs[0])
assert isinstance(topo[1].op.scalar_op,
theano.tensor.nnet.sigm.ScalarSoftplus)
assert isinstance(topo[2].op.scalar_op, theano.scalar.Neg)
f(numpy.random.rand(54, 11).astype(config.floatX))
# Same test with a reshape
out = T.log(1 - sigmoid(x).reshape([x.size]))
f = theano.function([x], out, mode=self.m)
topo = f.maker.fgraph.toposort()
#assert len(topo) == 3
assert any(isinstance(node.op, T.Reshape) for node in topo)
assert any(
isinstance(getattr(node.op, 'scalar_op', None),
theano.tensor.nnet.sigm.ScalarSoftplus)
for node in topo)
f(numpy.random.rand(54, 11).astype(config.floatX))