def make_loss_and_grad(net):
X_b = inps[0] #cgt.matrix(dtype=cgt.floatX)
y_onehot = cgt.matrix(dtype='i4')
outputs = [logprobs]
loss = nn.crossent(outputs[0], y_onehot) / b_size
#gradloss = cgt.grad(loss, params)
gradloss = cgt.grad(loss, param_list)
# XXX use flatcat function
grad = cgt.concatenate([x.flatten() for x in gradloss])
#grad = gradloss
return cgt.make_function([X_b, y_onehot], [loss, grad, logprobs])
def runTest(self):
if cgt.get_config()["backend"] != "python":
cgt.utils.warn("Skipping test -- only works for backend=python")
return
x = cgt.scalar()
with cgt.debug_context() as dbg:
cgt.assert_(cgt.equal(x, 1), "yoyoyo")
cgt.dbg_call(myfunc, x)
print "dbg", dbg.nodes
# cgt.assert_(cgt.equal(x, 2))
f = cgt.make_function([x], [x], dbg=dbg)
f(1)
with self.assertRaises(AssertionError):
f(2)
def runTest(self):
if cgt.get_config()["backend"] != "python":
cgt.utils.warn("Skipping test -- only works for backend=python")
return
x = cgt.scalar()
with cgt.debug_context() as dbg:
cgt.assert_(cgt.equal(x, 1),"yoyoyo")
cgt.dbg_call(myfunc, x)
print "dbg",dbg.nodes
# cgt.assert_(cgt.equal(x, 2))
f = cgt.make_function([x],[x],dbg=dbg)
f(1)
with self.assertRaises(AssertionError):
f(2)
