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)
def make_funcs(opt, ntm, total_time, loss_timesteps):
x_tbk = cgt.tensor3("x", fixed_shape=(total_time, opt.b, opt.k))
y_tbp = cgt.tensor3("y", fixed_shape=(total_time, opt.b, opt.p))
loss_timesteps = set(loss_timesteps)
initial_states = make_ntm_initial_states(opt)
params = ntm.get_parameters() + get_parameters(initial_states)
# params = ntm.get_parameters()
lossCE = 0
loss01 = 0
state_arrs = initial_states
for t in xrange(total_time):
tmp = ntm([x_tbk[t]] + state_arrs)
raw_pred = tmp[0]
state_arrs = tmp[1:4]
if t in loss_timesteps:
p_pred = cgt.sigmoid(raw_pred)
ce = bernoulli_crossentropy(
y_tbp[t],
p_pred).sum() # cross-entropy of bernoulli distribution
lossCE = lossCE + ce
loss01 = loss01 + cgt.cast(cgt.equal(y_tbp[t], round01(p_pred)),
cgt.floatX).sum()
lossCE = lossCE / (len(loss_timesteps) * opt.p * opt.b) / np.log(2)
loss01 = loss01 / (len(loss_timesteps) * opt.p * opt.b)
gradloss = cgt.grad(lossCE, params)
flatgrad = flatcat(gradloss)
f_loss = cgt.function([x_tbk, y_tbp], lossCE)
f_loss_and_grad = cgt.function([x_tbk, y_tbp], [lossCE, loss01, flatgrad])
print "number of nodes in computation graph:", core.count_nodes(
[lossCE, loss01, flatgrad])
return f_loss, f_loss_and_grad, params
def make_funcs(opt, ntm, total_time, loss_timesteps):
x_tbk = cgt.tensor3("x", fixed_shape=(total_time, opt.b, opt.k))
y_tbp = cgt.tensor3("y", fixed_shape=(total_time, opt.b, opt.p))
loss_timesteps = set(loss_timesteps)
initial_states = make_ntm_initial_states(opt)
params = ntm.get_parameters() + get_parameters(initial_states)
# params = ntm.get_parameters()
lossCE = 0
loss01 = 0
state_arrs = initial_states
for t in xrange(total_time):
tmp = ntm([x_tbk[t]] + state_arrs)
raw_pred = tmp[0]
state_arrs = tmp[1:4]
if t in loss_timesteps:
p_pred = cgt.sigmoid(raw_pred)
ce = bernoulli_crossentropy(y_tbp[t] , p_pred).sum() # cross-entropy of bernoulli distribution
lossCE = lossCE + ce
loss01 = loss01 + cgt.cast(cgt.equal(y_tbp[t], round01(p_pred)),cgt.floatX).sum()
lossCE = lossCE / (len(loss_timesteps) * opt.p * opt.b) / np.log(2)
loss01 = loss01 / (len(loss_timesteps) * opt.p * opt.b)
gradloss = cgt.grad(lossCE, params)
flatgrad = flatcat(gradloss)
f_loss = cgt.function([x_tbk, y_tbp], lossCE)
f_loss_and_grad = cgt.function([x_tbk, y_tbp], [lossCE, loss01, flatgrad])
print "number of nodes in computation graph:", core.count_nodes([lossCE, loss01, flatgrad])
return f_loss, f_loss_and_grad, params
def zero_one_loss(x, y):
assert x.ndim == 2 and y.ndim in (1, 2) and core.dtype_kind(y.dtype) == 'i'
return cgt.equal(x.argmax(axis=1, keepdims=False), y.flatten())
def zero_one_loss(x, y):
assert x.ndim == 2 and y.ndim in (1,2) and core.dtype_kind(y.dtype)=='i'
return cgt.equal(x.argmax(axis=1,keepdims=False),y.flatten())
