本文整理汇总了Python中theano.compat.six.moves.xrange函数的典型用法代码示例。如果您正苦于以下问题：Python xrange函数的具体用法？Python xrange怎么用？Python xrange使用的例子？那么恭喜您, 这里精选的函数代码示例或许可以为您提供帮助。
在下文中一共展示了xrange函数的3个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: check_padding
def check_padding(axes):
padding = 3
ddata = DummyDataset()
topo = ddata.get_topological_view()
wf_cls = WindowAndFlip
wf = wf_cls(window_shape=(5, 5), randomize=[ddata],
pad_randomized=padding)
wf.setup(None, None, None)
new_topo = ddata.get_topological_view()
assert_equal(topo.shape, new_topo.shape)
saw_padding = dict([((direction, amount), False) for direction, amount
in itertools.product(['l', 'b', 'r', 't'],
xrange(padding))])
iters = 0
while not all(saw_padding.values()) and iters < 50:
for image in new_topo.swapaxes(0, 3):
for i in xrange(padding):
if (image[:i] == 0).all():
saw_padding['t', i] = True
if (image[-i:] == 0).all():
saw_padding['b', i] = True
if (image[:, -i:] == 0).all():
saw_padding['r', i] = True
if (image[:, :i] == 0).all():
saw_padding['l', i] = True
wf.on_monitor(None, None, None)
new_topo = ddata.get_topological_view()
iters += 1示例2: test_mean_H_given_V
def test_mean_H_given_V(self):
tol = 1e-6
# P(h_1 | v) / P(h_2 | v) = a
# => exp(-E(v, h_1)) / exp(-E(v,h_2)) = a
# => exp(E(v,h_2)-E(v,h_1)) = a
# E(v,h_2) - E(v,h_1) = log(a)
# also log P(h_1 | v) - log P(h_2) = log(a)
rng = N.random.RandomState([1, 2, 3])
m = 5
Vv = as_floatX(N.zeros((m, self.nv)) + rng.randn(self.nv))
Hv = as_floatX(rng.randn(m, self.nh) > 0.)
log_Pv = self.log_P_H_given_V_func(Hv, Vv)
Ev = self.E_func(Vv, Hv)
for i in xrange(m):
for j in xrange(i + 1, m):
log_a = log_Pv[i] - log_Pv[j]
e = Ev[j] - Ev[i]
assert abs(e-log_a) < tol示例3: outer
def outer(self, Y, Y_hat):
if self._requires_reshape:
if self._requires_unmask:
try:
Y, Y_mask = Y
Y_hat, Y_hat_mask = Y_hat
except:
log.warning("Lost the mask when wrapping cost. This "
"can happen if this function is called "
"from within another wrapped function. "
"Most likely this won't cause any problem")
return cost(self, Y, Y_hat)
input_shape = ([Y.shape[0] * Y.shape[1]] +
[Y.shape[i] for i in xrange(2, Y.ndim)])
reshaped_Y = Y.reshape(input_shape)
if isinstance(Y_hat, tuple):
input_shape = ([[Y_hat[j].shape[0] * Y_hat[j].shape[1]] +
[Y_hat[j].shape[i]
for i in xrange(2, Y_hat[j].ndim)]
for j in xrange(len(Y_hat))])
reshaped_Y_hat = []
for i in xrange(len(Y_hat)):
reshaped_Y_hat.append(Y_hat[i].reshape(input_shape[i]))
reshaped_Y_hat = tuple(reshaped_Y_hat)
else:
reshaped_Y_hat = Y_hat.reshape(input_shape)
# Here we need to take the indices of only the unmasked data
if self._requires_unmask:
return cost(self, reshaped_Y[Y_mask.flatten().nonzero()],
reshaped_Y_hat[Y_mask.flatten().nonzero()])
return cost(self, reshaped_Y, reshaped_Y_hat)
else: # Not RNN-friendly, but not requiring reshape
return cost(self, Y, Y_hat)