本文整理汇总了Python中theano.tensor.stack方法的典型用法代码示例。如果您正苦于以下问题:Python tensor.stack方法的具体用法?Python tensor.stack怎么用?Python tensor.stack使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类theano.tensor
的用法示例。
在下文中一共展示了tensor.stack方法的15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: _create_observation_variable
# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import stack [as 别名]
def _create_observation_variable(individual_selections, choices, partsworth):
"""
This function handles creating the PyMC3 observation variables. It also gracefully handles missing observations in individual selections.
`individual_selections` is a Series of the individuals selections made, starting from 0. It can contain NaNs which represent answer was not provided.
`choices` is a DataFrame with a hierarchical index: level=0 enumerates the choices, and level=1 displays the profile at a specific choice.
It's size is (n_questions, n_choices_per_question).
`partsworth` is a slice of PyMC3 matrix. It represents the partsworth variables of a individual. Size is (n_profiles,)
This computes the values exp(partsworth * profile_j) / sum[ exp(partsworth * profile_k ] for all j.
"""
nan_mask = pd.notnull(individual_selections)
return pm.Categorical("Obs_%s" % individual_selections.name,
tt.nnet.softmax(tt.stack([
tt.dot(choice.values, partsworth) for _, choice in choices[nan_mask.values].groupby(axis=1, level=0)
], axis=0).T),
observed=individual_selections[nan_mask.values].values)
示例2: __init__
# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import stack [as 别名]
def __init__(self):
def f(x, u, i, terminal):
if terminal:
ctrl_cost = T.zeros_like(x[..., 0])
else:
ctrl_cost = T.square(u).sum(axis=-1)
# x: (batch_size, 8)
# x[..., 0:4]: qpos
# x[..., 4:8]: qvel, time derivatives of qpos, not used in the cost.
theta = x[..., 0] # qpos[0]: angle of joint 0
phi = x[..., 1] # qpos[1]: angle of joint 1
target_xpos = x[..., 2:4] # qpos[2:4], target x & y coordinate
body1_xpos = 0.1 * T.stack([T.cos(theta), T.sin(theta)], axis=1)
tip_xpos_incr = 0.11 * T.stack([T.cos(phi), T.sin(phi)], axis=1)
tip_xpos = body1_xpos + tip_xpos_incr
delta = tip_xpos - target_xpos
state_cost = T.sqrt(T.sum(delta * delta, axis=-1))
cost = state_cost + ctrl_cost
return cost
super().__init__(f, state_size=8, action_size=2)
示例3: grad
# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import stack [as 别名]
def grad(self, inputs, gout):
(gz,) = gout
is_continuous = [(inputs[i].dtype in tensor.continuous_dtypes)
for i in range(len(inputs))]
if _is_sparse_variable(gz):
gz = dense_from_sparse(gz)
split = tensor.Split(len(inputs))(gz, 1,
tensor.stack(
[x.shape[1]
for x in inputs]))
if not isinstance(split, list):
split = [split]
derivative = [SparseFromDense(self.format)(s) for s in split]
def choose(continuous, derivative):
if continuous:
return derivative
else:
return None
return [choose(c, d) for c, d in zip(is_continuous, derivative)]
示例4: infer_shape
# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import stack [as 别名]
def infer_shape(self, node, in_shapes):
shape_a = in_shapes[0]
n = node.inputs[1]
axis = node.inputs[2]
if len(shape_a) == 1:
return [(n,)]
elif isinstance(axis, tensor.TensorConstant):
out_shape = (list(shape_a[0: axis.data.item()]) + [n] +
list(shape_a[axis.data + 1:]))
else:
l = len(shape_a)
shape_a = tensor.stack(shape_a)
out_shape = tensor.concatenate((shape_a[0: axis], [n],
shape_a[axis + 1:]))
n_splits = [1] * l
out_shape = tensor.split(out_shape, n_splits, l)
out_shape = [a[0] for a in out_shape]
return [out_shape]
示例5: errors4one
# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import stack [as 别名]
def errors4one(self, z, out, weight=None, distLabelType='12C'):
distBins = config.distCutoffs[distLabelType]
label8 = DistanceUtils.LabelsOfOneDistance(config.ContactDefinition, distBins)
label15 = DistanceUtils.LabelsOfOneDistance(config.InteractionLimit, distBins)
z3C = T.cast( T.ge(z, label8), 'int32') + T.cast( T.ge(z, label15), 'int32')
o3C = T.cast( T.ge(out, label8), 'int32') + T.cast( T.ge(out, label15), 'int32')
if weight is not None:
err = T.sum( T.mul(weight, T.neq(o3C, z3C) ) )*1./T.sum(weight)
else:
err = T.mean( T.neq(o3C , z3C) )
## err is s scalar, convert it to a tensor with ndim=1
return T.stack([err] )
## this function returns a vector of errors, the size of this vector is equal to the sum of ValueDims for all the responses
示例6: parameter_stats
# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import stack [as 别名]
def parameter_stats(parameters, algorithm):
vars_ = []
for name, param in parameters.items():
num_elements = numpy.product(param.get_value().shape)
norm = param.norm(2) / num_elements ** 0.5
trained_param = param in algorithm.gradients
if trained_param:
grad_norm = algorithm.gradients[param].norm(2) / num_elements ** 0.5
step_norm = algorithm.steps[param].norm(2) / num_elements ** 0.5
relative_step_norm = step_norm / grad_norm
else:
grad_norm = 0.
step_norm = 0.
relative_step_norm = 0.
stats = tensor.stack(norm, grad_norm, step_norm, relative_step_norm)
stats.name = name + '_stats'
vars_.append(stats)
return vars_
示例7: normalize_batch_in_training
# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import stack [as 别名]
def normalize_batch_in_training(x, gamma, beta,
reduction_axes, epsilon=0.0001):
'''Compute mean and std for batch then apply batch_normalization on batch.
'''
var = x.var(reduction_axes)
mean = x.mean(reduction_axes)
target_shape = []
for axis in range(ndim(x)):
if axis in reduction_axes:
target_shape.append(1)
else:
target_shape.append(x.shape[axis])
target_shape = T.stack(*target_shape)
broadcast_mean = T.reshape(mean, target_shape)
broadcast_var = T.reshape(var, target_shape)
broadcast_beta = T.reshape(beta, target_shape)
broadcast_gamma = T.reshape(gamma, target_shape)
normed = batch_normalization(x, broadcast_mean, broadcast_var,
broadcast_beta, broadcast_gamma,
epsilon)
return normed, mean, var
示例8: new_episode
# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import stack [as 别名]
def new_episode(self, mem):
g, g_updates = theano.scan(fn=self.new_attention_step,
sequences=self.inp_c,
non_sequences=[mem, self.q_q],
outputs_info=T.zeros_like(self.inp_c[0][0]))
if (self.normalize_attention):
g = nn_utils.softmax(g)
e, e_updates = theano.scan(fn=self.new_episode_step,
sequences=[self.inp_c, g],
outputs_info=T.zeros_like(self.inp_c[0]))
e_list = []
for index in range(self.batch_size):
e_list.append(e[self.fact_count_var[index] - 1, :, index])
return T.stack(e_list).dimshuffle((1, 0))
示例9: select_finite_faults
# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import stack [as 别名]
def select_finite_faults(self):
fault_points = T.vertical_stack(T.stack([self.ref_layer_points[0]], axis=0), self.rest_layer_points).T
ctr = T.mean(fault_points, axis=1)
x = fault_points - ctr.reshape((-1, 1))
M = T.dot(x, x.T)
U = T.nlinalg.svd(M)[2]
rotated_x = T.dot(self.x_to_interpolate(), U)
rotated_fault_points = T.dot(fault_points.T, U)
rotated_ctr = T.mean(rotated_fault_points, axis=0)
a_radius = (rotated_fault_points[:, 0].max() - rotated_fault_points[:, 0].min()) / 2 + self.inf_factor[
self.n_surface_op[0] - 1]
b_radius = (rotated_fault_points[:, 1].max() - rotated_fault_points[:, 1].min()) / 2 + self.inf_factor[
self.n_surface_op[0] - 1]
sel = T.lt((rotated_x[:, 0] - rotated_ctr[0]) ** 2 / a_radius ** 2 + (
rotated_x[:, 1] - rotated_ctr[1]) ** 2 / b_radius ** 2,
1)
if "select_finite_faults" in self.verbose:
sel = theano.printing.Print("scalar_field_iter")(sel)
return sel
示例10: set_rest_ref_matrix
# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import stack [as 别名]
def set_rest_ref_matrix(self, number_of_points_per_surface):
ref_positions = T.cumsum(T.concatenate((T.stack([0]), number_of_points_per_surface[:-1] + 1)))
cum_rep = T.cumsum(T.concatenate((T.stack([0]), number_of_points_per_surface)))
ref_points_init = T.zeros((cum_rep[-1], 3))
ref_points_loop, update_ = theano.scan(self.repeat_list,
outputs_info=[ref_points_init],
sequences=[self.surface_points_all[ref_positions],
dict(input=cum_rep, taps=[0, 1])],
non_sequences=[T.as_tensor(3)],
return_list=False)
# ref_points_loop = theano.printing.Print('loop')(ref_points_loop)
ref_points = ref_points_loop[-1]
# ref_points = T.repeat(self.surface_points_all[ref_positions], number_of_points_per_surface, axis=0)
rest_mask = T.ones(T.stack([self.surface_points_all.shape[0]]), dtype='int16')
rest_mask = T.set_subtensor(rest_mask[ref_positions], 0)
rest_mask = T.nonzero(rest_mask)[0]
rest_points = self.surface_points_all[rest_mask]
return [ref_points, rest_points, ref_positions, rest_mask]
示例11: set_nugget_surface_points
# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import stack [as 别名]
def set_nugget_surface_points(self, ref_positions, rest_mask, number_of_points_per_surface):
# ref_nugget = T.repeat(self.nugget_effect_scalar_T[ref_positions], number_of_points_per_surface)
cum_rep = T.cumsum(T.concatenate((T.stack([0]), number_of_points_per_surface)))
ref_nugget_init = T.zeros((cum_rep[-1], 1))
ref_nugget_loop, update_ = theano.scan(self.repeat_list,
outputs_info=[ref_nugget_init],
sequences=[self.nugget_effect_scalar_T[ref_positions],
dict(input=cum_rep, taps=[0, 1])],
non_sequences=[T.as_tensor(1)],
return_list=False)
# ref_nugget_loop = theano.printing.Print('loop')(ref_nugget_loop)
ref_nugget = ref_nugget_loop[-1]
rest_nugget = self.nugget_effect_scalar_T[rest_mask]
nugget_rest_ref = ref_nugget.reshape((1, -1))[0] + rest_nugget
return nugget_rest_ref
示例12: _old_normalize_batch_in_training
# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import stack [as 别名]
def _old_normalize_batch_in_training(x, gamma, beta,
reduction_axes, epsilon=1e-3):
'''Computes mean and std for batch then apply batch_normalization on batch.
'''
dev = theano.config.device
use_cudnn = ndim(x) < 5 and reduction_axes == [0, 2, 3] and (dev.startswith('cuda') or dev.startswith('gpu'))
if use_cudnn:
broadcast_beta = beta.dimshuffle('x', 0, 'x', 'x')
broadcast_gamma = gamma.dimshuffle('x', 0, 'x', 'x')
try:
normed, mean, stdinv = theano.sandbox.cuda.dnn.dnn_batch_normalization_train(
x, broadcast_gamma, broadcast_beta, 'spatial', epsilon)
var = T.inv(stdinv ** 2)
return normed, T.flatten(mean), T.flatten(var)
except AttributeError:
pass
var = x.var(reduction_axes)
mean = x.mean(reduction_axes)
target_shape = []
for axis in range(ndim(x)):
if axis in reduction_axes:
target_shape.append(1)
else:
target_shape.append(x.shape[axis])
target_shape = T.stack(*target_shape)
broadcast_mean = T.reshape(mean, target_shape)
broadcast_var = T.reshape(var, target_shape)
broadcast_beta = T.reshape(beta, target_shape)
broadcast_gamma = T.reshape(gamma, target_shape)
normed = batch_normalization(x, broadcast_mean, broadcast_var,
broadcast_beta, broadcast_gamma,
epsilon)
return normed, mean, var
# TODO remove this if statement when Theano without
# T.nnet.bn.batch_normalization_test is deprecated
示例13: stack
# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import stack [as 别名]
def stack(x):
return T.stack(*x)
示例14: _grad
# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import stack [as 别名]
def _grad(mat):
grad_y = (mat[idx1, :] - mat[idx2, :]) * h
grad_x = (mat[:, idx1] - mat[:, idx2]) * h
# Ret shape: 2x224x224
return T.stack([grad_y, grad_x], axis=0)
示例15: pack
# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import stack [as 别名]
def pack(x):
return T.stack(*x)
# VALUE MANIPULATION