本文整理汇总了Python中theano.tensor.std方法的典型用法代码示例。如果您正苦于以下问题:Python tensor.std方法的具体用法?Python tensor.std怎么用?Python tensor.std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.std方法的13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: normalize_batch_in_training
# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import std [as 别名]
def normalize_batch_in_training(x, gamma, beta,
reduction_axes, epsilon=1e-3):
"""Computes mean and std for batch then apply batch_normalization on batch.
"""
# TODO remove this if statement when Theano without
# T.nnet.bn.batch_normalization_train is deprecated
if not hasattr(T.nnet.bn, 'batch_normalization_train'):
return _old_normalize_batch_in_training(x, gamma, beta, reduction_axes, epsilon)
if gamma is None:
if beta is None:
gamma = ones_like(x)
else:
gamma = ones_like(beta)
if beta is None:
if gamma is None:
beta = zeros_like(x)
beta = zeros_like(gamma)
normed, mean, stdinv = T.nnet.bn.batch_normalization_train(
x, gamma, beta, reduction_axes, epsilon)
return normed, mean, T.inv(stdinv ** 2)
示例2: truncated_normal
# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import std [as 别名]
def truncated_normal(shape, mean=0.0, stddev=1.0, dtype=None, seed=None):
if dtype is None:
dtype = floatx()
if seed is None:
seed = np.random.randint(1, 10e6)
rng = RandomStreams(seed=seed)
try:
return rng.normal(size=shape, avg=mean, std=stddev, dtype=dtype,
truncate=True)
except TypeError:
normal_t = rng.normal(size=shape, avg=mean, std=stddev, dtype=dtype)
# Poor man's truncated normal: we literally clip the tensor
return T.clip(normal_t, mean - 2 * stddev, mean + 2 * stddev)
# Theano implementation of CTC
# Used with permission from Shawn Tan
# https://github.com/shawntan/
# Note that TensorFlow's native CTC code is significantly
# faster than this
示例3: zca_whiten
# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import std [as 别名]
def zca_whiten(data, W, batchsize=500, use_gpu=True, verbose=True):
data = data.astype(np.float32)
ncases = data.shape[0]
nbatches = (ncases - 1) / batchsize + 1
data_white = np.zeros((ncases, data.shape[1]), dtype=np.float32)
for bidx in range(nbatches):
start = bidx * batchsize
end = min((bidx + 1) * batchsize, ncases)
if use_gpu:
data[start:end] = theano_subtract_m1(data[start:end])
data[start:end] = theano_divide_s1(data[start:end])
data_white[start:end] = theano_dot(data[start:end], W)
else:
data[start:end] -= data[start:end].mean(1)[:, None]
s1 = data[start:end].std(1)[:, None]
data[start:end] /= s1 + s1.mean()
data_white[start:end] = np.dot(data[start:end], W)
return data_white
示例4: whiten
# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import std [as 别名]
def whiten(data, V, m0, s0, var_fracs, retain_var, nprincomps=0, batchsize=1000, use_gpu=True, verbose=True):
data = data.astype(np.float32)
ncases = data.shape[0]
nbatches = (ncases - 1) / batchsize + 1
if nprincomps == 0:
nprincomps = np.where(var_fracs > retain_var)[0][0]
data_white = np.zeros((ncases, nprincomps), dtype=np.float32)
for bidx in range(nbatches):
start = bidx * batchsize
end = min((bidx + 1) * batchsize, ncases)
data[start:end] -= data[start:end].mean(1)[:, None]
s1 = data[start:end].std(1)[:, None]
data[start:end] /= s1 + s1.mean()
data[start:end] -= m0
data[start:end] /= s0
if use_gpu:
data_white[start:end] = theano_dot(
data[start:end], V[:nprincomps].T)
else:
data_white[start:end] = np.dot(data[start:end], V[:nprincomps].T)
return data_white
示例5: truncated_normal
# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import std [as 别名]
def truncated_normal(shape, mean=0.0, stddev=1.0, dtype=None, seed=None):
if dtype is None:
dtype = floatx()
if seed is None:
seed = np.random.randint(1, 10e6)
rng = RandomStreams(seed=seed)
normal_tensor = rng.normal(size=shape, avg=mean, std=stddev, dtype=dtype)
# Poor man's truncated normal: we literally clip the tensor
return T.clip(normal_tensor, mean - 2 * stddev, mean + 2 * stddev)
# Theano implementation of CTC
# Used with permission from Shawn Tan
# https://github.com/shawntan/
# Note that TensorFlow's native CTC code is significantly
# faster than this
示例6: normalize_batch_in_training
# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import std [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7: truncated_normal
# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import std [as 别名]
def truncated_normal(shape, mean=0.0, stddev=1.0, dtype=None, seed=None):
if dtype is None:
dtype = floatx()
if seed is None:
seed = np.random.randint(1, 10e6)
rng = RandomStreams(seed=seed)
normal_tensor = rng.normal(size=shape, avg=mean, std=stddev, dtype=dtype)
# Poor man's truncated normal: we literally clip the tensor
return T.clip(normal_tensor, mean - 2 * stddev, mean + 2 * stddev)
# Theano implementation of CTC
# Used with permission from Shawn Tan
# https://github.com/shawntan/
# Note that tensorflow's native CTC code is significantly
# faster than this
示例8: std
# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import std [as 别名]
def std(x, axis=None, keepdims=False):
return T.std(x, axis=axis, keepdims=keepdims)
示例9: normalize_batch_in_training
# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import std [as 别名]
def normalize_batch_in_training(x, gamma, beta,
reduction_axes, epsilon=1e-3):
'''Computes mean and std for batch then apply batch_normalization on batch.
'''
# TODO remove this if statement when Theano without
# T.nnet.bn.batch_normalization_train is deprecated
if not hasattr(T.nnet.bn, 'batch_normalization_train'):
return _old_normalize_batch_in_training(x, gamma, beta, reduction_axes, epsilon)
normed, mean, stdinv = T.nnet.bn.batch_normalization_train(
x, gamma, beta, reduction_axes, epsilon)
return normed, mean, T.inv(stdinv ** 2)
示例10: _old_normalize_batch_in_training
# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import std [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11: random_normal
# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import std [as 别名]
def random_normal(shape, mean=0.0, std=1.0, dtype=None, seed=None):
if dtype is None:
dtype = floatx()
if seed is None:
seed = np.random.randint(1, 10e6)
rng = RandomStreams(seed=seed)
return rng.normal(size=shape, avg=mean, std=std, dtype=dtype)
示例12: random_normal
# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import std [as 别名]
def random_normal(shape, mean=0.0, std=1.0, dtype=_FLOATX, seed=None):
if seed is None:
seed = np.random.randint(10e6)
rng = RandomStreams(seed=seed)
return rng.normal(size=shape, avg=mean, std=std, dtype=dtype)
示例13: get_stats
# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import std [as 别名]
def get_stats(input, stat=None):
"""
Returns a dictionary mapping the name of the statistic to the result on the input.
Currently gets mean, var, std, min, max, l1, l2.
Parameters
----------
input : tensor
Theano tensor to grab stats for.
Returns
-------
dict
Dictionary of all the statistics expressions {string_name: theano expression}
"""
stats = {
'mean': T.mean(input),
'var': T.var(input),
'std': T.std(input),
'min': T.min(input),
'max': T.max(input),
'l1': input.norm(L=1),
'l2': input.norm(L=2),
#'num_nonzero': T.sum(T.nonzero(input)),
}
stat_list = raise_to_list(stat)
compiled_stats = {}
if stat_list is None:
return stats
for stat in stat_list:
if isinstance(stat, string_types) and stat in stats:
compiled_stats.update({stat: stats[stat]})
return compiled_stats