本文整理匯總了Python中tensorflow.python.ops.linalg_ops.eye方法的典型用法代碼示例。如果您正苦於以下問題：Python linalg_ops.eye方法的具體用法？Python linalg_ops.eye怎麽用？Python linalg_ops.eye使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類tensorflow.python.ops.linalg_ops的用法示例。
在下文中一共展示了linalg_ops.eye方法的7個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: _to_dense
# 需要導入模塊: from tensorflow.python.ops import linalg_ops [as 別名]
# 或者: from tensorflow.python.ops.linalg_ops import eye [as 別名]
def _to_dense(self):
"""Generic and often inefficient implementation. Override often."""
logging.warn("Using (possibly slow) default implementation of to_dense."
" Converts by self.matmul(identity).")
if self.batch_shape.is_fully_defined():
batch_shape = self.batch_shape
else:
batch_shape = self.batch_shape_tensor()
if self.domain_dimension.value is not None:
n = self.domain_dimension.value
else:
n = self.domain_dimension_tensor()
eye = linalg_ops.eye(num_rows=n, batch_shape=batch_shape, dtype=self.dtype)
return self.matmul(eye) 示例2: _CholeskyGrad
# 需要導入模塊: from tensorflow.python.ops import linalg_ops [as 別名]
# 或者: from tensorflow.python.ops.linalg_ops import eye [as 別名]
def _CholeskyGrad(op, grad):
"""Gradient for Cholesky."""
# Gradient is l^{-H} @ ((l^{H} @ grad) * (tril(ones)-1/2*eye)) @ l^{-1}
l = op.outputs[0]
num_rows = array_ops.shape(l)[-1]
batch_shape = array_ops.shape(l)[:-2]
l_inverse = linalg_ops.matrix_triangular_solve(l,
linalg_ops.eye(
num_rows,
batch_shape=batch_shape,
dtype=l.dtype))
middle = math_ops.matmul(l, grad, adjoint_a=True)
middle = array_ops.matrix_set_diag(middle,
0.5 * array_ops.matrix_diag_part(middle))
middle = array_ops.matrix_band_part(middle, -1, 0)
grad_a = math_ops.matmul(
math_ops.matmul(l_inverse, middle, adjoint_a=True), l_inverse)
grad_a += math_ops.conj(array_ops.matrix_transpose(grad_a))
return grad_a * 0.5 開發者ID:PacktPublishing，項目名稱:Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda，代碼行數:25，代碼來源:linalg_grad.py
示例3: _to_dense
# 需要導入模塊: from tensorflow.python.ops import linalg_ops [as 別名]
# 或者: from tensorflow.python.ops.linalg_ops import eye [as 別名]
def _to_dense(self):
"""Generic and often inefficient implementation. Override often."""
if self.batch_shape.is_fully_defined():
batch_shape = self.batch_shape
else:
batch_shape = self.batch_shape_dynamic()
if self.domain_dimension.value is not None:
n = self.domain_dimension.value
else:
n = self.domain_dimension_dynamic()
eye = linalg_ops.eye(num_rows=n, batch_shape=batch_shape, dtype=self.dtype)
return self.apply(eye) 示例4: __call__
# 需要導入模塊: from tensorflow.python.ops import linalg_ops [as 別名]
# 或者: from tensorflow.python.ops.linalg_ops import eye [as 別名]
def __call__(self, shape, dtype=None, partition_info=None):
del partition_info # unused
assert len(shape) > 2, shape
support = tuple(shape[:-2]) + (1, 1)
indices = [[s // 2 for s in support]]
updates = array_ops.constant([self.gain], dtype=dtype)
kernel = array_ops.scatter_nd(indices, updates, support)
assert shape[-2] == shape[-1], shape
if shape[-1] != 1:
kernel *= linalg_ops.eye(shape[-1], dtype=dtype)
return kernel 示例5: posdef_inv
# 需要導入模塊: from tensorflow.python.ops import linalg_ops [as 別名]
# 或者: from tensorflow.python.ops.linalg_ops import eye [as 別名]
def posdef_inv(tensor, damping):
"""Computes the inverse of tensor + damping * identity."""
identity = linalg_ops.eye(tensor.shape.as_list()[0], dtype=tensor.dtype)
damping = math_ops.cast(damping, dtype=tensor.dtype)
return posdef_inv_functions[POSDEF_INV_METHOD](tensor, identity, damping) 示例6: __call__
# 需要導入模塊: from tensorflow.python.ops import linalg_ops [as 別名]
# 或者: from tensorflow.python.ops.linalg_ops import eye [as 別名]
def __call__(self, shape, dtype=None, partition_info=None):
full_shape = shape if partition_info is None else partition_info.full_shape
if len(full_shape) != 2:
raise ValueError(
"Identity matrix initializer can only be used for 2D matrices.")
if dtype is None:
dtype = self.dtype
initializer = linalg_ops.eye(*full_shape, dtype=dtype)
if partition_info is not None:
initializer = array_ops.slice(initializer, partition_info.var_offset,
shape)
return self.gain * initializer 開發者ID:PacktPublishing，項目名稱:Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda，代碼行數:14，代碼來源:init_ops.py
示例7: build
# 需要導入模塊: from tensorflow.python.ops import linalg_ops [as 別名]
# 或者: from tensorflow.python.ops.linalg_ops import eye [as 別名]
def build(self, input_shape):
channel_axis = self._channel_axis()
input_shape = tensor_shape.TensorShape(input_shape)
num_channels = input_shape.dims[channel_axis].value
if num_channels is None:
raise ValueError('The channel dimension of the inputs to `GDN` '
'must be defined.')
self._input_rank = input_shape.ndims
self.input_spec = input_spec.InputSpec(
ndim=input_shape.ndims, axes={channel_axis: num_channels})
pedestal = array_ops.constant(self._reparam_offset**2, dtype=self.dtype)
beta_bound = array_ops.constant(
(self._beta_min + self._reparam_offset**2)**.5, dtype=self.dtype)
gamma_bound = array_ops.constant(self._reparam_offset, dtype=self.dtype)
def beta_initializer(shape, dtype=None, partition_info=None):
del partition_info # unused
pedestal = array_ops.constant(self._reparam_offset**2, dtype=self.dtype)
return math_ops.sqrt(array_ops.ones(shape, dtype=dtype) + pedestal)
def gamma_initializer(shape, dtype=None, partition_info=None):
del partition_info # unused
assert len(shape) == 2
assert shape[0] == shape[1]
eye = linalg_ops.eye(shape[0], dtype=dtype)
pedestal = array_ops.constant(self._reparam_offset**2, dtype=self.dtype)
return math_ops.sqrt(self._gamma_init * eye + pedestal)
beta = self.add_variable(
'reparam_beta',
shape=[num_channels],
initializer=beta_initializer,
dtype=self.dtype,
trainable=True)
beta = self._lower_bound(beta, beta_bound)
self.beta = math_ops.square(beta) - pedestal
gamma = self.add_variable(
'reparam_gamma',
shape=[num_channels, num_channels],
initializer=gamma_initializer,
dtype=self.dtype,
trainable=True)
gamma = self._lower_bound(gamma, gamma_bound)
self.gamma = math_ops.square(gamma) - pedestal
self.built = True