本文整理匯總了Python中tensorflow.python.ops.math_ops.segment_sum方法的典型用法代碼示例。如果您正苦於以下問題：Python math_ops.segment_sum方法的具體用法？Python math_ops.segment_sum怎麽用？Python math_ops.segment_sum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.math_ops的用法示例。
在下文中一共展示了math_ops.segment_sum方法的8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: _SegmentMinOrMaxGrad
# 需要導入模塊: from tensorflow.python.ops import math_ops [as 別名]
# 或者: from tensorflow.python.ops.math_ops import segment_sum [as 別名]
def _SegmentMinOrMaxGrad(op, grad, is_sorted):
"""Gradient for SegmentMin and (unsorted) SegmentMax. They share similar code."""
zeros = array_ops.zeros(array_ops.shape(op.inputs[0]),
dtype=op.inputs[0].dtype)
# Get the number of selected (minimum or maximum) elements in each segment.
gathered_outputs = array_ops.gather(op.outputs[0], op.inputs[1])
is_selected = math_ops.equal(op.inputs[0], gathered_outputs)
if is_sorted:
num_selected = math_ops.segment_sum(math_ops.cast(is_selected, grad.dtype),
op.inputs[1])
else:
num_selected = math_ops.unsorted_segment_sum(math_ops.cast(is_selected, grad.dtype),
op.inputs[1], op.inputs[2])
# Compute the gradient for each segment. The gradient for the ith segment is
# divided evenly among the selected elements in that segment.
weighted_grads = math_ops.div(grad, num_selected)
gathered_grads = array_ops.gather(weighted_grads, op.inputs[1])
if is_sorted:
return array_ops.where(is_selected, gathered_grads, zeros), None
else:
return array_ops.where(is_selected, gathered_grads, zeros), None, None 示例2: _linear_predictions
# 需要導入模塊: from tensorflow.python.ops import math_ops [as 別名]
# 或者: from tensorflow.python.ops.math_ops import segment_sum [as 別名]
def _linear_predictions(self, examples):
"""Returns predictions of the form w*x."""
with name_scope('sdca/prediction'):
sparse_variables = self._convert_n_to_tensor(self._variables[
'sparse_features_weights'])
result = 0.0
for sfc, sv in zip(examples['sparse_features'], sparse_variables):
# TODO(sibyl-Aix6ihai): following does not take care of missing features.
result += math_ops.segment_sum(
math_ops.multiply(
array_ops.gather(sv, sfc.feature_indices), sfc.feature_values),
sfc.example_indices)
dense_features = self._convert_n_to_tensor(examples['dense_features'])
dense_variables = self._convert_n_to_tensor(self._variables[
'dense_features_weights'])
for i in range(len(dense_variables)):
result += math_ops.matmul(dense_features[i],
array_ops.expand_dims(dense_variables[i], -1))
# Reshaping to allow shape inference at graph construction time.
return array_ops.reshape(result, [-1]) 示例3: _SegmentMinOrMaxGrad
# 需要導入模塊: from tensorflow.python.ops import math_ops [as 別名]
# 或者: from tensorflow.python.ops.math_ops import segment_sum [as 別名]
def _SegmentMinOrMaxGrad(op, grad):
"""Gradient for SegmentMin and SegmentMax. Both share the same code."""
zeros = array_ops.zeros(
array_ops.shape(op.inputs[0]), dtype=op.inputs[0].dtype)
# Get the number of selected (minimum or maximum) elements in each segment.
gathered_outputs = array_ops.gather(op.outputs[0], op.inputs[1])
is_selected = math_ops.equal(op.inputs[0], gathered_outputs)
num_selected = math_ops.segment_sum(
math_ops.cast(is_selected, grad.dtype), op.inputs[1])
# Compute the gradient for each segment. The gradient for the ith segment is
# divided evenly among the selected elements in that segment.
weighted_grads = math_ops.div(grad, num_selected)
gathered_grads = array_ops.gather(weighted_grads, op.inputs[1])
return array_ops.where(is_selected, gathered_grads, zeros), None 示例4: _SegmentMinOrMaxGrad
# 需要導入模塊: from tensorflow.python.ops import math_ops [as 別名]
# 或者: from tensorflow.python.ops.math_ops import segment_sum [as 別名]
def _SegmentMinOrMaxGrad(op, grad):
"""Gradient for SegmentMin and SegmentMax. Both share the same code."""
zeros = array_ops.zeros(array_ops.shape(op.inputs[0]),
dtype=op.inputs[0].dtype)
# Get the number of selected (minimum or maximum) elements in each segment.
gathered_outputs = array_ops.gather(op.outputs[0], op.inputs[1])
is_selected = math_ops.equal(op.inputs[0], gathered_outputs)
num_selected = math_ops.segment_sum(math_ops.cast(is_selected, grad.dtype),
op.inputs[1])
# Compute the gradient for each segment. The gradient for the ith segment is
# divided evenly among the selected elements in that segment.
weighted_grads = math_ops.div(grad, num_selected)
gathered_grads = array_ops.gather(weighted_grads, op.inputs[1])
return math_ops.select(is_selected, gathered_grads, zeros), None 示例5: _linear_predictions
# 需要導入模塊: from tensorflow.python.ops import math_ops [as 別名]
# 或者: from tensorflow.python.ops.math_ops import segment_sum [as 別名]
def _linear_predictions(self, examples):
"""Returns predictions of the form w*x."""
with name_scope('sdca/prediction'):
sparse_variables = self._convert_n_to_tensor(self._variables[
'sparse_features_weights'])
result = 0.0
for sfc, sv in zip(examples['sparse_features'], sparse_variables):
# TODO(sibyl-Aix6ihai): following does not take care of missing features.
result += math_ops.segment_sum(
math_ops.mul(
array_ops.gather(sv, sfc.feature_indices), sfc.feature_values),
sfc.example_indices)
dense_features = self._convert_n_to_tensor(examples['dense_features'])
dense_variables = self._convert_n_to_tensor(self._variables[
'dense_features_weights'])
for i in range(len(dense_variables)):
result += math_ops.matmul(dense_features[i], array_ops.expand_dims(
dense_variables[i], -1))
# Reshaping to allow shape inference at graph construction time.
return array_ops.reshape(result, [-1]) 示例6: _SegmentMinOrMaxGrad
# 需要導入模塊: from tensorflow.python.ops import math_ops [as 別名]
# 或者: from tensorflow.python.ops.math_ops import segment_sum [as 別名]
def _SegmentMinOrMaxGrad(op, grad, is_sorted):
"""Gradient for SegmentMin and (unsorted) SegmentMax. They share similar code."""
zeros = array_ops.zeros(array_ops.shape(op.inputs[0]),
dtype=op.inputs[0].dtype)
# Get the number of selected (minimum or maximum) elements in each segment.
gathered_outputs = array_ops.gather(op.outputs[0], op.inputs[1])
is_selected = math_ops.equal(op.inputs[0], gathered_outputs)
if is_sorted:
num_selected = math_ops.segment_sum(math_ops.cast(is_selected, grad.dtype),
op.inputs[1])
else:
num_selected = math_ops.unsorted_segment_sum(
math_ops.cast(is_selected, grad.dtype), op.inputs[1], op.inputs[2])
# Compute the gradient for each segment. The gradient for the ith segment is
# divided evenly among the selected elements in that segment.
weighted_grads = math_ops.div(grad, num_selected)
gathered_grads = array_ops.gather(weighted_grads, op.inputs[1])
if is_sorted:
return array_ops.where(is_selected, gathered_grads, zeros), None
else:
return array_ops.where(is_selected, gathered_grads, zeros), None, None 開發者ID:PacktPublishing，項目名稱:Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda，代碼行數:26，代碼來源:math_grad.py
示例7: _SegmentMeanGrad
# 需要導入模塊: from tensorflow.python.ops import math_ops [as 別名]
# 或者: from tensorflow.python.ops.math_ops import segment_sum [as 別名]
def _SegmentMeanGrad(op, grad):
"""Gradient for SegmentMean."""
input_rank = array_ops.rank(op.inputs[0])
ones_shape = array_ops.concat([
array_ops.shape(op.inputs[1]),
array_ops.fill(array_ops.expand_dims(input_rank - 1, 0), 1)
], 0)
ones = array_ops.fill(ones_shape,
constant_op.constant(1, dtype=grad.dtype))
scaled_grad = math_ops.div(grad, math_ops.segment_sum(ones, op.inputs[1]))
return array_ops.gather(scaled_grad, op.inputs[1]), None 示例8: _SegmentMeanGrad
# 需要導入模塊: from tensorflow.python.ops import math_ops [as 別名]
# 或者: from tensorflow.python.ops.math_ops import segment_sum [as 別名]
def _SegmentMeanGrad(op, grad):
"""Gradient for SegmentMean."""
input_rank = array_ops.rank(op.inputs[0])
ones_shape = array_ops.concat(
0, [array_ops.shape(op.inputs[1]),
array_ops.fill(array_ops.expand_dims(input_rank - 1, 0), 1)])
ones = array_ops.fill(ones_shape,
constant_op.constant(1, dtype=grad.dtype))
scaled_grad = math_ops.div(grad, math_ops.segment_sum(ones, op.inputs[1]))
return array_ops.gather(scaled_grad, op.inputs[1]), None