本文整理匯總了Python中tensorflow.python.ops.math_ops.argmin方法的典型用法代碼示例。如果您正苦於以下問題：Python math_ops.argmin方法的具體用法？Python math_ops.argmin怎麽用？Python math_ops.argmin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.argmin方法的5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: argmin
# 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 argmin [as 別名]
def argmin(x, axis=-1):
"""Returns the index of the minimum value along an axis.
Arguments:
x: Tensor or variable.
axis: axis along which to perform the reduction.
Returns:
A tensor.
"""
axis = _normalize_axis(axis, ndim(x))
return math_ops.argmin(x, axis) 示例2: get_cluster_assignment
# 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 argmin [as 別名]
def get_cluster_assignment(pairwise_distances, centroid_ids):
"""Assign data points to the neareset centroids.
Tensorflow has numerical instability and doesn't always choose
the data point with theoretically zero distance as it's nearest neighbor.
Thus, for each centroid in centroid_ids, explicitly assign
the centroid itself as the nearest centroid.
This is done through the mask tensor and the constraint_vect tensor.
Args:
pairwise_distances: 2-D Tensor of pairwise distances.
centroid_ids: 1-D Tensor of centroid indices.
Returns:
y_fixed: 1-D tensor of cluster assignment.
"""
predictions = math_ops.argmin(
array_ops.gather(pairwise_distances, centroid_ids), dimension=0)
batch_size = array_ops.shape(pairwise_distances)[0]
# Deal with numerical instability
mask = math_ops.reduce_any(array_ops.one_hot(
centroid_ids, batch_size, True, False, axis=-1, dtype=dtypes.bool),
axis=0)
constraint_one_hot = math_ops.multiply(
array_ops.one_hot(centroid_ids,
batch_size,
array_ops.constant(1, dtype=dtypes.int64),
array_ops.constant(0, dtype=dtypes.int64),
axis=0,
dtype=dtypes.int64),
math_ops.cast(math_ops.range(array_ops.shape(centroid_ids)[0]),
dtypes.int64))
constraint_vect = math_ops.reduce_sum(
array_ops.transpose(constraint_one_hot), axis=0)
y_fixed = array_ops.where(mask, constraint_vect, predictions)
return y_fixed 示例3: get_cluster_assignment
# 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 argmin [as 別名]
def get_cluster_assignment(pairwise_distances, centroid_ids):
"""Assign data points to the neareset centroids.
Tensorflow has numerical instability and doesn't always choose
the data point with theoretically zero distance as it's nearest neighbor.
Thus, for each centroid in centroid_ids, explicitly assign
the centroid itself as the nearest centroid.
This is done through the mask tensor and the constraint_vect tensor.
Args:
pairwise_distances: 2-D Tensor of pairwise distances.
centroid_ids: 1-D Tensor of centroid indices.
Returns:
y_fixed: 1-D tensor of cluster assignment.
"""
predictions = math_ops.argmin(
array_ops.gather(pairwise_distances, centroid_ids), dimension=0)
batch_size = array_ops.shape(pairwise_distances)[0]
# Deal with numerical instability
mask = math_ops.reduce_any(array_ops.one_hot(
centroid_ids, batch_size, True, False, axis=-1, dtype=dtypes.bool),
axis=0)
constraint_one_hot = math_ops.multiply(
array_ops.one_hot(centroid_ids,
batch_size,
array_ops.constant(1, dtype=dtypes.int64),
array_ops.constant(0, dtype=dtypes.int64),
axis=0,
dtype=dtypes.int64),
math_ops.to_int64(math_ops.range(array_ops.shape(centroid_ids)[0])))
constraint_vect = math_ops.reduce_sum(
array_ops.transpose(constraint_one_hot), axis=0)
y_fixed = array_ops.where(mask, constraint_vect, predictions)
return y_fixed 示例4: argmin
# 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 argmin [as 別名]
def argmin(x, axis=-1):
"""Returns the index of the minimum value along an axis.
Arguments:
x: Tensor or variable.
axis: axis along which to perform the reduction.
Returns:
A tensor.
"""
return math_ops.argmin(x, axis) 開發者ID:PacktPublishing，項目名稱:Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda，代碼行數:13，代碼來源:backend.py
示例5: _compute_recall_at_precision
# 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 argmin [as 別名]
def _compute_recall_at_precision(tp, fp, fn, precision, name,
strict_mode=False):
"""Helper function to compute recall at a given `precision`.
Args:
tp: The number of true positives.
fp: The number of false positives.
fn: The number of false negatives.
precision: The precision for which the recall will be calculated.
name: An optional variable_scope name.
strict_mode: If true and there exists a threshold where the precision is no
smaller than the target precision, return the corresponding recall at the
threshold. Otherwise, return 0. If false, find the threshold where the
precision is closest to the target precision and return the recall at the
threshold.
Returns:
The recall at a given `precision`.
"""
precisions = math_ops.div(tp, tp + fp + _EPSILON)
if not strict_mode:
tf_index = math_ops.argmin(
math_ops.abs(precisions - precision), 0, output_type=dtypes.int32)
# Now, we have the implicit threshold, so compute the recall:
return math_ops.div(tp[tf_index], tp[tf_index] + fn[tf_index] + _EPSILON,
name)
else:
# We aim to find the threshold where the precision is minimum but no smaller
# than the target precision.
# The rationale:
# 1. Compute the difference between precisions (by different thresholds) and
# the target precision.
# 2. Take the reciprocal of the values by the above step. The intention is
# to make the positive values rank before negative values and also the
# smaller positives rank before larger positives.
tf_index = math_ops.argmax(
math_ops.div(1.0, precisions - precision + _EPSILON),
0,
output_type=dtypes.int32)
def _return_good_recall():
return math_ops.div(tp[tf_index], tp[tf_index] + fn[tf_index] + _EPSILON,
name)
return control_flow_ops.cond(precisions[tf_index] >= precision,
_return_good_recall, lambda: .0)