本文整理匯總了Python中cntk.greater_equal方法的典型用法代碼示例。如果您正苦於以下問題：Python cntk.greater_equal方法的具體用法？Python cntk.greater_equal怎麽用？Python cntk.greater_equal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cntk的用法示例。
在下文中一共展示了cntk.greater_equal方法的2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: greater_equal
# 需要導入模塊: import cntk [as 別名]
# 或者: from cntk import greater_equal [as 別名]
def greater_equal(x, y):
return C.greater_equal(x, y) 示例2: create_detection_losses
# 需要導入模塊: import cntk [as 別名]
# 或者: from cntk import greater_equal [as 別名]
def create_detection_losses(cls_score, label_targets, rois, bbox_pred, bbox_targets, bbox_inside_weights):
# classification loss
cls_loss = cross_entropy_with_softmax(cls_score, label_targets, axis=1)
p_cls_loss = placeholder()
p_rois = placeholder()
# The terms that are accounted for in the cls loss are those that correspond to an actual roi proposal --> do not count no-op (all-zero) rois
roi_indicator = reduce_sum(p_rois, axis=1)
cls_num_terms = reduce_sum(cntk.greater_equal(roi_indicator, 0.0))
cls_normalization_factor = 1.0 / cls_num_terms
normalized_cls_loss = reduce_sum(p_cls_loss) * cls_normalization_factor
reduced_cls_loss = cntk.as_block(normalized_cls_loss,
[(p_cls_loss, cls_loss), (p_rois, rois)],
'Normalize', 'norm_cls_loss')
# regression loss
p_bbox_pred = placeholder()
p_bbox_targets = placeholder()
p_bbox_inside_weights = placeholder()
bbox_loss = SmoothL1Loss(cfg["CNTK"].SIGMA_DET_L1, p_bbox_pred, p_bbox_targets, p_bbox_inside_weights, 1.0)
# The bbox loss is normalized by the batch size
bbox_normalization_factor = 1.0 / cfg["TRAIN"].BATCH_SIZE
normalized_bbox_loss = reduce_sum(bbox_loss) * bbox_normalization_factor
reduced_bbox_loss = cntk.as_block(normalized_bbox_loss,
[(p_bbox_pred, bbox_pred), (p_bbox_targets, bbox_targets), (p_bbox_inside_weights, bbox_inside_weights)],
'SmoothL1Loss', 'norm_bbox_loss')
detection_losses = plus(reduced_cls_loss, reduced_bbox_loss, name="detection_losses")
return detection_losses