tf.greater (x, y, name=None) tf.greater_equal tensor x 是否大于等于 tensor y，返回 bool型 tensor。...tf.greater_equal (x, y, name=None) tf.where 如果 x 和 y 都为 None，则返回 tensor condition 中的 bool值 为 True 的坐标列表
500000 start_learning_rate = 0.0001 decay_steps = 500000 end_learning_rate = 0.0 out_lr = (tf.where(tf.greater_equal...out_global_step-start_decay_step, decay_steps, end_learning_rate, power=1.0),start_learning_rate)) gen_lr = (tf.where(tf.greater_equal...gen_global_step-start_decay_step, decay_steps, end_learning_rate, power=1.0),start_learning_rate)) disc_lr = (tf.where(tf.greater_equal
None) #(3)其他比较 tf.less(x, y, name=None) tf.less_equal(x, y, name=None) tf.greater(x, y, name=None) tf.greater_equal
tf.logical_not tf.logical_or tf.logical_xor 对照op tf.equal tf.not_equal tf.less tf.less_equal tf.greater tf.greater_equal
., 1.1, 0.1):#将recall分为多个区间# 在所有 recall > t对应的precision中找出最大值mask = tf.greater_equal(recall, t)v = tf.reduce_max
scale * z : scale * alpha * tf.nn.elu(z) is_positive = tf.greater_equal(z, 0.0) # tf判断大于等于 return
: Create a filtering mask based on "box_class_scores" by using "threshold". score_thresh=0.4 mask = tf.greater_equal
logits") if config.numClasses == 1: self.predictions = tf.cast(tf.greater_equal
tpr = [] fpr = [] for th in thresholds: # 计算真正例数量 tp_cases = tf.where((tf.greater_equal...tf.equal(labels, 0))) tn = tf.size(tn_cases) # 计算假正例数量 fp_cases = tf.where((tf.greater_equal
例如，让我们定义一个断言来检查所有输入是否为正： assert_op = tf.Assert(tf.reduce_all(tf.greater_equal(x,0)),[x]) 在定义模型时将assert_op
6, 5, 7]]) c = tf.random.uniform((2, 4)) d = a print(tf.equal(a, b)) # >= print(tf.greater_equal
= tf.pow(x, y) z = x @ y # z = tf.matmul(x, y) z = x > y # z = tf.greater(x, y) z = x >= y # z = tf.greater_equal
2),1) #Sum the samples inside the circle tempsum = sess.run(tf.reduce_sum(tf.cast(tf.greater_equal...FLAGS.numsamples,2]) distances=tf.reduce_sum(tf.pow(i,2),1) return (tf.reduce_sum(tf.cast(tf.greater_equal
scale *z :scale * alpha * tf.nn.elu(z) is_positive = tf.greater_equal(z,0.0) return scale * tf.where
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