Lines Matching refs:math_ops
47 from tensorflow.python.ops import math_ops
108 f = math_ops.exp(logu) * logu
110 f = math_ops.expm1(alpha * logu) / (alpha * (alpha - 1.))
116 return f + math_ops.expm1(logu)
118 return f - math_ops.expm1(logu)
120 return f - math_ops.expm1(logu) / (alpha - 1.)
274 return math_ops.exp(logu) * logu - (1. + math_ops.exp(logu)) * y
327 return (1. + math_ops.exp(logu)) * y
359 return 0.5 * math_ops.abs(math_ops.expm1(logu))
391 return math_ops.square(math_ops.expm1(logu))
461 return pearson(logu) / (1. + math_ops.exp(logu))
499 fu = math_ops.expm1(t * logu)
501 fu -= t * math_ops.expm1(logu)
502 fu *= array_ops.where(math_ops.logical_and(0. < t, t < 1.),
549 return math_ops.expm1(math_ops.abs(logu))
587 return 0.5 * math_ops.expm1(logu) * logu
619 return math_ops.expm1(2. * logu)
662 y += 0.5 * math_ops.expm1(logu)
711 return math_ops.exp(logu) * csiszar_function(-logu)
983 dotprod = math_ops.reduce_sum(
997 return math_ops.reduce_mean(f_log_avg_u, axis=0) # Avg over batches.
1035 log_n = math_ops.log(math_ops.cast(n, dtype=logu.dtype))
1036 nm1 = math_ops.cast(n - 1, dtype=logu.dtype)
1044 log_max_u = math_ops.reduce_max(logu, axis=0)
1045 log_sum_u_minus_log_max_u = math_ops.reduce_logsumexp(
1059 d_ok = math_ops.not_equal(d, 0.)
1071 is_positive_and_largest = math_ops.logical_and(
1073 math_ops.equal(logu, log_max_u[array_ops.newaxis, ...]))
1074 log_lomsum_u = math_ops.reduce_logsumexp(
1094 looavg_logu = (math_ops.reduce_sum(logu, axis=0) - logu) / nm1
1095 log_soosum_u = math_ops.reduce_logsumexp(
