tf生成随机数
import tensorflow as tf sess = tf.InteractiveSession() ### 生成符合正态分布的随机值 # tf.random_normal(shape, mean, stddev, dtype, seed, name) a = tf.random_normal([2, 3], name='a') print(a.eval()) # [[-1.2077953 -0.69333565 -0.10252991] # [ 0.51914424 0.7754795 -0.02618051]] ### 生成截断的正态分布的随机值 ## 只保留[mean - 2stddev, mean + 2stddev]内的随机数 # tf.truncated_normal(shape, mean, stddev, dtype, seed, name) b = tf.truncated_normal([2, 3], name='b') print(b.eval()) # [[-1.8038174 1.521785 0.33182728] # [ 1.0274183 -0.39916983 -0.50485927]] ### 生成均匀分布的随机数 # tf.random_uniform(shape, minval, maxval, dtype, seed, name) c = tf.random_uniform([2, 3], name='c') print(c.eval()) # [[0.6636964 0.20990396 0.44687605] # [0.64548564 0.22155988 0.19247997]] ### 按行乱序 # tf.random_shuffle(value, dtype, name) d = tf.random_shuffle([[1, 2], [3, 2]], name='d') print(d.eval())