从均匀分布中输出随机值。生成的值在该 [minval, maxval) 范围内遵循均匀分布.下限 minval 包含在范围内,而上限 maxval 被排除在外。...
操作的名称(可选) 示例 import tensorflow as tf import numpy as np with tf.Session() as sess: print(sess.run(tf.random_uniform
tf.random_uniform 函数 random_uniform(shape, minval=0, maxval...可能引发的异常: ValueError:如果 dtype 是整数并且 maxval 没有被指定. ---- tf.random_uniform((5, 5), minval=low,maxval=high...例如: import tensorflow as tf import numpy as np with tf.Session() as sess: print(sess.run(tf.random_uniform
tf.random_uniform()简介 tf.random_uniform( shape, minval=0, maxval=None, dtype=tf.float32, seed=None..., name=None ) tf.random_uniform([4,4], minval=-10,maxval=10,dtype=tf.float32)))返回4*4的矩阵,产生于-10和10...([], -10, 10, seed=2) d = tf.random_uniform([], -10, 10, seed=2) with tf.Session() as sess: print...([], -10, 10, seed=1) d = tf.random_uniform([], -10, 10, seed=2) with tf.Session() as sess: print...([], -10, 10) d = tf.random_uniform([], -10, 10) with tf.Session() as sess: print(sess.run(c)) #
sess.run(norm)) 结果: [[ 1.89490759 -1.03072059 0.2172989 ] [-0.29377019 -0.38990787 -1.09539473]] tf.random_uniform...产生均匀分布 格式:tf.random_uniform(shape, minval=0.0, maxval=1.0, dtype=tf.float32, seed=None, name=None)...tensor of shape [2, 3] consisting of random uniform values, with minval=1 # and maxval =3. norm = tf.random_uniform...格式:tf.set_random_seed(seed) seed是给定的种子 例子: import tensorflow as tf tf.set_random_seed(1234) a = tf.random_uniform
1.2659812 0.42991444 -1.09538388] [-0.49309424 0.65165377 1.05139613 1.37237358 2.1126318 ]] tf.random_uniform...tf.random_uniform (shape, minval=0, maxval=None, dtype=tf.float32, seed=None, name=None) 在输出 浮点型定域随机值...时,等同于 np.random.uniform;区别在于, tf.random_uniform 还可以输出 整型定域随机值。...) print n_1 import random n_2 = random.uniform(a=0, b=10) print n_2 import tensorflow as tf t_1 = tf.random_uniform...(shape=[2, 5], minval=0, maxval=10, dtype=tf.float32) t_2 = tf.random_uniform(shape=[2, 5], minval=0,
为了说明用户可见的效果,请考虑以下示例:要跨会话生成不同的序列,既不设置图级别也不设置op级别的seed:a = tf.random_uniform([1])b = tf.random_normal([...sess2.run(b)) # generates 'B4'要为跨会话生成一个可操作的序列,请为op设置seed:为了使所有op产生的随机序列在会话之间是可重复的,请设置一个图级别的seed:a = tf.random_uniform...print(sess2.run(b)) # generates 'B4'为了使所有op产生的随机序列在会话之间是可重复的,请设置一个图级别的seed:tf.set_random_seed(1234)a = tf.random_uniform
2. tf.random_uniform() #均匀分布 tf.random_uniform( shape, minval=0, maxval=None, dtype=...例子: import tensorflow as tf b = tf.random_uniform([2,3]) with tf.Session() as sess: print(sess.run...0.10125887 0.30887377] [0.10988557 0.64894116 0.2683978 ]] 例子二:添加数据范围 import tensorflow as tf b = tf.random_uniform
Distribution Uniform Distribution的训练效果 After 858 Batches (2 Epochs): Validation Accuracy 65.340% -- tf.random_uniform...[0, 1) Loss 64.356 -- tf.random_uniform [0, 1) 设置 UniformDistribution权重的方式 通用的方法是,设置一个0左右的不太小的区间
activation_function=None): wlimit = np.sqrt(6.0 / (in_size + out_size)) Weights = tf.Variable(tf.random_uniform...([in_size, out_size], -wlimit, wlimit)) biases = tf.Variable(tf.random_uniform([out_size], -wlimit...对于权重和Bias,使用了按照论文的特定的初始化方式: wlimit = np.sqrt(6.0 / (in_size + out_size)) Weights = tf.Variable(tf.random_uniform...([in_size, out_size], -wlimit, wlimit)) biases = tf.Variable(tf.random_uniform([out_size], -wlimit
tf.float32, [None, 1]) y = tf.placeholder(tf.float32, [None, 1]) # X->hidden_layer w1 = tf.Variable(tf.random_uniform...wb1 = tf.matmul(x, w1) + b1 layer1 = tf.nn.relu(wb1) # 激励函数 # hidden_layer->output w2 = tf.Variable(tf.random_uniform...tf.float32, [None, 1]) y = tf.placeholder(tf.float32, [None, 1]) # X->hidden_layer w1 = tf.Variable(tf.random_uniform...wb1 = tf.matmul(x, w1) + b1 layer1 = tf.nn.relu(wb1) # 激励函数 # hidden_layer->output w2 = tf.Variable(tf.random_uniform
out_size, activation_function=None): wlimit = np.sqrt(6.0 / (in_size + out_size)) Weights = tf.Variable(tf.random_uniform...([in_size, out_size], -wlimit, wlimit)) biases = tf.Variable(tf.random_uniform([out_size], -wlimit, wlimit...其中,对于权重和Bias,使用了按照论文的特定的初始化方式: wlimit = np.sqrt(6.0 / (in_size + out_size)) Weights = tf.Variable(tf.random_uniform...([in_size, out_size], -wlimit, wlimit)) biases = tf.Variable(tf.random_uniform([out_size], -wlimit, wlimit
name=None) tf.truncated_normal(shape, mean=0.0, stddev=1.0, dtype=tf.float32, seed=None, name=None) tf.random_uniform...linspace") # float32 or float64 v7_1 = tf.range(10, 20, 3) # just int32 # 生成各种随机数据矩阵 v8_1 = tf.Variable(tf.random_uniform...tf.truncated_normal([2, 3], mean=0.0, stddev=1.0, dtype=tf.float32, seed=1234, name="v8_3")) v8_4 = tf.Variable(tf.random_uniform
例如: dataset1 = tf.data.Dataset.from_tensor_slices(tf.random_uniform([4, 10]))print(dataset1.output_types...([4]), tf.random_uniform([4, 100], maxval=100, dtype=tf.int32)))print(dataset2.output_types) # =...dataset = tf.data.Dataset.from_tensor_slices( {"a": tf.random_uniform([4]), "b": tf.random_uniform...datasets with the same structure.training_dataset = tf.data.Dataset.range(100).map( lambda x: x + tf.random_uniform...([4, 10])) dataset2 = tf.data.Dataset.from_tensor_slices((tf.random_uniform([4]), tf.random_uniform([
tensorflow as tf; import numpy as np; input1 = tf.constant([1.0, 2.0, 3.0]) input2 = tf.Variable(tf.random_uniform
= tf.gather(square_error, k_index) # 返回均值 return tf.reduce_mean(square_error) bbox_pred = tf.random_uniform...([2,4],10,100,seed = 100) bbox_target = tf.random_uniform([2,4],15,150,seed = 100) with tf.Session()
3.接下来就是我们的计算图的构建了 首先介绍一些东西: tf.random_uniform(shape, a, b)#用来生成a~b范围内的均匀分布的随机数,其中shape是生成的张量的形状 tf.square...博主也只是略知一二,具体可以去查手册或百度 代码如下,也是有注释的(注意,下面的*,+,-都是张量运算) def train(x_data, y_data): w = tf.Variable(tf.random_uniform
): # 练习1: 构建简单的计算图 input1 = tf.constant([1.0, 2.0, 3.0],name="input1") input2 = tf.Variable(tf.random_uniform...tf.constant([1.0, 2.0, 3.0],name="input1") with tf.name_scope("input2"): input2 = tf.Variable(tf.random_uniform
2.搭建神经网络 Weights = tf.Variable(tf.random_uniform([1], -1.0, 1.0)) biases = tf.Variable(tf.zeros([1]))...as np w = 2 b = 0.5 X = np.random.rand(100).astype('float32') y = X * w + b Weights = tf.Variable(tf.random_uniform
np.dot([0.100, 0.200], x_data) + 0.300 # 构造一个线性模型 # b = tf.Variable(tf.zeros([1])) W = tf.Variable(tf.random_uniform
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