1.1 截断高斯分布 使用举例: tf.truncated_normal_initializer(stddev=0.01) 1.2 xavier初始化(适用于激活函数是sigmoid和tanh) 论文地址: Understanding the difficulty of training deep feedforward neural networks 使用举例: tf.contrib.layers.xavier_initializer(uniform=True, seed=None, dtype=tf.float32) 1. 1...
initializer = tf.compat.v1.truncated_normal_initializer( mean=mean, stddev=stddev, seed=seed, dtype=dtype) weight_one = tf.Variable(initializer(shape_one)) weight_two = tf.Variable(initializer(shape_two)) tf2.x initializer=tf.initializers.truncated_normal(mean=mean,seed=seed,stddev=stddev)weigh...
tf.random_normal(shape,mean=0.0,dtype=tf.float32,): 正态分布,默认均值为0,标准差为1.0,数据类型为float32。 tf.truncated_normal(shape, mean=0.0, stddev=1.0, dtype=tf.float32, ):截断正太分布,到均值的距离超过2倍标准差的随机数将被丢弃,然后重新抽取,直到取得足够数量的随机数为止 seed:是设置抽样...
random_normal_initializer 类 继承自: Initializer 别名: 类tf.initializers.random_normal 类tf.keras.initializers.RandomNormal 类tf.random_normal_initializer 定义在:tensorflow/python/ops/init_ops.py. 请参阅指南:变量>共享变量 用正态分布产生张量的初始化器. 参数: mean:一个python标量或一个标量张量.要生...
truncated_normal_initializer)# 对生成的batch_size=2的数据进行卷积操作,立即获得结果conv = tf.nn.conv2d(input = images, filter = filter, strides = [1,2,2,1], padding = 'VALID')# 用结果的numpy()方法可获得结果的numpy表示,显示其shapeprint(conv.numpy().shape) 运行结果如下,成功获得卷积后...
weights2=tf.Variable(tf.truncated_normal([LAYER1_NODE,OUTPUT_NODE],stddev=0.1)) biases2=tf.Variable(tf.constant(0.1,shape=[OUTPUT_NODE])) # 计算不含滑动平均类的前向传播结果 y=inference(x,None,weights1,biases1,weights2,biases2) # 定义训练轮数及相关的滑动平均类 ...
initial=tf.truncated_normal(shape=shape,mean=0.0,stddev=sdtdev) if name is None: return tf.Variable(initial) else: returntf.get_variable(name=name,initial=initialdefbias_variable(self,shape,name):initial=tf.constant(.01,shape=shape)
( input_dim=new_num_tokens, output_dim=old_embeddings.output_dim, embeddings_initializer=tf.keras.initializers.TruncatedNormal(stddev=init_range), name=old_embeddings.embeddings.name[:-13], # exact same scoped name except "/embeddings:0" ) new_embeddings(tf.constant([[0]])) # Copy the ...
get_variable("Uniform_Matrix", [total_arg_size, output_size], initializer = tf.uniform_unit_scaling_initializer()) elif weight_initializer == "truncated" or weight_initializer == "truncated_uniform": if complex_weights: real_w = tf.Variable(tf.truncated_normal([total_arg_size, output_size...
tf.truncated_normal(shape, mean, stddev) :shape表示生成张量的维度,mean是均值,stddev是标准差。这个函数产生正太分布,均值和标准差自己设定。这是一个截断的产生正太分布的函数,就是说产生正太分布的值如果与均值的差值大于两倍的标准差,那就重新生成。 例: 代码语言:javascript 复制 import tensorflow as tf; ...