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...
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 代码运行次数:0 运行 AI代码解释...
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...
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)
truncated_normal(...): 从截断的正态分布中输出随机值。 uniform(...): 从均匀分布中输出随机值。 uniform_candidate_sampler(...): 使用统一的基分布对一组类进行采样。 二、重要的函数 1、tf.random.multinomial 从多项分布中抽取样本。(弃用) 代码语言:javascript 代码运行次数:0 运行 AI代码解释 tf.ran...
truncated_normal([total_arg_size, output_size], stddev=0.1) weight_matrix = tf.Variable(tf.complex(a,a), name="Complex_Weight") Perhaps I'm missing something or not writing the code properly. I have TF 0.8 installed. gradients = tf.gradients(self.average_mean_loss, params) File "/usr...
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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 ...