Norm Decay and the Nuclear Non-Proliferation NormDoyle, Thomas E
L2范数,又叫“岭回归”(Ridge Regression)、“权值衰减”(weight decay)。它的作用是改善过拟合。 L2范数:也称为欧几里得距离(Euclidean distance),定义为向量各个元素的平方和再开根号,即 ||x||2 = √(x1^2 + x2^2 + ... + xn^2) L2范数是指向量中各元素的平方和然后开根。我们让L2范数的规则项||...
在随机优化的理论中,学习率往往设置为常数或者逐渐衰减 (decay),从而保证算法的收敛,这种学习率的设置方法也与机器学习里很多任务上的实际经验类似,例如图像分类、语音识别等。然而,不管是设置学习率为常数还是使学习率逐渐衰减都不能让 Transformer 很好地收敛。 在优化 Transformer 结构时,除了设置初始学习率与它的衰...
在随机优化的理论中,学习率往往设置为常数或者逐渐衰减 (decay),从而保证算法的收敛,这种学习率的设置方法也与机器学习里很多任务上的实际经验类似,例如图像分类、语音识别等。然而,不管是设置学习率为常数还是使学习率逐渐衰减都不能让 Transformer 很好地收敛。 在优化 Transformer 结构时,除了设置初始学习率与它的衰...
ema=tf.train.ExponentialMovingAverage(decay=0.5)defmean_var_with_update():ema_apply_op=ema.apply([batch_mean,batch_var])withtf.control_dependencies([ema_apply_op]):returntf.identity(batch_mean),tf.identity(batch_var)# train_phase 训练还是测试的flag ...
另外,在实现时一般使用一个decay系数来逐步更新moving_mean和moving_variance,moving_mean = moving_mean * decay + new_batch_mean * (1 - decay) 三、tensorflow中的三种实现 tensorflow中关于batch_norm现在有三种实现方式。 1、tf.nn.batch_normalization(最底层的实现)...
ema=tf.train.ExponentialMovingAverage(decay=0.5)defmean_var_with_update():ema_apply_op=ema.apply([batch_mean,batch_var])withtf.control_dependencies([ema_apply_op]):returntf.identity(batch_mean),tf.identity(batch_var)# train_phase 训练还是测试的flag ...
self.weight_decay = weight_decay self.is_training = is_training self.image_size = image_size self.self_attention = self_attention self.residual_block = residual_block self.final_channels = final_channels self.epoch = epoch self.g_blur = g_blur ...
We prove some L2(R)-norm decay estimates of solutions and their higher-order derivatives with respect to the space variable, where the decay rates depend on the number of the present frictional dampings, the regularity of the initial data, and some connections between the speeds of wave ...
defbatch_norm(x,beta,gamma,phase_train,scope='bn',decay=0.9,eps=1e-5):withtf.variable_scope(scope):# beta = tf.get_variable(name='beta', shape=[n_out], initializer=tf.constant_initializer(0.0), trainable=True)# gamma = tf.get_variable(name='gamma', shape=[n_out],# initializer=...