网站建设相关知识博客,济南seo排名优化推广,怎么做整蛊网站,浙江企业在线学习率周期性变化#xff0c;能后解决陷入鞍点的问题#xff0c;更多的方式请参考https://github.com/bckenstler/CLR
base_lr:最低的学习率
max_lr:最高的学习率
step_size#xff1a;#xff08;2-8#xff09;倍的每个epoch的训练次数。
scale_fn(x)#xff1a;自…学习率周期性变化能后解决陷入鞍点的问题更多的方式请参考https://github.com/bckenstler/CLR
base_lr:最低的学习率
max_lr:最高的学习率
step_size2-8倍的每个epoch的训练次数。
scale_fn(x)自定义用来减少最高的学习率。 cycle np.floor(1iterations/(2*step_size)) x np.abs(iterations/step_size - 2*cycle 1) lr base_lr (max_lr-base_lr)*np.maximum(0, (1-x))*scale_fn(x) 一学习率周期性变化
global_stepstf.placeholder(shape[1],dtypetf.int64)
cycle tf.cast(tf.floor(1. tf.cast(global_steps, dtypetf.float32) /(2 * 1000.)), dtypetf.float32)x tf.cast(tf.abs(tf.cast(global_steps, dtypetf.float32) / 1000. - 2. * cycle 1.), dtypetf.float32)learning_rate 1e-6 (1e-3 - 1e-6) * tf.maximum(0., (1 - x))
with tf.Session() as sess:lr_list []cycle_list[]for i in range(8000):lrsess.run(learning_rate,feed_dict{global_steps:[i]})lr_list.append(lr)cl sess.run(cycle, feed_dict{global_steps: [i]})cycle_list.append(cl)plt.plot(lr_list)plt.show()print(cycle_list)
def lr_change(base_lr1e-6, max_lr1e-3, step_size1000.,):lr_list[]for clr_iterations in range(8000):cycle np.floor(1 clr_iterations / (2 * step_size))x np.abs(clr_iterations / step_size - 2 * cycle 1)lrbase_lr (max_lr - base_lr) * np.maximum(0, (1 - x))#/(2 ** (cycle - 1))lr_list.append(lr)plt.plot(lr_list,r)plt.xlabel(iterations)plt.ylabel(learning rate)plt.savefig(./data/learning_rate.jpg)plt.show()二学习率周期性衰减
global_stepstf.placeholder(shape[1],dtypetf.int64)
cycle tf.cast(tf.floor(1. tf.cast(global_steps, dtypetf.float32) /(2 * 1000.)), dtypetf.float32)x tf.cast(tf.abs(tf.cast(global_steps, dtypetf.float32) / 1000. - 2. * cycle 1.), dtypetf.float32)learning_rate 1e-6 (1e-3 - 1e-6) * tf.maximum(0., (1 - x))/tf.cast(2**(cycle-1),dtypetf.float32)
with tf.Session() as sess:lr_list []cycle_list[]for i in range(8000):lrsess.run(learning_rate,feed_dict{global_steps:[i]})lr_list.append(lr)cl sess.run(cycle, feed_dict{global_steps: [i]})cycle_list.append(cl)plt.plot(lr_list)plt.show()print(cycle_list)
def lr_change(base_lr1e-6, max_lr1e-3, step_size1000.,):lr_list[]for clr_iterations in range(8000):cycle np.floor(1 clr_iterations / (2 * step_size))x np.abs(clr_iterations / step_size - 2 * cycle 1)lrbase_lr (max_lr - base_lr) * np.maximum(0, (1 - x))/(2 ** (cycle - 1))lr_list.append(lr)plt.plot(lr_list,r)plt.xlabel(iterations)plt.ylabel(learning rate)plt.savefig(./data/learning_rate.jpg)plt.show()
