saver = tf.train.Saver(write_version=tf.train.SaverDef.V2) 
saver.save(sess, save_dir+"crfmodel.ckpt", global_step=0) 

saver = tf.train.Saver() 
ckpt = tf.train.get_checkpoint_state(FLAGS.restore_model) 
saver.restore(sess, ckpt.model_checkpoint_path) 

ww, bb = sess.run([self.W,self.b]) 

with tf.name_scope('weights'): 
   self.W = tf.get_variable( 
     shape=[self.feat_size, self.nb_classes], 
     initializer=tf.truncated_normal_initializer(stddev=0.01), 
     name='weights' 
     # ,regularizer=tf.contrib.layers.l1_regularizer(0.1) 
   ) 
 with tf.name_scope('biases'): 
   self.b = tf.get_variable( 
     shape=[self.nb_classes], 
     initializer=tf.truncated_normal_initializer(stddev=0.01), 
     name='bias' 
   ) 

import numpy as np
W_val, b_val = sess.run([weights_tensor, biases_tensor])
np.savetxt("W.csv", W_val, delimiter=",")
np.savetxt("b.csv", b_val, delimiter=",")