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a guest Dec 13th, 2018 46 Never
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  1. with tf.name_scope('digit1/'):
  2.   with tf.variable_scope('digit1/'):
  3.     softmax1_weights = tf.get_variable('digit1_w', shape=[num_hidden, num_labels],
  4.                                       initializer=tf.contrib.layers.xavier_initializer())
  5.     softmax1_biases = tf.get_variable('digit1_b', shape=[num_labels],
  6.                                       initializer=tf.random_uniform_initializer(-0.1, 0.1))
  7.  
  8.   tf.summary.histogram('weights', softmax1_weights)
  9.   tf.summary.histogram('bias', softmax1_biases)
  10.  
  11. summary = tf.summary.merge_all()
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