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Method optimizer

examples/basics/mnist-visualizations.py:107–114  ·  view source on GitHub ↗
(self)

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105 return tf.add_n([wd_cost, cost], name='total_cost')
106
107 def optimizer(self):
108 lr = tf.train.exponential_decay(
109 learning_rate=1e-3,
110 global_step=get_global_step_var(),
111 decay_steps=468 * 10,
112 decay_rate=0.3, staircase=True, name='learning_rate')
113 tf.summary.scalar('lr', lr)
114 return tf.train.AdamOptimizer(lr)
115
116
117def get_data():

Callers

nothing calls this directly

Calls 1

get_global_step_varFunction · 0.85

Tested by

no test coverage detected