原代码：
self.lr = tf.placeholder(shape=[1],dtype=tf.float32,name="learning_rate") 。。。 。。。 optimizer = tf.train.AdamOptimizer(learning_rate=self.lr) self.trainops = optimizer.minimize(self.cost)报错信息：
InvalidArgumentError: Shape must be rank 0 but is rank 1 for 'Adam/update_weight/ApplyAdam' (op: 'ApplyAdam') with input shapes: [39,2], [39,2], [39,2], [], [], [1], [], [], [], [39,2]. During handling of the above exception, another exception occurred: ValueError Traceback (most recent call last) <ipython-input-149-f2a8d1d94ea1> in __init__(self) 29 30 optimizer = tf.train.AdamOptimizer(learning_rate=self.lr) ---> 31 self.trainops = optimizer.minimize(self.cost)出错原因：自定义的变量self.lr的shape为[1]，即vector（rank 1 tesnsor），而tf.train.AdamOptimizer中learning_rate只接受scalar（rank 0 tensor）。
修改为：
self.lr = tf.placeholder(dtype=tf.float32,name="learning_rate") 。。。 。。。 optimizer = tf.train.AdamOptimizer(learning_rate=self.lr) self.trainops = optimizer.minimize(self.cost)参考：https://stackoverflow.com/questions/45733581/tensorflow-optimizer-minimize-valueerror-shape-must-be-rank-0-but-is-rank
