我使用较高级别的Tensorflow层创建了CNN模型，例如
conv1 = tf.layers.conv2d(...)
maxpooling1 = tf.layers.max_pooling2d(...)
conv2 = tf.layers.conv2d(...)
maxpooling2 = tf.layers.max_pooling2d(...)
flatten = tf.layers.flatten(...)
logits = tf.layers.dense(...)
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(...))
optimizer = tf.train.AdadeltaOptimizer(init_lr).minimize(loss)
acc = tf.reduce_mean(...)
该模型已经过良好训练和保存，到目前为止一切都很好。接下来，我想加载此保存的模型，更改学习率，然后继续训练（我知道tensorflow提供了exponential_decay（）函数以允许衰减学习率，在这里我只想完全控制学习率，然后手动进行更改）。为此，我的想法是：
saver = tf.train.import_meta_grah(...)
saver.restore(sess, tf.train.latest_chechpoint(...))
graph = tf.get_default_graph()
inputImg_ = graph.get_tensor_by_name(...) # this is place_holder in model
labels_ = graph.get_tensor_by_name(...) # place_holder in model
logits = graphget_tensor_by_name(...) # output of dense layer
loss = grah.get_tensor_by_name(...) # loss
optimizer = tf.train.AdadeltaOptimizer(new_lr).minimize(loss) # I give it a new learning rate
acc = tf.reduce_mean(...) …