Comments (3)
I'm sure there's a problem with the procedure. The gradient will explode with more training times, and the loss will become super-large. I trained twice, once in 14,000 explosions and once in 18,000 explosions.
from edsr-tensorflow.
Using exponential attenuation to change learning rate can solve the above problems, and can bring the advantage of accuracy. The loss value is reduced from the original minimum of 1.2 to about 0.4, and the PSNR is increased from 45 to 50.
The parameters are set as follows
LEARNING_RATE_BASE = 0.001 # 最初学习率
LEARNING_RATE_DECAY = 0.99 # 学习率的衰减率
LEARNING_RATE_STEP = 100 # 喂入多少轮BATCH-SIZE以后,更新一次学习率。一般为总样本数量/BATCH_SIZE
gloabl_steps = tf.Variable(0, trainable=False) # 计数器,用来记录运行了几轮的BATCH_SIZE,初始为0,设置为不可训练
learning_rate = tf.train.exponential_decay(LEARNING_RATE_BASE
, gloabl_steps,
LEARNING_RATE_STEP,
LEARNING_RATE_DECAY,
staircase=True)
from edsr-tensorflow.
学习率可以解决上述问题,并可以带来准确性的优势。损耗值从原来的最小值1.2减小到大约0.4,PSNR
请问在哪里修改学习率啊
from edsr-tensorflow.
Related Issues (20)
- error in train.py HOT 4
- about training HOT 1
- I've implemented a Keras version of EDSR HOT 1
- training problem for tensorflow HOT 1
- What are the changes compared to the original paper? HOT 3
- test error HOT 2
- test_size in load_dataset
- train error HOT 3
- train error HOT 7
- ResourceExhaustedError HOT 1
- after 1 epoch, you don't shuffle? HOT 6
- train_setOnly 200
- How to control the training epochs in the codes ? HOT 2
- test error HOT 2
- Did you use the GAN structure? HOT 5
- how to training MDSR HOT 2
- test error
- out of range HOT 4
- Versions?
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