Hello,I found some performance issues.
The first one is in train_nets.py,
dataset = dataset.map(parse_function) was called without num_parallel_calls.
I think it will increase the efficiency of your program if you add this.
The same issues also exist in dataset = dataset.map(parse_function) ,
dataset = dataset.map(parse_function),
dataset = dataset.map(parse_function),
Here is the documemtation of tensorflow to support this thing.
The socond one is in thedefinition of load_bin,data/eval_data_reader.py.
sess = tf.Session() was repeatedly called in for i in range and was not closed.
I think it will increase the efficiency and avoid out of memory if you close this session after using it.
Here are two files to support this issue,support1 and support2
Looking forward to your reply. Btw, I am very glad to create a PR to fix it if you are too busy.
Hello,I found some performance issues.
The first one is in
train_nets.py,dataset = dataset.map(parse_function) was called without num_parallel_calls.
I think it will increase the efficiency of your program if you add this.
The same issues also exist in dataset = dataset.map(parse_function) ,
dataset = dataset.map(parse_function),
dataset = dataset.map(parse_function),
Here is the documemtation of tensorflow to support this thing.
The socond one is in thedefinition of
load_bin,data/eval_data_reader.py.sess = tf.Session() was repeatedly called in for i in range and was not closed.
I think it will increase the efficiency and avoid out of memory if you close this session after using it.
Here are two files to support this issue,support1 and support2
Looking forward to your reply. Btw, I am very glad to create a PR to fix it if you are too busy.