Update readme and docs after change from loop to epochs.

README.md

@@ -78,7 +78,7 @@ setoption name SkipLoadingEval value true
 setoption name Use NNUE value pure
 setoption name Threads value x
 isready
-learn targetdir trainingdata loop 100 batchsize 1000000 use_draw_in_training 1 use_draw_in_validation 1 lr 1 lambda 1 eval_limit 32000 nn_batch_size 1000 newbob_decay 0.5 eval_save_interval 250000000 loss_output_interval 1000000 validation_set_file_name validationdata\val.binpack
+learn targetdir trainingdata epochs 10000 batchsize 1000000 use_draw_in_training 1 use_draw_in_validation 1 lr 1 lambda 1 eval_limit 32000 nn_batch_size 1000 newbob_decay 0.5 eval_save_interval 250000000 loss_output_interval 1000000 validation_set_file_name validationdata\val.binpack
 ```
 
 This will utilize training data files in the "trainingdata" directory and validation data from file "validationdata\val.bin". Produced nets are saved in the "evalsave" folder.

docs/learn.md

@@ -20,7 +20,7 @@ Currently the following options are available:
 
 `targetdir` - path to the direction from which training data will be read. All files in this directory are read sequentially. If not specified then only the list of files from positional arguments will be used. If specified then files from the given directory will be used after the explicitly specified files.
 
-`loop` - the number of times to loop over all training data.
+`epochs` - the number of weight update cycles (epochs) to train the network for. One such cycle is `batchsize` positions. If not specified then the training will loop forever.
 
 `basedir` - the base directory for the paths. Default: "" (current directory)
 

src/learn/learn.cpp

@@ -922,7 +922,7 @@ namespace Learner
         auto mini_batch_size = LEARN_MINI_BATCH_SIZE;
 
         // Number of epochs
-        uint64_t epochs = 1;
+        uint64_t epochs = std::numeric_limits<uint64_t>::max();
 
         // Game file storage folder (get game file with relative path from here)
         string base_dir;