elaborate on the correlation between overfit_pct and xxx_percent_check (#132)

* Update Training Loop.md

* update docs and elaborate on the correlation

docs/Trainer/Training Loop.md

@@ -54,7 +54,10 @@ trainer = Trainer(track_grad_norm=2)
 
 ---
 #### Set how much of the training set to check
-If you don't want to check 100% of the training set (for debugging or if it's huge), set this flag
+If you don't want to check 100% of the training set (for debugging or if it's huge), set this flag.
+
+train_percent_check will be overwritten by overfit_pct if `overfit_pct > 0`
+
 ``` {.python}
 # DEFAULT
 trainer = Trainer(train_percent_check=1.0)

docs/Trainer/Validation loop.md

@@ -18,6 +18,9 @@ trainer = Trainer(check_val_every_n_epoch=1)
 ---
 #### Set how much of the validation set to check 
 If you don't want to check 100% of the validation set (for debugging or if it's huge), set this flag
+
+val_percent_check will be overwritten by overfit_pct if `overfit_pct > 0`
+
 ``` {.python}
 # DEFAULT
 trainer = Trainer(val_percent_check=1.0)
@@ -29,6 +32,9 @@ trainer = Trainer(val_percent_check=0.1)
 ---
 #### Set how much of the test set to check 
 If you don't want to check 100% of the test set (for debugging or if it's huge), set this flag
+
+test_percent_check will be overwritten by overfit_pct if `overfit_pct > 0`
+
 ``` {.python}
 # DEFAULT
 trainer = Trainer(test_percent_check=1.0)

docs/Trainer/debugging.md

@@ -23,6 +23,9 @@ trainer = Trainer(track_grad_norm=2)
 ---
 #### Make model overfit on subset of data
 A useful debugging trick is to make your model overfit a tiny fraction of the data.
+
+setting `overfit_pct > 0` will overwrite train_percent_check, val_percent_check, test_percent_check
+
 ``` {.python}
 # DEFAULT don't overfit (ie: normal training)
 trainer = Trainer(overfit_pct=0.0)