easier logging of losses etc.

onn_mnist.py

@@ -2,6 +2,7 @@
 
 from onn import *
 from onn_core import _f
+from dotmap import DotMap
 
 #np.random.seed(42069)
 
@@ -155,11 +156,18 @@ log('parameters', model.param_count)
 
 ritual.prepare(model)
 
-batch_losses, batch_mlosses = [], []
-train_losses, train_mlosses = [], []
-valid_losses, valid_mlosses = [], []
-
-train_confid, valid_confid = [], []
+logs = DotMap(
+    batch_losses = [],
+    batch_mlosses = [],
+    train_losses = [],
+    train_mlosses = [],
+    valid_losses = [],
+    valid_mlosses = [],
+    train_confid = [],
+    valid_confid = [],
+    learning_rate = [],
+    momentum = [],
+)
 
 def measure_error(quiet=False):
     def print_error(name, inputs, outputs, comparison=None):
@@ -177,13 +185,13 @@ def measure_error(quiet=False):
         return loss, mloss, confid
 
     loss, mloss, confid = print_error("train", inputs, outputs)
-    train_losses.append(loss)
-    train_mlosses.append(mloss)
-    train_confid.append(confid)
+    logs.train_losses.append(loss)
+    logs.train_mlosses.append(mloss)
+    #logs.train_confid.append(confid)
     loss, mloss, confid = print_error("valid", valid_inputs, valid_outputs)
-    valid_losses.append(loss)
-    valid_mlosses.append(mloss)
-    valid_confid.append(confid)
+    logs.valid_losses.append(loss)
+    logs.valid_mlosses.append(mloss)
+    #logs.valid_confid.append(confid)
 
 measure_error()
 
@@ -202,13 +210,17 @@ while learner.next():
     log("epoch {}".format(learner.epoch),
         fmt.format(learner.rate, avg_loss, avg_mloss * 100))
 
-    batch_losses += losses
-    batch_mlosses += mlosses
+    logs.batch_losses += losses
+    logs.batch_mlosses += mlosses
 
     if measure_every_epoch:
         quiet = learner.epoch != learner.epochs
         measure_error(quiet=quiet)
 
+    logs.learning_rate.append(optim.alpha)
+    if getattr(optim, 'mu', None):
+        logs.momentum.append(optim.mu)
+
 if not measure_every_epoch:
     measure_error()
 
@@ -218,12 +230,8 @@ if save_fn is not None:
 
 if log_fn:
     log('saving losses', log_fn)
-    np.savez_compressed(log_fn,
-                        batch_losses =np.array(batch_losses,  dtype=_f),
-                        batch_mlosses=np.array(batch_mlosses, dtype=_f),
-                        train_losses =np.array(train_losses,  dtype=_f),
-                        train_mlosses=np.array(train_mlosses, dtype=_f),
-                        valid_losses =np.array(valid_losses,  dtype=_f),
-                        valid_mlosses=np.array(valid_mlosses, dtype=_f),
-                        train_confid =np.array(train_confid,  dtype=_f),
-                        valid_confid =np.array(valid_confid,  dtype=_f))
+    kwargs = dict()
+    for k, v in logs.items():
+        if len(v) > 0:
+            kwargs[k] = np.array(v, dtype=_f)
+    np.savez_compressed(log_fn, **kwargs)