log both word acc and cer for valset

src/main.py

@@ -18,15 +18,16 @@ class FilePaths:
     fnCorpus = '../data/corpus.txt'
 
 
-def write_summary(charErrorRates):
+def write_summary(charErrorRates, wordAccuracies):
     with open(FilePaths.fnSummary, 'w') as f:
-        json.dump(charErrorRates, f)
+        json.dump({'charErrorRates': charErrorRates, 'wordAccuracies': wordAccuracies}, f)
 
 
 def train(model, loader):
     "train NN"
     epoch = 0  # number of training epochs since start
     summaryCharErrorRates = []
+    summaryWordAccuracies = []
     bestCharErrorRate = float('inf')  # best valdiation character error rate
     noImprovementSince = 0  # number of epochs no improvement of character error rate occured
     earlyStopping = 25  # stop training after this number of epochs without improvement
@@ -44,11 +45,12 @@ def train(model, loader):
             print(f'Epoch: {epoch} Batch: {iterInfo[0]}/{iterInfo[1]} Loss: {loss}')
 
         # validate
-        charErrorRate = validate(model, loader)
+        charErrorRate, wordAccuracy = validate(model, loader)
 
         # write summary
         summaryCharErrorRates.append(charErrorRate)
-        write_summary(summaryCharErrorRates)
+        summaryWordAccuracies.append(wordAccuracy)
+        write_summary(summaryCharErrorRates, summaryWordAccuracies)
 
         # if best validation accuracy so far, save model parameters
         if charErrorRate < bestCharErrorRate:
@@ -94,7 +96,7 @@ def validate(model, loader):
     charErrorRate = numCharErr / numCharTotal
     wordAccuracy = numWordOK / numWordTotal
     print(f'Character error rate: {charErrorRate * 100.0}%. Word accuracy: {wordAccuracy * 100.0}%.')
-    return charErrorRate
+    return charErrorRate, wordAccuracy
 
 
 def infer(model, fnImg):