Remove Epoch training metric log

example/kaggle-ndsb1/training_curves.py

@@ -22,26 +22,22 @@
 import re
 import argparse
 
-parser = argparse.ArgumentParser(description='Parses log file and generates train/val curves')
-parser.add_argument('--log-file', type=str,default="log_tr_va",
+parser = argparse.ArgumentParser(description='Parses log file and generates val curves')
+parser.add_argument('--log-file', type=str,default="log_va",
                     help='the path of log file')
 args = parser.parse_args()
 
 
-TR_RE = re.compile('.*?]\sTrain-accuracy=([\d\.]+)')
 VA_RE = re.compile('.*?]\sValidation-accuracy=([\d\.]+)')
 
 log = open(args.log_file).read()
 
-log_tr = [float(x) for x in TR_RE.findall(log)]
 log_va = [float(x) for x in VA_RE.findall(log)]
-idx = np.arange(len(log_tr))
+idx = np.arange(len(log_va))
 
 plt.figure(figsize=(8, 6))
 plt.xlabel("Epoch")
 plt.ylabel("Accuracy")
-plt.plot(idx, log_tr, 'o', linestyle='-', color="r",
-         label="Train accuracy")
 
 plt.plot(idx, log_va, 'o', linestyle='-', color="b",
          label="Validation accuracy")

python/mxnet/module/base_module.py

@@ -542,9 +542,6 @@ def fit(self, train_data, eval_data=None, eval_metric='acc',
                 if monitor is not None:
                     monitor.toc_print()
 
-                if end_of_batch:
-                    eval_name_vals = eval_metric.get_name_value()
-
                 if batch_end_callback is not None:
                     batch_end_params = BatchEndParam(epoch=epoch, nbatch=nbatch,
                                                      eval_metric=eval_metric,
@@ -553,9 +550,6 @@ def fit(self, train_data, eval_data=None, eval_metric='acc',
                         callback(batch_end_params)
                 nbatch += 1
 
-            # one epoch of training is finished
-            for name, val in eval_name_vals:
-                self.logger.info('Epoch[%d] Train-%s=%f', epoch, name, val)
             toc = time.time()
             self.logger.info('Epoch[%d] Time cost=%.3f', epoch, (toc-tic))
 

tools/parse_log.py

@@ -37,8 +37,7 @@
 with open(args.logfile[0]) as f:
     lines = f.readlines()
 
-res = [re.compile('.*Epoch\[(\d+)\] Train-'+s+'.*=([.\d]+)') for s in args.metric_names]\
-     + [re.compile('.*Epoch\[(\d+)\] Validation-'+s+'.*=([.\d]+)') for s in args.metric_names]\
+res = [re.compile('.*Epoch\[(\d+)\] Validation-'+s+'.*=([.\d]+)') for s in args.metric_names]\
      + [re.compile('.*Epoch\[(\d+)\] Time.*=([.\d]+)')]
 
 data = {}
@@ -63,11 +62,11 @@
     data[epoch][i*2+1] += 1
 
 if args.format == 'markdown':
-    print("| epoch | " + " | ".join(['train-'+s for s in args.metric_names]) + " | " + " | ".join(['val-'+s for s in args.metric_names]) + " | time |")
+    print("| epoch | " + " | ".join(['val-'+s for s in args.metric_names]) + " | time |")
     print("| --- "*(len(res)+1) + "|")
     for k, v in data.items():
         print("| %2d | " % (k+1)\
-              + " | ".join(["%f" % (v[2*j]/v[2*j+1]) for j in range(2*len(args.metric_names))])\
+              + " | ".join(["%f" % (v[2*j]/v[2*j+1]) for j in range(len(args.metric_names))])\
               + " | %.1f |" % (v[-2]/v[-1]))
 elif args.format == 'none':
     print("\t".join(['epoch'] + ['train-' + s for s in args.metric_names] + ['val-' + s for s in args.metric_names] + ['time']))