Save results more clearly

Author: emalgorithm

models/lstm_to_lstm_full_training.py

@@ -175,7 +175,14 @@ def train_iters(seq2seq_model, n_iters, pairs, print_every=1000, learning_rate=0
             val_rouge_2_scores.append(val_rouge_2)
             val_rouge_l_scores.append(val_rouge_l)
 
-            pickle.dump([train_losses, val_losses, val_f1_scores, val_rouge_2_scores,
-                         val_rouge_l_scores], open(model_dir + 'results.pkl', 'wb'))
+            results = {'train_losses': train_losses,
+                       'val_losses': val_losses,
+                       'val_f1_scores': val_f1_scores,
+                       'val_rouge_2_scores': val_rouge_2_scores,
+                       'val_rouge_l_scores': val_rouge_l_scores}
+
+            with open(model_dir + 'results.txt', 'w') as f:
+                f.write(str(results))
+            pickle.dump(results, open(model_dir + 'results.pkl', 'wb'))
 
             plot_loss(train_losses, val_losses, file_path=model_dir + 'loss.jpg')

train.py

@@ -1,5 +1,4 @@
 import os
-import torch
 import argparse
 
 from models.lstm_to_lstm import Seq2Seq
@@ -11,10 +10,12 @@
 from models.gcn_encoder import GCNEncoder
 
 
-def main(model_name):
-    model_dir = '../results/{}/'.format(model_name)
+def main():
+    model_dir = '../results/{}/'.format(opt.model_name)
     if not os.path.exists(model_dir):
         os.makedirs(model_dir)
+    with open(model_dir + 'hyperparams.txt', 'w') as f:
+        f.write(str(opt))
 
     if opt.graph:
         lang, pairs = prepare_data(num_samples=opt.n_samples)
@@ -55,4 +56,4 @@ def main(model_name):
 print(opt)
 
 if __name__ == "__main__":
-    main(opt.model_name)
+    main()