From a4e33358691431575d169a6102a945e16d132a44 Mon Sep 17 00:00:00 2001
From: Marcin Chrzanowski <m@m-chrzan.xyz>
Date: Sat, 29 May 2021 14:14:33 +0200
Subject: Save model and metrics

---
 experiment/experiment.py | 18 +++++++++++++++++-
 train/train.py           | 11 +++++++++--
 2 files changed, 26 insertions(+), 3 deletions(-)

diff --git a/experiment/experiment.py b/experiment/experiment.py
index 600732f..b06408f 100644
--- a/experiment/experiment.py
+++ b/experiment/experiment.py
@@ -2,6 +2,9 @@ import subprocess
 import os
 import time
 
+import pandas as pd
+import torch
+
 from train.train import train_model
 from util.parse_config import parse_file
 from model.encoder import EncoderModel
@@ -16,11 +19,24 @@ class Experiment:
     def run(self):
         model_config, train_config = parse_file(self.file)
         model = EncoderModel(device=self.device, **model_config).to(self.device)
-        train_model(model, device=self.device, **train_config)
+        train_losses, test_losses, accuracies = train_model(model, device=self.device, **train_config)
+        self.save_model(model)
+        self.save_metrics(train_losses, test_losses, accuracies)
+
+    def save_model(self, model):
+        torch.save(model.state_dict(), self.dir_path('net.pt'))
 
     def dir_path(self, file):
         return '{}/{}'.format(self.dirname, file)
 
+    def save_metrics(self, train_losses, test_losses, accuracies):
+        data_frame = pd.DataFrame({
+            'train_loss': train_losses,
+            'test_loss': test_losses,
+            'accuracy': accuracies
+        })
+        data_frame.to_csv(self.dir_path('metrics.csv'))
+
     def make_dir(self, prefix):
         time_string = time.strftime('%Y%m%d%H%M%S')
         prefix = '' if prefix == '' else '{}-'.format(prefix)
diff --git a/train/train.py b/train/train.py
index e72fb3f..be6693c 100644
--- a/train/train.py
+++ b/train/train.py
@@ -22,6 +22,8 @@ def train_model(model, lr, num_steps, batch_size, n_tokens, seqlen, max_count, d
 
     start_time = time()
     accs = []
+    train_losses = []
+    test_losses = []
 
     loss_function = nn.CrossEntropyLoss(
         # weight=torch.log(2 + torch.tensor(range(max_count+1), dtype=torch.float))
@@ -52,16 +54,21 @@ def train_model(model, lr, num_steps, batch_size, n_tokens, seqlen, max_count, d
             model.eval()
             predicted_logits = model.forward(test_X).reshape(-1, max_count + 1)
             predicted_logits = predicted_logits.to('cpu')
+            test_loss = loss_function(predicted_logits, test_Y.reshape(-1))
             test_acc = (
                     torch.sum(torch.argmax(predicted_logits, dim=-1) == test_Y.reshape(-1))
                     / test_Y.reshape(-1).shape[0])
             print('step', step, 'out of', num_steps)
             print('loss train', float(loss))
+            print('loss test', float(test_loss))
             print('accuracy test', float(test_acc))
             do_verbose_test(model, n_tokens, seqlen, max_count)
             print()
             sys.stdout.flush()
-            accs.append(test_acc)
+            accs.append(round(float(test_acc), 2))
+            train_losses.append(round(float(loss.detach()), 2))
+            test_losses.append(round(float(test_loss.detach()), 2))
+            # print(accs, train_losses, test_losses)
     print('\nTRAINING TIME:', time()-start_time)
     model.eval()
-    return accs
+    return train_losses, test_losses, accs
-- 
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