RNN small droupout

main.py

@@ -120,16 +120,17 @@ if __name__ == "__main__":
         wandb.watch(network, log_freq = log_freq)
 
     for t in range(cfg["Training"]["Epochs"]):
-        print("Epoch {}".format(t))
+        logging.info("Epoch {}".format(t))
         train(args, network, train_loader, f_loss, optimizer, device, log_interval)
 
         val_loss = test.test(network, valid_loader, f_loss, device)
 
         network_checkpoint.update(val_loss)
 
-        print(" Validation : Loss : {:.4f}".format(val_loss))
+        logging.info(" Validation : Loss : {:.4f}".format(val_loss))
         if not args.no_wandb:
             wandb.log({"val_loss": val_loss})
 
-
+    utils.write_summary(logdir, network, optimizer, val_loss)
+    
     create_submission.create_submission(network, dataloader.composite_transform(dataloader.transform_remove_space_time(), dataloader.transform_min_max_scaling(MIN, MAX)), device, rootDir, logdir)

train.py

@@ -42,18 +42,12 @@ def train(args, model, loader, f_loss, optimizer, device, log_interval = 100):
         
         Y = list(model.parameters())[0].grad.cpu().tolist()
         
-        #gradients.append(np.mean(Y))
-        #tar.append(np.mean(outputs.cpu().tolist()))
-        #out.append(np.mean(targets.cpu().tolist()))
+
         if not args.no_wandb:
             if batch_idx % log_interval == 0:
                 wandb.log({"train_loss" : loss})
         optimizer.step()
 
-    #visualize_gradients(gradients)
-    #visualize_gradients(tar)
-    #visualize_gradients(out)
-
 def visualize_gradients(gradients):
     print(gradients)
     import numpy as np

utils.py

 import os
+import sys
 
 def generate_unique_logpath(logdir, raw_run_name):
     i = 0
@@ -14,4 +15,35 @@ def create_unique_logpath(top_logdir, raw_run_name):
         os.mkdir(top_logdir)
     logdir = generate_unique_logpath(top_logdir, raw_run_name)
     os.mkdir(logdir)
-    return logdir, raw_run_name
+    return logdir, raw_run_name
\ No newline at end of file
+
+def write_summary(logdir, model, optimizer, val_loss):
+    summary_file = open(logdir + "/summary.txt", 'w')
+    summary_text = """
+    Validation loss
+    ===============
+    {}
+
+
+    Executed command
+    ================
+    {}
+
+    Dataset
+    =======
+    FashionMNIST
+
+    Model summary
+    =============
+    {}
+
+    {} trainable parameters
+
+    Optimizer
+    ========
+    {}
+
+
+    """.format(val_loss," ".join(sys.argv), model, sum(p.numel() for p in model.parameters() if p.requires_grad), optimizer)
+    summary_file.write(summary_text)
+    summary_file.close()
\ No newline at end of file