Playgrounds/nanosocrates-train-experiment-2.py

@@ -47,11 +47,11 @@ MAX_EPOCHS = int(3e3)
 PRETRAIN_EPOCHS = int(300)
 WARMUP_EPOCHS = int(1e3)
 MINI_BATCH_SIZE = 300
-VALIDATION_STEPS = 25
+VALIDATION_STEPS = 5
 CHECKPOINT_STEPS = VALIDATION_STEPS * 4
 PATIENCE = 4
 CURRENT_EPOCH = 0
-VERBOSE = False
+VERBOSE = True
 LEARNING_RATE = 1.5
 
 SOS_TOKEN = TOKENANO.encode("<SOS>")[0]
@@ -333,15 +333,15 @@ while current_epoch < MAX_EPOCHS:
                 patience += 1
 
 
-        txt_avg_loss = sum(text_batch_losses) / len(text_batch_losses)
+        txt_train_avg_loss = sum(text_batch_losses) / len(text_batch_losses)
 
         enc_avg_train_loss = float("inf")
-        dec_avg_loss = float("inf")
+        dec_avg_train_loss = float("inf")
 
         if current_epoch > PRETRAIN_EPOCHS:
             try:
                 enc_avg_train_loss = sum(encoder_batch_losses) / len(encoder_batch_losses)
-                dec_avg_loss = sum(decoder_batch_losses) / len(decoder_batch_losses)
+                dec_avg_train_loss = sum(decoder_batch_losses) / len(decoder_batch_losses)
             except:
                 pass
 
@@ -353,7 +353,7 @@ while current_epoch < MAX_EPOCHS:
                 f"{SEPARATOR}\n",
                 f"Train Losses:\n",
                 f"\tAvg Losses:\n",
-                f"\t\tavg_txt: {txt_avg_loss} - avg_enc: {enc_avg_loss} - avg_dec: {dec_avg_loss}\n",
+                f"\t\tavg_txt: {txt_train_avg_loss} - avg_enc: {enc_avg_train_loss} - avg_dec: {dec_avg_train_loss}\n",
                 f"{SEPARATOR}\n",
                 f"Validation Losses:\n",
                 f"\ttxt_loss: {txt_avg_loss} - masking_loss: {enc_avg_loss} - prediction: {dec_avg_loss}\n",