refactor: use tqdm instead of custom load bar

autoencoder.py

@@ -4,6 +4,7 @@ from utils import (regularize,
                    dynamic_loss_plot_update,
                    dynamic_loss_plot_finish)
 import types
+from tqdm import tqdm
 
 LOADER = ['⡿', '⣟', '⣯', '⣷', '⣾', '⣽', '⣻', '⢿']
 
@@ -88,10 +89,12 @@ class Autoencoder:
         epoch = 0
         no_improv = 0
         prev_error = float('inf')
+        with tqdm(bar_format="{desc} {elapsed} {rate_fmt}") as lbar :
             while True:
-            print(
+                lbar.set_description(
-                f"{LOADER[epoch % len(LOADER)]} Training \t({epoch=} error={prev_error:.2f})", # noqa
+                    f"{LOADER[epoch % len(LOADER)]} Training ({epoch=} error={prev_error:.2f})",
                 )
+                lbar.update()
                 error = 0
                 for x in data_set:
                     input = x.flatten()

requirements.txt

@@ -1,3 +1,4 @@
 numpy
 matplotlib
 requests
+tqdm