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 = ['⡿', '⣟', '⣯', '⣷', '⣾', '⣽', '⣻', '⢿']
 
@@ -88,32 +89,34 @@ class Autoencoder:
         epoch = 0
         no_improv = 0
         prev_error = float('inf')
-        while True:
+        with tqdm(bar_format="{desc} {elapsed} {rate_fmt}") as lbar :
-            print(
+            while True:
-                f"{LOADER[epoch % len(LOADER)]} Training \t({epoch=} error={prev_error:.2f})", # noqa
+                lbar.set_description(
-            )
+                    f"{LOADER[epoch % len(LOADER)]} Training ({epoch=} error={prev_error:.2f})",
-            error = 0
+                )
-            for x in data_set:
+                lbar.update()
-                input = x.flatten()
+                error = 0
-                error += self.train(input)
+                for x in data_set:
-            error /= len(data_set)
+                    input = x.flatten()
-            if prev_error - error <= 1e-8:
+                    error += self.train(input)
-                no_improv += 1
+                error /= len(data_set)
-            else:
+                if prev_error - error <= 1e-8:
-                no_improv = 0
+                    no_improv += 1
-            prev_error = float(error)
+                else:
-            losses.append(error)
+                    no_improv = 0
+                prev_error = float(error)
+                losses.append(error)
+                if display_loss is True:
+                    dynamic_loss_plot_update(ax, line, losses)
+                if no_improv > patience:
+                    break
+                if epoch > max_epoch:
+                    break
+                epoch += 1
             if display_loss is True:
-                dynamic_loss_plot_update(ax, line, losses)
+                dynamic_loss_plot_finish(ax, line)
-            if no_improv > patience:
+            print("#Training complete !")
-                break
+            return losses
-            if epoch > max_epoch:
-                break
-            epoch += 1
-        if display_loss is True:
-            dynamic_loss_plot_finish(ax, line)
-        print("#Training complete !")
-        return losses
 
     def encode(self, v: np.ndarray) -> np.ndarray:
         return self.encoder.forward(v)

requirements.txt

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