feat: custom load_mnist function and rm useless dependency

autoencoder.py

@@ -91,7 +91,6 @@ class Autoencoder:
         while True:
             print(
                 f"{LOADER[epoch % len(LOADER)]} Training \t({epoch=} error={prev_error:.2f})", # noqa
-                end="\r"
             )
             error = 0
             for x in data_set:
@@ -113,7 +112,7 @@ class Autoencoder:
             epoch += 1
         if display_loss is True:
             dynamic_loss_plot_finish(ax, line)
-        print("\r#Training complete !")
+        print("#Training complete !")
         return losses
 
     def encode(self, v: np.ndarray) -> np.ndarray:

mnist_test.py

@@ -1,16 +1,28 @@
 import matplotlib.pyplot as plt
 import numpy as np
-import keras
 from autoencoder import Autoencoder
 from utils import relu
 
 
+def load_mnist():
+    import os
+    import requests
+
+    mnist_path = "./mnist.npz"
+    mnist_url = "https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz"
+    if not os.path.exists(mnist_path):
+        with open(mnist_path, "w+b") as f:
+            f.write(requests.get(mnist_url, stream=True).content)
+    res = np.load(mnist_path)
+    return res["x_train"], res["y_train"], res["x_test"], res["y_test"]
+
+
 def mnist_test(
         bottleneck: int,
         max_epoch: int,
         patience: int,
         ):
-    (x_train, _), (x_test, _) = keras.datasets.mnist.load_data()
+    x_train, _, x_test, _ = load_mnist()
     x_train = np.divide(x_train, 255)
     x_test = np.divide(x_train, 255)
     in_len = x_train[0].flatten().shape[0]

requirements.txt

@@ -1,4 +1,3 @@
 numpy
 matplotlib
-keras
-tensorflow
+requests