feat: save and load methods for Autoencoder

.gitignore

@@ -1,4 +1,5 @@
 __pycache__
 *.pyc
+*.npz
+*.npy
 .venv
-mnist.npz

autoencoder.py

@@ -53,7 +53,8 @@ class Autoencoder:
                 for x in data_set:
                     error += self.train(x)
                 error /= len(data_set)
-                if prev_error - error <= 1e-8:
+                derror = prev_error - error
+                if derror <= 0 or abs(derror) < 1e-8:
                     no_improv += 1
                 else:
                     no_improv = 0
@@ -81,3 +82,10 @@ class Autoencoder:
         code = self.encode(v)
         out = self.decode(code)
         return out, code
+
+    def save(self, path: str):
+        np.save(path, self)
+
+    def load(path: str) -> 'Autoencoder':
+        data = np.load(path, allow_pickle=True)
+        return data.item()

mnist_test.py

@@ -17,19 +17,17 @@ def load_mnist() -> list[np.ndarray]:
     return res["x_train"], res["y_train"], res["x_test"], res["y_test"]
 
 
-def mnist_test(
+def mnist_train(
         bottleneck: int,
         max_epoch: int,
         patience: int,
         ):
     x_train, _, x_test, _ = load_mnist()
     in_len = x_train[0].shape[0] * x_train[0].shape[0]
-    img_shape = x_train[0].shape
     x_train.resize(x_train.shape[0], in_len)
     x_test.resize(x_test.shape[0], in_len)
-    x_train = np.divide(x_train, 255)
-    x_test = np.divide(x_train, 255)
-    x_train = x_train[:1000]
+    x_train = x_train / 255
+    x_test = x_test / 255
     autoencoder = Autoencoder(
         [in_len, bottleneck],
         [bottleneck, in_len],
@@ -41,9 +39,20 @@ def mnist_test(
         max_epoch,
         patience,
         display_loss=True)
+    autoencoder.save("autoencoder_mnist")
+
+
+def mnist_test():
+    x_train, _, x_test, _ = load_mnist()
+    in_len = x_train[0].shape[0] * x_train[0].shape[0]
+    img_shape = x_train[0].shape
+    x_train.resize(x_train.shape[0], in_len)
+    x_test.resize(x_test.shape[0], in_len)
+    x_train = x_train / 255
+    x_test = x_test / 255
+    autoencoder = Autoencoder.load('autoencoder_mnist.npy')
     example: np.ndarray = x_test[np.random.randint(0, len(x_test))]
-    code = autoencoder.encode(example.flatten())
-    output = autoencoder.decode(code)
+    output, _ = autoencoder.forward(example.flatten())
     plt.subplot(1, 2, 1)
     plt.matshow(example.reshape(img_shape), fignum=False)
     plt.subplot(1, 2, 2)
@@ -55,10 +64,14 @@ if __name__ == "__main__":
     import argparse
     import sys
 
-    options = "b:e:p:"
     parser = argparse.ArgumentParser()
     parser.add_argument('-b', type=int, nargs='?', default=50)
     parser.add_argument('-e', type=int, nargs='?', default=1000)
     parser.add_argument('-p', type=int, nargs='?', default=5)
+    parser.add_argument('-r', action='store_true')
     args = parser.parse_args(sys.argv[1:])
-    mnist_test(args.b, args.e, args.p)
+    if args.r:
+        mnist_test()
+    else:
+        mnist_train(args.b, args.e, args.p)
+        mnist_test()