fix: missing activation func derivative + send error before update

mnist_test.py

@@ -1,7 +1,7 @@
 import matplotlib.pyplot as plt
 import numpy as np
 from autoencoder import Autoencoder
-from utils import relu
+from utils import relu, regularize
 
 
 def load_mnist() -> list[np.ndarray]:
@@ -18,7 +18,7 @@ def load_mnist() -> list[np.ndarray]:
 
 
 def mnist_train(
-        bottleneck: int,
+        filename: str,
         max_epoch: int,
         patience: int,
         ):
@@ -29,9 +29,9 @@ def mnist_train(
     x_train = x_train / 255
     x_test = x_test / 255
     autoencoder = Autoencoder(
-        [in_len, bottleneck],
-        [bottleneck, in_len],
-        0.1,
+        [in_len, 64, 16],
+        [16, 64, in_len],
+        0.01,
         relu
     )
     autoencoder.train_dataset(
@@ -39,24 +39,39 @@ def mnist_train(
         max_epoch,
         patience,
         display_loss=True)
-    autoencoder.save("autoencoder_mnist")
+    autoencoder.save(filename)
 
 
-def mnist_test():
-    x_train, _, x_test, _ = load_mnist()
+def mnist_test(filename: str):
+    x_train, _, x_test, y_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))]
-    output, _ = autoencoder.forward(example.flatten())
-    plt.subplot(1, 2, 1)
-    plt.matshow(example.reshape(img_shape), fignum=False)
-    plt.subplot(1, 2, 2)
-    plt.matshow(output.reshape(img_shape), fignum=False)
+    autoencoder: Autoencoder = Autoencoder.load(filename)
+    for i in autoencoder.encoder.layers:
+        print(len(i.input), len(i.output))
+    idx = np.random.randint(0, len(x_test))
+    example: np.ndarray = x_test[idx]
+    output, code = autoencoder.forward(example.flatten())
+    output = regularize(output)
+    plt.subplot(1, 3, 1)
+    plt.matshow(
+        example.reshape(img_shape),
+        fignum=False)
+    plt.title(f"Input ({y_test[idx]})")
+    plt.subplot(1, 3, 2)
+    plt.matshow(
+        output.reshape(img_shape),
+        fignum=False)
+    plt.title(f"Output ({y_test[idx]})")
+    plt.subplot(1, 3, 3)
+    s = int(np.ceil(np.sqrt(code.shape[0])))
+    code.resize((s, s), refcheck=False)
+    plt.matshow(code, fignum=False)
+    plt.title(f"Code ({y_test[idx]})")
     plt.show()
 
 
@@ -65,13 +80,34 @@ if __name__ == "__main__":
     import sys
 
     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')
+    parser.add_argument(
+        '-e',
+        type=int,
+        nargs='?',
+        default=1000,
+        help='Max epochs'
+    )
+    parser.add_argument(
+        '-p',
+        type=int,
+        nargs='?',
+        default=5,
+        help='Patience'
+    )
+    parser.add_argument(
+        '-m',
+        type=str, nargs='?',
+        default='autoencoder_mnist.npy',
+        help='Model filename to save in run mode or load in training mode'
+    )
+    parser.add_argument(
+        '-r',
+        action='store_true',
+        help='Run mode'
+    )
     args = parser.parse_args(sys.argv[1:])
     if args.r:
-        mnist_test()
+        mnist_test(args.m)
     else:
-        mnist_train(args.b, args.e, args.p)
-        mnist_test()
+        mnist_train(args.m, args.e, args.p)
+        mnist_test(args.m)