feat: loss method + mv data reshaping out of Autoencoder class

autoencoder.py

@@ -17,6 +17,12 @@ class Autoencoder:
         self.encoder = DeepNNLayer(encoder_layers, lr, activation_func)
         self.decoder = DeepNNLayer(decoder_layers, lr, activation_func)
 
+    def loss(self, data_set: list[np.ndarray]) -> float:
+        loss = 0
+        for x in data_set:
+            loss += np.sum(np.abs(x - self.forward(x)[0])) / len(x)
+        return loss / len(data_set)
+
     def train(self, v: np.ndarray):
         out = self.decoder.forward(
             self.encoder.forward(v)
@@ -31,12 +37,12 @@ class Autoencoder:
                       max_epoch: int,
                       patience: int,
                       display_loss: bool = False) -> list[float]:
+        losses = [self.loss(data_set)]
         if display_loss is True:
-            ax, line = dynamic_loss_plot_init()
-        losses = []
+            ax, line = dynamic_loss_plot_init(losses)
         epoch = 0
         no_improv = 0
-        prev_error = float('inf')
+        prev_error = losses[0]
         with tqdm(bar_format="{desc} {elapsed} {rate_fmt}") as lbar:
             while True:
                 lbar.set_description(
@@ -45,8 +51,7 @@ class Autoencoder:
                 lbar.update()
                 error = 0
                 for x in data_set:
-                    input = x.flatten()
-                    error += self.train(input)
+                    error += self.train(x)
                 error /= len(data_set)
                 if prev_error - error <= 1e-8:
                     no_improv += 1
@@ -71,3 +76,8 @@ class Autoencoder:
 
     def decode(self, v: np.ndarray) -> np.ndarray:
         return self.decoder.forward(v)
+
+    def forward(self, v: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
+        code = self.encode(v)
+        out = self.decode(code)
+        return out, code

mnist_test.py

@@ -4,7 +4,7 @@ from autoencoder import Autoencoder
 from utils import relu
 
 
-def load_mnist():
+def load_mnist() -> list[np.ndarray]:
     import os
     import requests
 
@@ -23,9 +23,13 @@ def mnist_test(
         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)
-    in_len = x_train[0].shape[0] * x_train[0].shape[0]
+    x_train = x_train[:1000]
     autoencoder = Autoencoder(
         [in_len, bottleneck],
         [bottleneck, in_len],
@@ -41,9 +45,9 @@ def mnist_test(
     code = autoencoder.encode(example.flatten())
     output = autoencoder.decode(code)
     plt.subplot(1, 2, 1)
-    plt.matshow(example, fignum=False)
+    plt.matshow(example.reshape(img_shape), fignum=False)
     plt.subplot(1, 2, 2)
-    plt.matshow(output.reshape(example.shape), fignum=False)
+    plt.matshow(output.reshape(img_shape), fignum=False)
     plt.show()
 
 

utils.py

@@ -25,10 +25,10 @@ def regularize(v: np.ndarray) -> np.ndarray:
     return (v - v_min) / (v_max - v_min)
 
 
-def dynamic_loss_plot_init():
+def dynamic_loss_plot_init(losses: list):
     plt.ion()
     fig, ax = plt.subplots()
-    line, = ax.plot([], [], label="Loss")
+    line, = ax.plot([0], losses, label="Loss")
     ax.set_xlabel("Epoch")
     ax.set_ylabel("Loss")
     ax.set_title("Training Loss")