feat: str methods for Autoencoder

autoencoder.py

@@ -17,6 +17,9 @@ class Autoencoder:
         self.encoder = DeepNNLayer(encoder_layers, lr, activation_func)
         self.decoder = DeepNNLayer(decoder_layers, lr, activation_func)
 
+    def __str__(self):
+        return f'Encoder:\n{self.encoder}\n\nDecoder:\n{self.decoder}'
+
     def loss(self, data_set: list[np.ndarray]) -> float:
         loss = 0
         for x in data_set:

layers.py

@@ -17,6 +17,9 @@ class NNLayer:
         self.output_linear = None
         self.activation_func = activation_func
 
+    def __str__(self):
+        return f'[ {self.W.shape[0]} => {self.W.shape[1]}\tlr:{self.lr}\tactivation:{self.activation_func.__name__} ]' # noqa
+
     def forward(self, V: np.ndarray) -> np.ndarray:
         self.input = normalize(V)
         self.output_linear = self.input @ self.W + self.B
@@ -50,6 +53,9 @@ class DeepNNLayer:
                         activation_func)
                 )
 
+    def __str__(self):
+        return '\n'.join([str(layer) for layer in self.layers])
+
     def forward(self, v: np.ndarray) -> np.ndarray:
         for layer in self.layers:
             v = layer.forward(v)

mnist_test.py

@@ -51,8 +51,7 @@ def mnist_test(filename: str):
     x_train = x_train / 255
     x_test = x_test / 255
     autoencoder: Autoencoder = Autoencoder.load(filename)
-    for i in autoencoder.encoder.layers:
-        print(len(i.input), len(i.output))
+    print(autoencoder)
     idx = np.random.randint(0, len(x_test))
     example: np.ndarray = x_test[idx]
     output, code = autoencoder.forward(example.flatten())