Tidy up the NeuralNetwork class

1 files changed, 16 insertions, 12 deletions

diff --git a/neural_network.py b/neural_network.py
index 3dbd94d..1408a91 100644
--- a/neural_network.py
+++ b/neural_network.py
@@ -39,14 +39,14 @@ class NeuralNetwork:
     input_image (a path).
     """
     def guess(self, input_image: np.array) -> str:
-        output_layer = self._predict(input_image)
+        output_layer = self._feedforward(input_image)
 
-        return self.out(output_layer)
+        return self.guessed_char(output_layer)
 
     """
     Save the weights to a csv file.
     """
-    def save(self):
+    def save(self, weights_filename):
         pass
 
     """
@@ -56,12 +56,20 @@ class NeuralNetwork:
         pass
 
     """
+    Get the result from a sigmoid matrix (the index with the highest chance
+    of being the correct answer).
+    """
+    def _guessed_char(self, output_layer: np.array) -> str:
+        return CYRILLIC_ALPHABET[np.argmax(np.transpose(output_layer))]
+
+    """
     Feedforwarding.
     """
-    def _predict(self, input_layer: np.array):
+    def _feedforward(self, input_layer: np.array):
         hidden_layer_inputs = np.dot(self._hidden_weights, input_layer)
         hidden_layer_outputs = self._get_layer_output(hidden_layer_inputs)
-        output_layer_inputs = np.dot(hidden_layer_outputs, self._output_weights)
+        output_layer_inputs = np.dot(hidden_layer_outputs,
+                                     self._output_weights)
 
         # The output layer outputs. (Final output of the neural network).
         return self._get_layer_output(output_layer_inputs)
@@ -72,15 +80,11 @@ class NeuralNetwork:
     def _get_layer_output(self, layer: np.array):
         return expit(layer)
 
+    """
+    Generate a random array via an uniform distribution.
+    """
     def _random_array(self, rows: int, columns: int) -> np.array:
         low = -1 / math.sqrt(rows)
         high = 1 / math.sqrt(columns)
 
         return np.random.uniform(low, high, (rows, columns))
-
-    """
-    Get the result from a sigmoid matrix (the index with the highest chance
-    of being the correct answer).
-    """
-    def out(self, output_layer: np.array):
-        return CYRILLIC_ALPHABET[np.argmax(np.transpose(output_layer))]