Add more methods

2 files changed, 51 insertions, 19 deletions

diff --git a/main.py b/main.py
index a44fbcb..f4b1ede 100644
--- a/main.py
+++ b/main.py
@@ -9,8 +9,11 @@ def main():
     data = Dataset()
     image = data.get_image('А/5a2f3c19c27bb.png')
     neural_network = NeuralNetwork(LEARNING_RATE, INPUT_RESOLUTION)
+    neural_network.load('hidden_weights_3e1064eab32018b3.csv',
+                        'output_weights_2406f3eb22111fe9.csv')
+    # Hey!
 
-    print(neural_network.guess(image))
+    print('This is it!')
 
 
 if __name__ == "__main__":
diff --git a/neural_network.py b/neural_network.py
index 1408a91..6c44ddb 100644
--- a/neural_network.py
+++ b/neural_network.py
@@ -1,7 +1,7 @@
 import numpy as np
 import math
 from scipy.special import expit
-from dataset import Dataset
+from secrets import token_hex
 
 CYRILLIC_ALPHABET = ['I', 'А', 'Б', 'В', 'Г', 'Д', 'Е', 'Ë', 'Ж', 'З',
                      'И', 'Й', 'К', 'Л', 'М', 'Н', 'О', 'П', 'Р', 'С',
@@ -39,21 +39,40 @@ class NeuralNetwork:
     input_image (a path).
     """
     def guess(self, input_image: np.array) -> str:
-        output_layer = self._feedforward(input_image)
+        output_layer = self.feedforward(input_image)
 
-        return self.guessed_char(output_layer)
+        return self._guessed_char(output_layer)
+
+    """
+    Feedforwarding.
+    """
+    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)
+
+        # The output layer outputs. (Final output of the neural network).
+        return self._get_layer_output(output_layer_inputs)
 
     """
     Save the weights to a csv file.
     """
-    def save(self, weights_filename):
-        pass
+    def save(self):
+        np.savetxt(f"./hidden_weights_{token_hex(8)}.csv",
+                   self._hidden_weights, delimiter=',')
+        np.savetxt(f"./output_weights_{token_hex(8)}.csv",
+                   self._output_weights, delimiter=',')
 
     """
     Load the weights from a csv file.
     """
-    def load(self, weights_file: str):
-        pass
+    def load(self, hidden_weights_file: str, output_weights_file: str):
+        with open(hidden_weights_file) as hidden_weights:
+            self._hidden_weights = np.loadtxt(hidden_weights, delimiter=',')
+
+        with open(output_weights_file) as output_weights:
+            self._output_weights = np.loadtxt(output_weights, delimiter=',')
 
     """
     Get the result from a sigmoid matrix (the index with the highest chance
@@ -63,22 +82,32 @@ class NeuralNetwork:
         return CYRILLIC_ALPHABET[np.argmax(np.transpose(output_layer))]
 
     """
-    Feedforwarding.
+    Apply the sigmoid function to a given layer
     """
-    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)
+    def _get_layer_output(self, layer: np.array) -> np.array:
+        return expit(layer)
 
-        # The output layer outputs. (Final output of the neural network).
-        return self._get_layer_output(output_layer_inputs)
+    """
+    Get the hidden layer and output layer error matrices.
+    """
+    def _get_errors(self, target: str) -> tuple:
+        output_layer_errors = np.substract(self._get_expected_outputs(target),
+                                           self._output_layer)
+        # Backpropagate the errors.
+        hidden_layer_errors = np.dot(np.transpose(self._hidden_weights),
+                                     output_layer_errors)
+
+        return (hidden_layer_errors, output_layer_errors)
 
     """
-    Apply the sigmoid function to a given layer
+    Given a cyrillic letter, get the target outputs.
     """
-    def _get_layer_output(self, layer: np.array):
-        return expit(layer)
+    def _get_expected_outputs(self, target: str) -> np.array:
+        index = CYRILLIC_ALPHABET.index(target)
+        expected_outputs = np.zeros(len(CYRILLIC_ALPHABET), dtype=np.int8)
+        expected_outputs[index] = 1
+
+        return expected_outputs
 
     """
     Generate a random array via an uniform distribution.