Fix weights generation

2 files changed, 19 insertions, 11 deletions

diff --git a/main.py b/main.py
index 9ca0a13..a44fbcb 100644
--- a/main.py
+++ b/main.py
@@ -1,12 +1,12 @@
-from PIL import Image
-from dataset_utils import DatasetUtils
+from dataset import Dataset
 from neural_network import NeuralNetwork
 
 LEARNING_RATE = .5
 INPUT_RESOLUTION = 278
 
+
 def main():
-    data = DatasetUtils()
+    data = Dataset()
     image = data.get_image('А/5a2f3c19c27bb.png')
     neural_network = NeuralNetwork(LEARNING_RATE, INPUT_RESOLUTION)
 
diff --git a/neural_network.py b/neural_network.py
index 68592f5..3dbd94d 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 utils import random_array
-from dataset_utils import DatasetUtils
+from dataset import Dataset
 
 CYRILLIC_ALPHABET = ['I', 'А', 'Б', 'В', 'Г', 'Д', 'Е', 'Ë', 'Ж', 'З',
                      'И', 'Й', 'К', 'Л', 'М', 'Н', 'О', 'П', 'Р', 'С',
@@ -9,14 +9,16 @@ CYRILLIC_ALPHABET = ['I', 'А', 'Б', 'В', 'Г', 'Д', 'Е', 'Ë', 'Ж', 'З',
                      'Ь', 'Э', 'Ю', 'Я']
 
 """The neural network class."""
+
+
 class NeuralNetwork:
     def __init__(self, learning_rate: float, input_resolution: int) -> None:
         self.learning_rate = learning_rate
         self.input_layer_size = input_resolution
         self.hidden_layer_size = len(CYRILLIC_ALPHABET)
-        self._hidden_weights = np.random.rand(input_resolution, self.hidden_layer_size)
-        self._output_weights = np.random.rand(input_resolution)
-
+        self._hidden_weights = self._random_array(self.hidden_layer_size,
+                                                  input_resolution)
+        self._output_weights = self._random_array(input_resolution, 1)
 
     """
     Train the neural network. It loads the dataset contained in ./data,
@@ -57,10 +59,10 @@ class NeuralNetwork:
     Feedforwarding.
     """
     def _predict(self, input_layer: np.array):
-        hidden_layer_inputs = np.dot(input_layer, self._hidden_weights)
+        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(self._output_weights, hidden_layer_outputs)
-        
+        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)
 
@@ -70,6 +72,12 @@ class NeuralNetwork:
     def _get_layer_output(self, layer: np.array):
         return expit(layer)
 
+    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).