diff options
Diffstat (limited to 'neural_network.py')
| -rw-r--r-- | neural_network.py | 25 |
1 files changed, 17 insertions, 8 deletions
diff --git a/neural_network.py b/neural_network.py index 90fd304..68592f5 100644 --- a/neural_network.py +++ b/neural_network.py @@ -1,6 +1,7 @@ import numpy as np from scipy.special import expit from utils import random_array +from dataset_utils import DatasetUtils CYRILLIC_ALPHABET = ['I', 'А', 'Б', 'В', 'Г', 'Д', 'Е', 'Ë', 'Ж', 'З', 'И', 'Й', 'К', 'Л', 'М', 'Н', 'О', 'П', 'Р', 'С', @@ -11,11 +12,10 @@ CYRILLIC_ALPHABET = ['I', 'А', 'Б', 'В', 'Г', 'Д', 'Е', 'Ë', 'Ж', 'З', class NeuralNetwork: def __init__(self, learning_rate: float, input_resolution: int) -> None: self.learning_rate = learning_rate - self.output_layer_size = len(CYRILLIC_ALPHABET) - self.input_layer_size = input_resolution ** 2 - self.hidden_layer_size = round((self.input_layer_size + self.output_layer_size) / 2) - self._hidden_weights = random_array(self.hidden_layer_size, self.input_layer_size) - self._output_weights = random_array(self.output_layer_size, self.hidden_layer_size) + 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) """ @@ -36,8 +36,10 @@ class NeuralNetwork: Guess the letter contained in the image file pointed by input_image (a path). """ - def guess(self, input_image: str) -> str: - pass + def guess(self, input_image: np.array) -> str: + output_layer = self._predict(input_image) + + return self.out(output_layer) """ Save the weights to a csv file. @@ -58,7 +60,7 @@ class NeuralNetwork: hidden_layer_inputs = np.dot(input_layer, self._hidden_weights) hidden_layer_outputs = self._get_layer_output(hidden_layer_inputs) output_layer_inputs = np.dot(self._output_weights, hidden_layer_outputs) - + # The output layer outputs. (Final output of the neural network). return self._get_layer_output(output_layer_inputs) @@ -67,3 +69,10 @@ class NeuralNetwork: """ def _get_layer_output(self, layer: np.array): return expit(layer) + + """ + 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))] |