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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',
'Т', 'У', 'Ф', 'Х', 'Ц', 'Ч', 'Ш', 'Щ', 'Ъ', 'Ы',
'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)
"""
Train the neural network. It loads the dataset contained in ./data,
converts each image into a numpy array and uses that data for training.
Algorithm:
1-. Feedforward the input matrix.
2-. Calculate the output layer error.
3-. Adjust the weights of the output layer via gradient descent.
3-. Calculate the hidden layer error by backpropagating the output layer
error.
4-. Adjust the weights of the hidden layer via gradient descent.
"""
def train(self):
pass
"""
Guess the letter contained in the image file pointed by
input_image (a path).
"""
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.
"""
def save(self):
pass
"""
Load the weights from a csv file.
"""
def load(self, weights_file: str):
pass
"""
Feedforwarding.
"""
def _predict(self, input_layer: np.array):
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)
"""
Apply the sigmoid function to a given layer
"""
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))]
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