OCR：一个有趣的网页版手写数字识别程序

import csv import matplotlib.pyplot as plt import matplotlib.cm as cm import numpy as np from numpy import matrix from math import pow from collections import namedtuple import math import random import os import json """ This class does some initial training of a neural network for predicting drawn digits based on a data set in data_matrix and data_labels. It can then be used to train the network further by calling train() with any array of data or to predict what a drawn digit is by calling predict(). The weights that define the neural network can be saved to a file, NN_FILE_PATH, to be reloaded upon initilization. """ class OCRNeuralNetwork: LEARNING_RATE = 0.1 WIDTH_IN_PIXELS = 20 NN_FILE_PATH = 'nn.json' def __init__(self, num_hidden_nodes, data_matrix, data_labels, training_indices, use_file=True): #True self.sigmoid = np.vectorize(self._sigmoid_scalar) self.sigmoid_prime = np.vectorize(self._sigmoid_prime_scalar) self._use_file = use_file self.data_matrix = data_matrix self.data_labels = data_labels # if (not os.path.isfile(OCRNeuralNetwork.NN_FILE_PATH) or not use_file): if (not os.path.isfile(OCRNeuralNetwork.NN_FILE_PATH) or not use_file): # Step 1: Initialize weights to small numbers self.theta1 = self._rand_initialize_weights(400, num_hidden_nodes) self.theta2 = self._rand_initialize_weights(num_hidden_nodes, 10) self.input_layer_bias = self._rand_initialize_weights(1, num_hidden_nodes) self.hidden_layer_bias = self._rand_initialize_weights(1, 10) # Train using sample data TrainData = namedtuple('TrainData', ['y0', 'label']) self.train([TrainData(self.data_matrix[i], int(self.data_labels[i])) for i in training_indices]) self.save() print("the if is run in ocr.py.") else: print("the else is run in ocr.py.") self._load() def _rand_initialize_weights(self, size_in, size_out): return [((x * 0.12) - 0.06) for x in np.random.rand(size_out, size_in)] # The sigmoid activation function. Operates on scalars. def _sigmoid_scalar(self, z): return 1 / (1 + math.e ** -z) def _sigmoid_prime_scalar(self, z): return self.sigmoid(z) * (1 - self.sigmoid(z)) def _draw(self, sample): pixelArray=np.array(sample).reshape(20,20) plt.imshow(zip(*pixelArray), cmap=cm.Greys_r, interpolation = "nearest") # pixelArray = [sample[j:j+self.WIDTH_IN_PIXELS] for j in xrange(0, len(sample), self.WIDTH_IN_PIXELS)] # plt.imshow(zip(*pixelArray), cmap = cm.Greys_r) # , interpolation = "nearest" ,norm="Normalize" plt.show() def train(self, training_data_array): for data in training_data_array: # Step 2: Forward propagation y1 = np.dot(np.mat(self.theta1), np.mat(data.y0).T) sum1 = y1 + np.mat(self.input_layer_bias) # Add the bias y1 = self.sigmoid(sum1) y2 = np.dot(np.array(self.theta2), y1) y2 = np.add(y2, self.hidden_layer_bias) # Add the bias y2 = self.sigmoid(y2) # Step 3: Back propagation actual_vals = [0] * 10 # actual_vals is a python list for easy initialization and is later turned into an np matrix (2 lines down). actual_vals[data.label] = 1 output_errors = np.mat(actual_vals).T - np.mat(y2) hidden_errors = np.multiply(np.dot(np.mat(self.theta2).T, output_errors), self.sigmoid_prime(sum1)) # Step 4: Update weights self.theta1 += self.LEARNING_RATE * np.dot(np.mat(hidden_errors), np.mat(data.y0)) self.theta2 += self.LEARNING_RATE * np.dot(np.mat(output_errors), np.mat(y1).T) self.hidden_layer_bias += self.LEARNING_RATE * output_errors self.input_layer_bias += self.LEARNING_RATE * hidden_errors def predict(self, test): y1 = np.dot(np.mat(self.theta1), np.mat(test).T) y1 = y1 + np.mat(self.input_layer_bias) # Add the bias y1 = self.sigmoid(y1) y2 = np.dot(np.array(self.theta2), y1) y2 = np.add(y2, self.hidden_layer_bias) # Add the bias y2 = self.sigmoid(y2) results = y2.T.tolist()[0] return results.index(max(results)) def save(self): # if not self._use_file: # return json_neural_network = { "theta1":[np_mat.tolist()[0] for np_mat in self.theta1], "theta2":[np_mat.tolist()[0] for np_mat in self.theta2], "b1":self.input_layer_bias[0].tolist()[0], "b2":self.hidden_layer_bias[0].tolist()[0] }; with open(OCRNeuralNetwork.NN_FILE_PATH,'w') as nnFile: json.dump(json_neural_network, nnFile) def _load(self): if not self._use_file: return with open(OCRNeuralNetwork.NN_FILE_PATH) as nnFile: nn = json.load(nnFile) self.theta1 = [np.array(li) for li in nn['theta1']] self.theta2 = [np.array(li) for li in nn['theta2']] self.input_layer_bias = [np.array(nn['b1'][0])] self.hidden_layer_bias = [np.array(nn['b2'][0])]