import numpy import scipy.special import matplotlib.pyplot as plt import scipy.misc import glob import imageio import scipy.ndimage class neuralNetWork: def __init__(self,inputnodes,hiddennodes,outputnodes,learningrate): self.inodes = inputnodes self.hnodes = hiddennodes self.onodes = outputnodes self.wih = numpy.random.normal(0.0,pow(self.inodes, -0.5),(self.hnodes,self.inodes)) self.who = numpy.random.normal(0.0,pow(self.hnodes, -0.5),(self.onodes,self.hnodes)) self.lr = learningrate self.activation_function = lambda x: scipy.special.expit(x) # 激活函数 self.inverse_activation_function = lambda x: scipy.special.logit(x) # 反向查询log激活函数 def train(self,inputs_list,targets_list): inputs = numpy.array(inputs_list,ndmin=2).T targets = numpy.array(targets_list,ndmin=2).T hidden_inputs = numpy.dot(self.wih,inputs) hidden_outputs = self.activation_function(hidden_inputs) final_inputs = numpy.dot(self.who,hidden_outputs) final_outputs = self.activation_function(final_inputs) output_errors = targets - final_outputs hidden_errors = numpy.dot(self.who.T,output_errors) self.who += self.lr * numpy.dot((output_errors * final_outputs * (1.0 - final_outputs)),numpy.transpose(hidden_outputs)) self.wih += self.lr * numpy.dot((hidden_errors * hidden_outputs * (1.0 - hidden_outputs)),numpy.transpose(inputs)) def query(self,inputs_list): inputs = numpy.array(inputs_list,ndmin=2).T hidden_inputs = numpy.dot(self.wih,inputs) hidden_outputs = self.activation_function(hidden_inputs) final_inputs = numpy.dot(self.who,hidden_outputs) final_outputs = self.activation_function(final_inputs) return final_outputs def backquery(self, targets_list): final_outputs = numpy.array(targets_list, ndmin=2).T final_inputs = self.inverse_activation_function(final_outputs) hidden_outputs = numpy.dot(self.who.T, final_inputs) hidden_outputs -= numpy.min(hidden_outputs) hidden_outputs /= numpy.max(hidden_outputs) hidden_outputs *= 0.98 hidden_outputs += 0.01 hidden_inputs = self.inverse_activation_function(hidden_outputs) inputs = numpy.dot(self.wih.T, hidden_inputs) inputs -= numpy.min(inputs) inputs /= numpy.max(inputs) inputs *= 0.98 inputs += 0.01 return inputs input_nodes = 784 hidden_nodes = 200 output_nodes = 10 learing_rate = 0.1 n = neuralNetWork(input_nodes,hidden_nodes,output_nodes,learing_rate) train_data_file = open('mnist_train.csv', 'r') train_data_list = train_data_file.readlines() train_data_file.close() epochs = 5 for e in range(epochs): for record in train_data_list: all_values = record.split(',') #image_array = numpy.asfarray(all_values[1:]).reshape((28,28)) #plt.imshow(image_array,cmap='Greys',interpolation='None') #plt.show() inputs = (numpy.asfarray(all_values[1:])/255.0 *0.99)+0.01 targets = numpy.zeros(output_nodes) + 0.01 targets[int(all_values[0])] = 0.99 n.train(inputs,targets) #手写字体倾斜10度作为测试数据 inputs_plusx_img = scipy.ndimage.interpolation.rotate(inputs.reshape(28,28), 10, cval=0.01, order=1, reshape=False) n.train(inputs_plusx_img.reshape(784), targets) inputs_minusx_img = scipy.ndimage.interpolation.rotate(inputs.reshape(28,28), -10, cval=0.01, order=1, reshape=False) n.train(inputs_minusx_img.reshape(784), targets) test_data_file = open('mnist_test.csv', 'r') test_data_list = test_data_file.readlines() test_data_file.close() # all_values = test_data_list[0].split(',') # # image_array = numpy.asfarray(all_values[1:]).reshape((28,28)) # # plt.imshow(image_array,cmap='Greys',interpolation='None') # # plt.show() # output = n.query((numpy.asfarray(all_values[1:])/ 255.0 * 0.99)+0.01) scorecard = [] for record in test_data_list: all_values = record.split(',') correct_label = int(all_values[0]) #print(correct_label,'correct_label') inputs = (numpy.asfarray(all_values[1:])/255.0 *0.99)+0.01 outputs = n.query(inputs) label = numpy.argmax(outputs) #print(label,'network answer') if (label == correct_label): scorecard.append(1) else: scorecard.append(0) scorecard_array = numpy.asarray(scorecard) print("performance = ",scorecard_array.sum() / scorecard_array.size) # 识别自己手写字 our_own_dataset = [] for image_file_name in glob.glob('2828_my_own_?.png'): label = int(image_file_name[-5:-4]) print ("loading ... ", image_file_name) img_array = imageio.imread(image_file_name, as_gray=True) img_data = 255.0 - img_array.reshape(784) img_data = (img_data / 255.0 * 0.99) + 0.01 print(numpy.min(img_data)) print(numpy.max(img_data)) record = numpy.append(label,img_data) our_own_dataset.append(record) item = 2 plt.imshow(our_own_dataset[item][1:].reshape(28,28), cmap='Greys', interpolation='None') correct_label = our_own_dataset[item][0] inputs = our_own_dataset[item][1:] outputs = n.query(inputs) print (outputs) label = numpy.argmax(outputs) print("network says ", label) if (label == correct_label): print ("match!") else: print ("no match!") # 反向生成图像 label = 0 targets = numpy.zeros(output_nodes) + 0.01 targets[label] = 0.99 print(targets) image_data = n.backquery(targets) plt.imshow(image_data.reshape(28,28), cmap='Greys', interpolation='None')