class NeuralNetWork: def __init__(self): ''' 初始化网络，设置输入层，中间层，输出层的节点数 ''' pass def fit(self): ''' 根据训练数据，不断更新神经网络层之间的链路权重 ''' pass def evaluate(self): ''' 输入新数据，网络给出对新数据的判断结果 ''' pass
class NeuralNetWork: def __init__(self, inputnodes, hiddenodes, outputnodes, learningRate): ''' 初始化网络，设置输入层，中间层，输出层的节点数 ''' self.input_nodes = inputnodes self.hidden_nodes = hiddenodes self.output_nodes = outputnodes self.lr = learningRate pass def fit(self): ''' 根据训练数据，不断更新神经网络层之间的链路权重 ''' pass def evaluate(self): ''' 输入新数据，网络给出对新数据的判断结果 ''' pass
input_nodes = 3 hidden_nodes = 3 output_nodes = 3 learning_rate = 0.3 n = NeuralNetWork(input_nodes, hidden_nodes, output_nodes, learning_rate)
class NeuralNetWork: def __init__(self, inputnodes, hiddenodes, outputnodes, learningRate): ''' 初始化网络，设置输入层，中间层，输出层的节点数 ''' self.input_nodes = inputnodes self.hidden_nodes = hiddenodes self.output_nodes = outputnodes self.lr = learningRate ''' 初始化权重矩阵，我们有两个权重矩阵，一个是wih表示输入层和中间层节点间链路权重形成的矩阵 一个是who,表示中间层和输出层间链路权重形成的矩阵 ''' self.wih = numpy.random.rand(self.hidden_nodes, self.input_nodes) - 0.5 self.who = numpy.random.rand(self.output_nodes, self.hidden_nodes) - 0.5 pass def fit(self): ''' 根据训练数据，不断更新神经网络层之间的链路权重 ''' pass def evaluate(self): ''' 输入新数据，网络给出对新数据的判断结果 ''' pass
import numpy import scipy.special class NeuralNetWork: def __init__(self, inputnodes, hiddenodes, outputnodes, learningRate): ''' 初始化网络，设置输入层，中间层，输出层的节点数 ''' self.input_nodes = inputnodes self.hidden_nodes = hiddenodes self.output_nodes = outputnodes self.lr = learningRate ''' 初始化权重矩阵，我们有两个权重矩阵，一个是wih表示输入层和中间层节点间链路权重形成的矩阵 一个是who,表示中间层和输出层间链路权重形成的矩阵 ''' self.wih = numpy.random.rand(self.hidden_nodes, self.input_nodes) - 0.5 self.who = numpy.random.rand(self.output_nodes, self.hidden_nodes) - 0.5 pass def fit(self): ''' 根据训练数据，不断更新神经网络层之间的链路权重 ''' pass def evaluate(self, inputs): ''' 输入新数据，网络给出对新数据的判断结果,inputs对应输入信号量。 ''' hidden_inputs = numpy.dot(self.wih, inputs) print("hidden inputs:", hidden_inputs) #sigmoid 激活函数对应于scipy.special.expit(x) sigmoid = lambda x:scipy.special.expit(x) hidden_outputs = sigmoid(hidden_inputs) #计算输出层神经元接收到的信号量 final_inputs = numpy.dot(self.who, hidden_outputs) #调用sigmoid函数计算最外层神经元的输出信号 final_outputs = sigmoid(final_inputs) print(final_outputs) pass
input_nodes = 3 hidden_nodes = 3 output_nodes = 3 learning_rate = 0.3 n = NeuralNetWork(input_nodes, hidden_nodes, output_nodes, learning_rate) n.evaluate([1.0, 0.5, -1.5])
import numpy import scipy.special class NeuralNetWork: def __init__(self, inputnodes, hiddenodes, outputnodes, learningRate): ''' 初始化网络，设置输入层，中间层，输出层的节点数 ''' self.input_nodes = inputnodes self.hidden_nodes = hiddenodes self.output_nodes = outputnodes self.lr = learningRate ''' 初始化权重矩阵，我们有两个权重矩阵，一个是wih表示输入层和中间层节点间链路权重形成的矩阵 一个是who,表示中间层和输出层间链路权重形成的矩阵 ''' self.wih = numpy.random.rand(self.hidden_nodes, self.input_nodes) - 0.5 self.who = numpy.random.rand(self.output_nodes, self.hidden_nodes) - 0.5 pass def fit(self, inputs_list, targets_list): #1,根据输入的训练数据更新节点链路权重 ''' 把inputs_list, targets_list转换成numpy支持的二维矩阵 .T表示做矩阵的转置 ''' inputs = numpy.array(inputs_list, ndmin=2).T targets = numpy.array(targets_list, ndmin=2).T #计算信号经过输入层后产生的信号量 hidden_inputs = numpy.dot(self.wih, inputs) #中间层神经元对输入的信号做激活函数后得到输出信号 #sigmoid 激活函数对应于scipy.special.expit(x) sigmoid = lambda x:scipy.special.expit(x) hidden_outputs = sigmoid(hidden_inputs) #输出层接收来自中间层的信号量 final_inputs = numpy.dot(self.who, hidden_outputs) #输出层对信号量进行激活函数后得到最终输出信号 final_outputs = sigmoid(final_inputs) #2,计算误差 output_errors = targets - final_outputs #3, 反向传播误差 hidden_errors = numpy.dot(self.who.T, output_errors) #4，根据误差计算链路权重的更新量，然后把更新加到原来链路权重上 self.who += self.lr * numpy.dot((output_errors * final_outputs *(1 - final_outputs)),numpy.transpose(hidden_outputs)) self.wih += self.lr * numpy.dot((hidden_errors * hidden_outputs * (1 - hidden_outputs)),numpy.transpose(inputs)) pass def evaluate(self, inputs): ''' 输入新数据，网络给出对新数据的判断结果,inputs对应输入信号量。 ''' hidden_inputs = numpy.dot(self.wih, inputs) #sigmoid 激活函数对应于scipy.special.expit(x) sigmoid = lambda x:scipy.special.expit(x) hidden_outputs = sigmoid(hidden_inputs) #计算输出层神经元接收到的信号量 final_inputs = numpy.dot(self.who, hidden_outputs) #调用sigmoid函数计算最外层神经元的输出信号 final_outputs = sigmoid(final_inputs) return final_outputs pass
from keras.datasets import mnist (train_images, train_labels),(test_images, test_labels) = mnist.load_data()
from keras.utils import to_categorical train_images = train_images.reshape((60000, 28*28)) train_images = train_images.astype('float32') / 255 test_images = test_images.reshape(10000, 28*28) test_images = test_images.astype('float32') / 255 train_labels = to_categorical(train_labels) test_labels = to_categorical(test_labels)
#初始化网络 input_nodes = 784 hidden_nodes = 100 output_nodes = 10 learning_rate = 0.3 n = NeuralNetWork(input_nodes, hidden_nodes, output_nodes, learning_rate) for train_image, train_label in zip(train_images, train_labels): n.fit(train_image, train_label)
scores = [] count = 10 for test_image, test_label in zip(test_images, test_labels): output = n.evaluate(test_image) evaluate_label = numpy.argmax(output) correct_label = numpy.argmax(test_label) if evaluate_label == correct_label: scores.append(1) else: scores.append(0) scores_array = numpy.asarray(scores) print("performace = ", scores_array.sum() / scores_array.size)
import matplotlib.pyplot as py (train_images, train_labels),(test_images, test_labels) = mnist.load_data() image = test_images[0] py.imshow(image, cmap='Greys', interpolation='None')
test_images = test_images.reshape(10000, 28*28) test_images = test_images.astype('float32') / 255 result = n.evaluate(test_images[0]) print(result) print("number of image is: ", numpy.argmax(result))
