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FCBP.py
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FCBP.py
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import numpy
import scipy.special
import matplotlib.pyplot
class neuralNetwork :
#initialise the neural network
def __init__(self, inputnodes, hiddennodes, outputnodes, learningrate) :
# set number of nodes in each input, hidden, output layer
self.inodes = inputnodes
self.hnodes = hiddennodes
self.onodes = outputnodes
# link weight matrices, wih and who
# weights inside the arrays are w_i_j, where link is from
# node i to node j in the next layer
# w11 w21
# w12 w22 etc
self.wih = numpy.random.normal(0.0, pow(self.hnodes, 0.5),
(self.hnodes, self.inodes))
self.who = numpy.random.normal(0.0, pow(self.onodes, 0.5),
(self.onodes, self.hnodes))
self.read();
# learning rate
self.lr = learningrate
# activation function is the sigmoid function
self.activation_function = lambda x: scipy.special.expit(x)
self.n=1
self.m=1
pass
# train the neural network
def train(self, inputs_list, targets_list) :
# convert inputs list to 2d array
inputs = numpy.array(inputs_list, ndmin=2).T
targets = numpy.array(targets_list, ndmin=2).T
# calculate signals into hidden layer
hidden_inputs = numpy.dot(self.wih, inputs)
# calculate the signals emerging from hidden layer
hidden_outputs = self.activation_function(hidden_inputs)
# calculate signals into final output layer
final_inputs = numpy.dot(self.who, hidden_outputs)
# calculate the signals emerging from final output layer
final_outputs = self.activation_function(final_inputs)
# output layer error is the (target actual)
output_errors = targets - final_outputs
t=targets.argmax()==final_outputs.argmax()
if t: self.m=self.m+1
self.n=self.n+1
if self.n%10000==0:
cc=self.m/10000
if cc>0.9:self.lr=0.01
print(self.n,cc,numpy.sqrt(numpy.sum(output_errors**2)/10))
self.m=0
# hidden layer error is the output_errors, split by weights, recombined at hidden nodes
hidden_errors = numpy.dot(self.who.T, output_errors)
# update the weights for the links between the hidden and output layers
self.who += self.lr * numpy.dot((output_errors * final_outputs * (1.0 - final_outputs)),
numpy.transpose(hidden_outputs))
# update the weights for the links between the input and hidden layers
self.wih += self.lr * numpy.dot((hidden_errors * hidden_outputs * (1.0 - hidden_outputs)),
numpy.transpose(inputs))
#numpy.savetxt(r"C:/Z/wh.csv",self.who,fmt='%.2f')
pass
# query the neural network
def query(self, inputs_list) :
# convert inputs list to 2d array
inputs = numpy.array(inputs_list, ndmin=2).T
# calculate signals into hidden layer
hidden_inputs = numpy.dot(self.wih, inputs)
# calculate the signals emerging from hidden layer
hidden_outputs = self.activation_function(hidden_inputs)
# calculate signals into final output layer
final_inputs = numpy.dot(self.who, hidden_outputs)
# calculate the signals emerging from final output layer
final_outputs = self.activation_function(final_inputs)
return final_outputs
def save(self):
numpy.savetxt(r"C:/Z/who.csv",self.who,fmt='%.2f')
numpy.savetxt(r"C:/Z/wih.csv",self.wih,fmt='%.2f')
pass
def read(self):
#self.who = numpy.loadtxt(r"C:/Z/who.csv")
#self.wih = numpy.loadtxt(r"C:/Z/wih.csv")
#print(self.who)
pass
input_nodes = 784
hidden_nodes = 200
output_nodes = 10
# learning rate
learning_rate = 0.1
# create instance of neural network
n = neuralNetwork(input_nodes,hidden_nodes,output_nodes, learning_rate)
data_file = open(r"C:\搜狗高速下载\mnist_train.csv", 'r') #一个可读的文件对象
# 因为文件不大，所以一次读了整个文件。理论上应该一行一行的读。
training_data_list = data_file .readlines() # 返回一个数据列表，data_list[i]表示第i样本
data_file.close()
#print(len(training_data_list))
#all_values = training_data_list[7].split(',')
#image_array = numpy.array(all_values[1:], dtype=float).reshape((28,28))
#print(all_values[0:1])
#matplotlib.pyplot.imshow(image_array)#, cmap='Greys', interpolation='None')
#matplotlib.pyplot.show()
epochs = 1
for e in range(epochs):
# go through all records in the training data set
print(e,"_____________________________________________________________________________________________________")
for record in training_data_list[1:60000]:
# split the record by the ',' commas
all_values = record.split(',')
# scale and shift the inputs
inputs = (numpy.asfarray(all_values[1:]) / 255.0 * 0.99) + 0.01
# create the target output values (all 0.01, except the desired label which is 0.99)
targets = numpy.zeros(output_nodes) + 0.01
# all_values[0] is the target label for this record
targets[int(all_values[0])] = 0.99
n.train(inputs, targets)
pass
n.save()
# 加载测试集
# load the mnist test data CSV file into a list
test_data_file = open(r"C:\搜狗高速下载\mnist_test.csv", 'r')
test_data_list = test_data_file.readlines()
test_data_file.close()
#print(len(test_data_list))
# 测试神经网络
#test_data_list=training_data_list
# test the neural network
# scorecard for how well the network performs, initially empty
scorecard = []
# go through all the records in the test data set
for record in test_data_list[1:100]:
# split the record by the ',' commas
all_values = record.split(',')
# correct answer is first value
correct_label = int(all_values[0])
# scale and shift the inputs
inputs = (numpy.asfarray(all_values[1:]) / 255.0 * 0.99) + 0.01
# query the network
outputs = n.query(inputs)
# the index of the highest value corresponds to the label
label = numpy.argmax(outputs)
# append correct or incorrect to list
if (label == correct_label):
# network's answer matches correct answer, add 1 to scorecard
scorecard.append(1)
else:
# network's answer doesn't match correct answer, add 0 to scorecard
scorecard.append(0)
pass
pass
a=numpy.asarray(scorecard)
print(a.sum())
print(a.size)
print(a.sum()/a.size)