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872天前import numpy |
import scipy.special |
import matplotlib.pyplot |
%matplotlib inline |
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.hnodes,-0.5),(self.hnodes,self.inodes)) |
self.who=numpy.random.normal(0.0,pow(self.onodes,-0.5), (self.onodes,self.hnodes)) |
self.lr=learningrate |
self.activation_function=lambda x:scipy.special.expit(x) |
pass |
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 #输出误差=目标-最终输出 |
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)) |
pass |
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 |
input_nodes=784 |
hidden_nodes=200 |
output_nodes=10 |
learning_rate=0.1 |
n=neuralNetwork(input_nodes,hidden_nodes,output_nodes,learning_rate) |
training_data_file=open("mnist_dataset/mnist_train_100.csv",'r') |
training_data_list=training_data_file.readlines() |
training_data_file.close() |
epochs=5 |
for e in range(epochs): |
for record in training_data_list: |
all_values=record.split(',') |
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) |
pass |
pass |
test_data_file=open("mnist_dataset/mnist_test_10.csv",'r') |
test_data_list=test_data_file.readlines() |
test_data_file.close() |
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's answer") |
if(label==correct_label): |
scorecard.append(1) |
else: |
scorecard.append(0) |
pass |
pass |
scorecard_array=numpy.asarray(scorecard) |
print("performance=",scorecard_array.sum()/scorecard_array.size) |
初级程序员
by: 海参OL 发表于：2020-04-27 16:28:24 顶(3) | 踩(1) 回复
我发现小白看是真的一点也看不懂
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