A basic framework to construct a neural network system.
In this example,I used it to classify texts.(It is not well constructed)
Creating a dataset:
from dataset.dataset_fetcher import Dataset,Sinset
train_set = Dataset()#Classification dataset
sin_set = Sinset()#Regression dataset
Creating a network:
from nn.network import Network
network=Network(train_set)
Adding layers to the network:
network.append_linear_layer(node_count)
network.append_activation_layer(type="Sigmoid")#ReLU , Tanh , etc
......
network.append_linear_layer(output_node_count)
Training your network:
network.train_repeatly(train_count)
Reading result:
network.final_result
Network evaluation:
from util.evaluator import Evaluator:
#using train set as the test set to self-evaluate
self_evaluator = Evaluator(train_set)
self_evaluator.clf_evaluate()#classification evaluating
#self_evaluator.reg_evaluate()#regression evaluating
- Numpy
- Matplotlib
- Scikit learn(Used to fetch text training source)
Sine function is well predicted- Text classifier did not work well,accuracy remains about 40%,suffering from overfitting problem.
- Mnist classifier works fine,accuray reaches 88%+