NeuralNetwork-Numpy
Code download link ： The code download

pattern recognition Use numpy Deep learning

 Please refer to main.ipynb

Network core

net.py
conv.py

Usage method

 Defining network

from net import ConvNet
net = ConvNet()
if not net.load(MODEL_PATH):
net.addConvLayout([3,3,1,4],bias = True,padding='VAILD',init_type=init_type,st_func='LEAKY_RELU_0.01')
net.addConvLayout([3,3,4,8],bias = True,padding='VAILD',init_type=init_type,st_func='LEAKY_RELU_0.01')
net.addConvLayout([5,5,8,16],bias = True,padding='VAILD',init_type=init_type,st_func='LEAKY_RELU_0.01')
net.addConvLayout([5,5,16,32],bias = True,padding='VAILD',init_type=init_type,st_func='LEAKY_RELU_0.01')
net.addConvLayout([16,16,32,64],bias = True,padding='VAILD',st_func='SIGMOID',init_type=init_type)
net.addConvLayout([1,1,64,10],bias = True,padding='VAILD',st_func='SIGMOID',init_type=init_type)

addData()： Add data
addConvLayout()： Add a layer of network at the end of the current network

Support convolution layer , Fully connected layer ,Batch Normalization layer

The activation function supports sigmoid,leaky_relu_alpha,alpha It can be any value

regress()： Return to

Support MSE And cross entropy Support multiple optimizers （SGD,Nesterov,RMSProp）

count()： Calculate the output of each layer
save()： Save weights
load()： Weight reading

function mnist_train Experience training

test.py Visual output of

Network structure output

print(net) Output network structure

notes : Required before input addData(), and count once

mnist Visual output

 function mnist_visual_test.py Testable mnist Data sets
model Inside the folder is mnist Training model , The latest model is only in batch_size=30 Training under the circumstances of 400 Time , Our test accuracy on the test set has reached :95.38%. The model structure used is shown in the figure above ,4 Convolution layer +2 Layer full connection layer .

function main.ipynb Conduct personalized testing