mirror of
https://github.com/TheAlgorithms/Python.git
synced 2024-12-25 12:40:14 +00:00
306 lines
13 KiB
Python
306 lines
13 KiB
Python
#-*- coding: utf-8 -*-
|
||
|
||
'''
|
||
- - - - - -- - - - - - - - - - - - - - - - - - - - - - -
|
||
Name - - CNN - Convolution Neural Network For Photo Recognizing
|
||
Goal - - Recognize Handing Writting Word Photo
|
||
Detail：Total 5 layers neural network
|
||
* Convolution layer
|
||
* Pooling layer
|
||
* Input layer layer of BP
|
||
* Hiden layer of BP
|
||
* Output layer of BP
|
||
Author: Stephen Lee
|
||
Github: 245885195@qq.com
|
||
Date: 2017.9.20
|
||
- - - - - -- - - - - - - - - - - - - - - - - - - - - - -
|
||
'''
|
||
from __future__ import print_function
|
||
|
||
import numpy as np
|
||
import matplotlib.pyplot as plt
|
||
|
||
class CNN():
|
||
|
||
def __init__(self,conv1_get,size_p1,bp_num1,bp_num2,bp_num3,rate_w=0.2,rate_t=0.2):
|
||
'''
|
||
:param conv1_get: [a,c,d]，size, number, step of convolution kernel
|
||
:param size_p1: pooling size
|
||
:param bp_num1: units number of flatten layer
|
||
:param bp_num2: units number of hidden layer
|
||
:param bp_num3: units number of output layer
|
||
:param rate_w: rate of weight learning
|
||
:param rate_t: rate of threshold learning
|
||
'''
|
||
self.num_bp1 = bp_num1
|
||
self.num_bp2 = bp_num2
|
||
self.num_bp3 = bp_num3
|
||
self.conv1 = conv1_get[:2]
|
||
self.step_conv1 = conv1_get[2]
|
||
self.size_pooling1 = size_p1
|
||
self.rate_weight = rate_w
|
||
self.rate_thre = rate_t
|
||
self.w_conv1 = [np.mat(-1*np.random.rand(self.conv1[0],self.conv1[0])+0.5) for i in range(self.conv1[1])]
|
||
self.wkj = np.mat(-1 * np.random.rand(self.num_bp3, self.num_bp2) + 0.5)
|
||
self.vji = np.mat(-1*np.random.rand(self.num_bp2, self.num_bp1)+0.5)
|
||
self.thre_conv1 = -2*np.random.rand(self.conv1[1])+1
|
||
self.thre_bp2 = -2*np.random.rand(self.num_bp2)+1
|
||
self.thre_bp3 = -2*np.random.rand(self.num_bp3)+1
|
||
|
||
|
||
def save_model(self,save_path):
|
||
#save model dict with pickle
|
||
import pickle
|
||
model_dic = {'num_bp1':self.num_bp1,
|
||
'num_bp2':self.num_bp2,
|
||
'num_bp3':self.num_bp3,
|
||
'conv1':self.conv1,
|
||
'step_conv1':self.step_conv1,
|
||
'size_pooling1':self.size_pooling1,
|
||
'rate_weight':self.rate_weight,
|
||
'rate_thre':self.rate_thre,
|
||
'w_conv1':self.w_conv1,
|
||
'wkj':self.wkj,
|
||
'vji':self.vji,
|
||
'thre_conv1':self.thre_conv1,
|
||
'thre_bp2':self.thre_bp2,
|
||
'thre_bp3':self.thre_bp3}
|
||
with open(save_path, 'wb') as f:
|
||
pickle.dump(model_dic, f)
|
||
|
||
print('Model saved： %s'% save_path)
|
||
|
||
@classmethod
|
||
def ReadModel(cls,model_path):
|
||
#read saved model
|
||
import pickle
|
||
with open(model_path, 'rb') as f:
|
||
model_dic = pickle.load(f)
|
||
|
||
conv_get= model_dic.get('conv1')
|
||
conv_get.append(model_dic.get('step_conv1'))
|
||
size_p1 = model_dic.get('size_pooling1')
|
||
bp1 = model_dic.get('num_bp1')
|
||
bp2 = model_dic.get('num_bp2')
|
||
bp3 = model_dic.get('num_bp3')
|
||
r_w = model_dic.get('rate_weight')
|
||
r_t = model_dic.get('rate_thre')
|
||
#create model instance
|
||
conv_ins = CNN(conv_get,size_p1,bp1,bp2,bp3,r_w,r_t)
|
||
#modify model parameter
|
||
conv_ins.w_conv1 = model_dic.get('w_conv1')
|
||
conv_ins.wkj = model_dic.get('wkj')
|
||
conv_ins.vji = model_dic.get('vji')
|
||
conv_ins.thre_conv1 = model_dic.get('thre_conv1')
|
||
conv_ins.thre_bp2 = model_dic.get('thre_bp2')
|
||
conv_ins.thre_bp3 = model_dic.get('thre_bp3')
|
||
return conv_ins
|
||
|
||
|
||
def sig(self,x):
|
||
return 1 / (1 + np.exp(-1*x))
|
||
|
||
def do_round(self,x):
|
||
return round(x, 3)
|
||
|
||
def convolute(self,data,convs,w_convs,thre_convs,conv_step):
|
||
#convolution process
|
||
size_conv = convs[0]
|
||
num_conv =convs[1]
|
||
size_data = np.shape(data)[0]
|
||
#get the data slice of original image data, data_focus
|
||
data_focus = []
|
||
for i_focus in range(0, size_data - size_conv + 1, conv_step):
|
||
for j_focus in range(0, size_data - size_conv + 1, conv_step):
|
||
focus = data[i_focus:i_focus + size_conv, j_focus:j_focus + size_conv]
|
||
data_focus.append(focus)
|
||
#caculate the feature map of every single kernel, and saved as list of matrix
|
||
data_featuremap = []
|
||
Size_FeatureMap = int((size_data - size_conv) / conv_step + 1)
|
||
for i_map in range(num_conv):
|
||
featuremap = []
|
||
for i_focus in range(len(data_focus)):
|
||
net_focus = np.sum(np.multiply(data_focus[i_focus], w_convs[i_map])) - thre_convs[i_map]
|
||
featuremap.append(self.sig(net_focus))
|
||
featuremap = np.asmatrix(featuremap).reshape(Size_FeatureMap, Size_FeatureMap)
|
||
data_featuremap.append(featuremap)
|
||
|
||
#expanding the data slice to One dimenssion
|
||
focus1_list = []
|
||
for each_focus in data_focus:
|
||
focus1_list.extend(self.Expand_Mat(each_focus))
|
||
focus_list = np.asarray(focus1_list)
|
||
return focus_list,data_featuremap
|
||
|
||
def pooling(self,featuremaps,size_pooling,type='average_pool'):
|
||
#pooling process
|
||
size_map = len(featuremaps[0])
|
||
size_pooled = int(size_map/size_pooling)
|
||
featuremap_pooled = []
|
||
for i_map in range(len(featuremaps)):
|
||
map = featuremaps[i_map]
|
||
map_pooled = []
|
||
for i_focus in range(0,size_map,size_pooling):
|
||
for j_focus in range(0, size_map, size_pooling):
|
||
focus = map[i_focus:i_focus + size_pooling, j_focus:j_focus + size_pooling]
|
||
if type == 'average_pool':
|
||
#average pooling
|
||
map_pooled.append(np.average(focus))
|
||
elif type == 'max_pooling':
|
||
#max pooling
|
||
map_pooled.append(np.max(focus))
|
||
map_pooled = np.asmatrix(map_pooled).reshape(size_pooled,size_pooled)
|
||
featuremap_pooled.append(map_pooled)
|
||
return featuremap_pooled
|
||
|
||
def _expand(self,datas):
|
||
#expanding three dimension data to one dimension list
|
||
data_expanded = []
|
||
for i in range(len(datas)):
|
||
shapes = np.shape(datas[i])
|
||
data_listed = datas[i].reshape(1,shapes[0]*shapes[1])
|
||
data_listed = data_listed.getA().tolist()[0]
|
||
data_expanded.extend(data_listed)
|
||
data_expanded = np.asarray(data_expanded)
|
||
return data_expanded
|
||
|
||
def _expand_mat(self,data_mat):
|
||
#expanding matrix to one dimension list
|
||
data_mat = np.asarray(data_mat)
|
||
shapes = np.shape(data_mat)
|
||
data_expanded = data_mat.reshape(1,shapes[0]*shapes[1])
|
||
return data_expanded
|
||
|
||
def _calculate_gradient_from_pool(self,out_map,pd_pool,num_map,size_map,size_pooling):
|
||
'''
|
||
calcluate the gradient from the data slice of pool layer
|
||
pd_pool: list of matrix
|
||
out_map: the shape of data slice(size_map*size_map)
|
||
return: pd_all: list of matrix, [num, size_map, size_map]
|
||
'''
|
||
pd_all = []
|
||
i_pool = 0
|
||
for i_map in range(num_map):
|
||
pd_conv1 = np.ones((size_map, size_map))
|
||
for i in range(0, size_map, size_pooling):
|
||
for j in range(0, size_map, size_pooling):
|
||
pd_conv1[i:i + size_pooling, j:j + size_pooling] = pd_pool[i_pool]
|
||
i_pool = i_pool + 1
|
||
pd_conv2 = np.multiply(pd_conv1,np.multiply(out_map[i_map],(1-out_map[i_map])))
|
||
pd_all.append(pd_conv2)
|
||
return pd_all
|
||
|
||
def trian(self,patterns,datas_train, datas_teach, n_repeat, error_accuracy,draw_e = bool):
|
||
#model traning
|
||
print('----------------------Start Training-------------------------')
|
||
print((' - - Shape: Train_Data ',np.shape(datas_train)))
|
||
print((' - - Shape: Teach_Data ',np.shape(datas_teach)))
|
||
rp = 0
|
||
all_mse = []
|
||
mse = 10000
|
||
while rp < n_repeat and mse >= error_accuracy:
|
||
alle = 0
|
||
print('-------------Learning Time %d--------------'%rp)
|
||
for p in range(len(datas_train)):
|
||
#print('------------Learning Image: %d--------------'%p)
|
||
data_train = np.asmatrix(datas_train[p])
|
||
data_teach = np.asarray(datas_teach[p])
|
||
data_focus1,data_conved1 = self.convolute(data_train,self.conv1,self.w_conv1,
|
||
self.thre_conv1,conv_step=self.step_conv1)
|
||
data_pooled1 = self.pooling(data_conved1,self.size_pooling1)
|
||
shape_featuremap1 = np.shape(data_conved1)
|
||
'''
|
||
print(' -----original shape ', np.shape(data_train))
|
||
print(' ---- after convolution ',np.shape(data_conv1))
|
||
print(' -----after pooling ',np.shape(data_pooled1))
|
||
'''
|
||
data_bp_input = self._expand(data_pooled1)
|
||
bp_out1 = data_bp_input
|
||
|
||
bp_net_j = np.dot(bp_out1,self.vji.T) - self.thre_bp2
|
||
bp_out2 = self.sig(bp_net_j)
|
||
bp_net_k = np.dot(bp_out2 ,self.wkj.T) - self.thre_bp3
|
||
bp_out3 = self.sig(bp_net_k)
|
||
|
||
#--------------Model Leaning ------------------------
|
||
# calcluate error and gradient---------------
|
||
pd_k_all = np.multiply((data_teach - bp_out3), np.multiply(bp_out3, (1 - bp_out3)))
|
||
pd_j_all = np.multiply(np.dot(pd_k_all,self.wkj), np.multiply(bp_out2, (1 - bp_out2)))
|
||
pd_i_all = np.dot(pd_j_all,self.vji)
|
||
|
||
pd_conv1_pooled = pd_i_all / (self.size_pooling1*self.size_pooling1)
|
||
pd_conv1_pooled = pd_conv1_pooled.T.getA().tolist()
|
||
pd_conv1_all = self._calculate_gradient_from_pool(data_conved1,pd_conv1_pooled,shape_featuremap1[0],
|
||
shape_featuremap1[1],self.size_pooling1)
|
||
#weight and threshold learning process---------
|
||
#convolution layer
|
||
for k_conv in range(self.conv1[1]):
|
||
pd_conv_list = self._expand_mat(pd_conv1_all[k_conv])
|
||
delta_w = self.rate_weight * np.dot(pd_conv_list,data_focus1)
|
||
|
||
self.w_conv1[k_conv] = self.w_conv1[k_conv] + delta_w.reshape((self.conv1[0],self.conv1[0]))
|
||
|
||
self.thre_conv1[k_conv] = self.thre_conv1[k_conv] - np.sum(pd_conv1_all[k_conv]) * self.rate_thre
|
||
#all connected layer
|
||
self.wkj = self.wkj + pd_k_all.T * bp_out2 * self.rate_weight
|
||
self.vji = self.vji + pd_j_all.T * bp_out1 * self.rate_weight
|
||
self.thre_bp3 = self.thre_bp3 - pd_k_all * self.rate_thre
|
||
self.thre_bp2 = self.thre_bp2 - pd_j_all * self.rate_thre
|
||
# calculate the sum error of all single image
|
||
errors = np.sum(abs((data_teach - bp_out3)))
|
||
alle = alle + errors
|
||
#print(' ----Teach ',data_teach)
|
||
#print(' ----BP_output ',bp_out3)
|
||
rp = rp + 1
|
||
mse = alle/patterns
|
||
all_mse.append(mse)
|
||
def draw_error():
|
||
yplot = [error_accuracy for i in range(int(n_repeat * 1.2))]
|
||
plt.plot(all_mse, '+-')
|
||
plt.plot(yplot, 'r--')
|
||
plt.xlabel('Learning Times')
|
||
plt.ylabel('All_mse')
|
||
plt.grid(True, alpha=0.5)
|
||
plt.show()
|
||
print('------------------Training Complished---------------------')
|
||
print((' - - Training epoch: ', rp, ' - - Mse: %.6f' % mse))
|
||
if draw_e:
|
||
draw_error()
|
||
return mse
|
||
|
||
def predict(self,datas_test):
|
||
#model predict
|
||
produce_out = []
|
||
print('-------------------Start Testing-------------------------')
|
||
print((' - - Shape: Test_Data ',np.shape(datas_test)))
|
||
for p in range(len(datas_test)):
|
||
data_test = np.asmatrix(datas_test[p])
|
||
data_focus1, data_conved1 = self.convolute(data_test, self.conv1, self.w_conv1,
|
||
self.thre_conv1, conv_step=self.step_conv1)
|
||
data_pooled1 = self.pooling(data_conved1, self.size_pooling1)
|
||
data_bp_input = self._expand(data_pooled1)
|
||
|
||
bp_out1 = data_bp_input
|
||
bp_net_j = bp_out1 * self.vji.T - self.thre_bp2
|
||
bp_out2 = self.sig(bp_net_j)
|
||
bp_net_k = bp_out2 * self.wkj.T - self.thre_bp3
|
||
bp_out3 = self.sig(bp_net_k)
|
||
produce_out.extend(bp_out3.getA().tolist())
|
||
res = [list(map(self.do_round,each)) for each in produce_out]
|
||
return np.asarray(res)
|
||
|
||
def convolution(self,data):
|
||
#return the data of image after convoluting process so we can check it out
|
||
data_test = np.asmatrix(data)
|
||
data_focus1, data_conved1 = self.convolute(data_test, self.conv1, self.w_conv1,
|
||
self.thre_conv1, conv_step=self.step_conv1)
|
||
data_pooled1 = self.pooling(data_conved1, self.size_pooling1)
|
||
|
||
return data_conved1,data_pooled1
|
||
|
||
|
||
if __name__ == '__main__':
|
||
pass
|
||
'''
|
||
I will put the example on other file
|
||
''' |