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Fix spelling in neural_network/convolution_neural_network.py (#849)

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* Fix spelling in neural_network/convolution_neural_network.py

* fix import

Signed-off-by: cedric.farinazzo <cedric.farinazzo@epita.fr>
This commit is contained in:
cedricfarinazzo 2019-05-30 02:47:00 +02:00 committed by John Law
parent fc95e7a91a
commit 9037abae11
1 changed files with 33 additions and 34 deletions
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67
neural_network/convolution_neural_network.py
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@ -14,15 +14,16 @@
Github: 245885195@qq.com
Date: 2017.9.20
- - - - - -- - - - - - - - - - - - - - - - - - - - - - -
'''
'''
from __future__ import print_function
import pickle
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):
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
@ -48,32 +49,30 @@ class CNN():
self.thre_bp3 = -2*np.random.rand(self.num_bp3)+1
def save_model(self,save_path):
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}
'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):
def ReadModel(cls, model_path):
#read saved model
import pickle
with open(model_path, 'rb') as f:
model_dic = pickle.load(f)
@ -97,13 +96,13 @@ class CNN():
return conv_ins
def sig(self,x):
def sig(self, x):
return 1 / (1 + np.exp(-1*x))
def do_round(self,x):
def do_round(self, x):
return round(x, 3)
def convolute(self,data,convs,w_convs,thre_convs,conv_step):
def convolute(self, data, convs, w_convs, thre_convs, conv_step):
#convolution process
size_conv = convs[0]
num_conv =convs[1]
@ -132,7 +131,7 @@ class CNN():
focus_list = np.asarray(focus1_list)
return focus_list,data_featuremap
def pooling(self,featuremaps,size_pooling,type='average_pool'):
def pooling(self, featuremaps, size_pooling, type='average_pool'):
#pooling process
size_map = len(featuremaps[0])
size_pooled = int(size_map/size_pooling)
@ -153,7 +152,7 @@ class CNN():
featuremap_pooled.append(map_pooled)
return featuremap_pooled
def _expand(self,datas):
def _expand(self, datas):
#expanding three dimension data to one dimension list
data_expanded = []
for i in range(len(datas)):
@ -164,14 +163,14 @@ class CNN():
data_expanded = np.asarray(data_expanded)
return data_expanded
def _expand_mat(self,data_mat):
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):
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
@ -190,7 +189,7 @@ class CNN():
pd_all.append(pd_conv2)
return pd_all
def trian(self,patterns,datas_train, datas_teach, n_repeat, error_accuracy,draw_e = bool):
def train(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)))
@ -206,7 +205,7 @@ class CNN():
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)
self.thre_conv1,conv_step=self.step_conv1)
data_pooled1 = self.pooling(data_conved1,self.size_pooling1)
shape_featuremap1 = np.shape(data_conved1)
'''
@ -231,7 +230,7 @@ class CNN():
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)
shape_featuremap1[1],self.size_pooling1)
#weight and threshold learning process---------
#convolution layer
for k_conv in range(self.conv1[1]):
@ -268,7 +267,7 @@ class CNN():
draw_error()
return mse
def predict(self,datas_test):
def predict(self, datas_test):
#model predict
produce_out = []
print('-------------------Start Testing-------------------------')
@ -276,7 +275,7 @@ class CNN():
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)
self.thre_conv1, conv_step=self.step_conv1)
data_pooled1 = self.pooling(data_conved1, self.size_pooling1)
data_bp_input = self._expand(data_pooled1)
@ -289,11 +288,11 @@ class CNN():
res = [list(map(self.do_round,each)) for each in produce_out]
return np.asarray(res)
def convolution(self,data):
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)
self.thre_conv1, conv_step=self.step_conv1)
data_pooled1 = self.pooling(data_conved1, self.size_pooling1)
return data_conved1,data_pooled1
@ -303,4 +302,4 @@ if __name__ == '__main__':
pass
'''
I will put the example on other file
'''
'''