From 9037abae110408e8c7cf45f613d84059be07993c Mon Sep 17 00:00:00 2001 From: cedricfarinazzo Date: Thu, 30 May 2019 02:47:00 +0200 Subject: [PATCH] Fix spelling in neural_network/convolution_neural_network.py (#849) * Fix spelling in neural_network/convolution_neural_network.py * fix import Signed-off-by: cedric.farinazzo --- neural_network/convolution_neural_network.py | 67 ++++++++++---------- 1 file changed, 33 insertions(+), 34 deletions(-) diff --git a/neural_network/convolution_neural_network.py b/neural_network/convolution_neural_network.py index 0dca2bc48..0e72f0c0d 100644 --- a/neural_network/convolution_neural_network.py +++ b/neural_network/convolution_neural_network.py @@ -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 - ''' \ No newline at end of file +'''