Fix spelling in neural_network/convolution_neural_network.py

neural_network/convolution_neural_network.py

@@ -14,15 +14,16 @@
  Github: [email protected]
  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 @@ def __init__(self,conv1_get,size_p1,bp_num1,bp_num2,bp_num3,rate_w=0.2,rate_t=0.
  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 @@ def ReadModel(cls,model_path):
  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 @@ def convolute(self,data,convs,w_convs,thre_convs,conv_step):
  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 @@ def pooling(self,featuremaps,size_pooling,type='average_pool'):
  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 @@ def _expand(self,datas):
  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 @@ def _calculate_gradient_from_pool(self,out_map,pd_pool,num_map,size_map,size_poo
  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 @@ def trian(self,patterns,datas_train, datas_teach, n_repeat, error_accuracy,draw_
  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 @@ def trian(self,patterns,datas_train, datas_teach, n_repeat, error_accuracy,draw_
  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]):
@@ -268,15 +267,15 @@ def draw_error():
  draw_error()
  return mse
 
- def predict(self,datas_test):
+ 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)
+ 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 @@ def predict(self,datas_test):
  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 @@ def convolution(self,data):
  pass
  '''
  I will put the example on other file
-'''
+'''