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Fix spelling in neural_network/convolution_neural_network.py #849

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Merged
poyea merged 2 commits into from
May 30, 2019
Merged

Fix spelling in neural_network/convolution_neural_network.py #849

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bf2ed75
Fix spelling in neural_network/convolution_neural_network.py
cedricfarinazzo May 28, 2019
75bae19
fix import
cedricfarinazzo May 29, 2019
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Fix spelling in neural_network/convolution_neural_network.py
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@cedricfarinazzo
cedricfarinazzo authored May 28, 2019
commit bf2ed759cfb1ff1de79b85a9d39a1aac5b5e5999
28 changes: 14 additions & 14 deletions neural_network/convolution_neural_network.py
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Original file line number Diff line number Diff line change
Expand Up @@ -22,7 +22,7 @@

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
Expand All @@ -48,7 +48,7 @@ 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,
Expand All @@ -71,7 +71,7 @@ def save_model(self,save_path):
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:
Expand All @@ -97,13 +97,13 @@ 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]
Expand Down Expand Up @@ -132,7 +132,7 @@ 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)
Expand All @@ -153,7 +153,7 @@ 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)):
Expand All @@ -164,14 +164,14 @@ 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
Expand All @@ -190,7 +190,7 @@ 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)))
Expand Down Expand Up @@ -268,7 +268,7 @@ def draw_error():
draw_error()
return mse

def predict(self,datas_test):
def predict(self, datas_test):
#model predict
produce_out = []
print('-------------------Start Testing-------------------------')
Expand All @@ -289,7 +289,7 @@ 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,
Expand All @@ -303,4 +303,4 @@ def convolution(self,data):
pass
'''
I will put the example on other file
'''
'''