master

Neural_Network/convolution_neural_network.py

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+#-*- 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: [email protected]
+    Date: 2017.9.20
+    - - - - - -- - - - - - - - - - - - - - - - - - - - - - -
+          '''
+
+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
+    '''