Optimize softmax regression code.

homework_04_logistic_regression/homework/code/softmax_regression.py

@@ -1,7 +1,7 @@
 '''
 Author: SJ2050
 Date: 2021-11-21 17:06:31
-LastEditTime: 2021-11-21 22:29:52
+LastEditTime: 2021-11-22 10:18:42
 Version: v0.0.1
 Description: Softmax regerssion.
 Copyright © 2021 SJ2050
@@ -28,47 +28,42 @@ class SoftmaxRegression():
     self.weights = np.random.random((self.inp_dim, self.out_dim))
     self.b = np.random.random((self.out_dim, 1))
 
-    y = lambda k, cls: 1 if k == cls else 0
-    weights_grad = [[] for i in range(self.out_dim)]
     for j in range(num_iterations):
-      # print(f'iteration: {j}')
+      print(f'iteration: {j}')
       data_index = list(range(train_data_num))
       for i in range(train_data_num):
         rand_index = int(np.random.uniform(0, len(data_index)))
-        # x_vec = np.vstack(self.train_data[rand_index])
-        x_vec = self.train_data[rand_index].reshape(-1, 1)
-        softmax_values = softmax(np.dot(self.weights.T, x_vec)+self.b)[:, 0]
+        x_arr = self.train_data[rand_index]
+        x_vec = x_arr.reshape(-1, 1)
+        x = np.array([x_arr for _ in range(self.out_dim)]).T
+        softmax_values = softmax(np.dot(self.weights.T, x_vec)+self.b).reshape(-1)
         label =self.train_label[rand_index]
         cls = np.argwhere(self.classes == label)[0][0]
-        error = lambda k: y(k, cls)-softmax_values[k]
+        y_arr = np.zeros(self.out_dim)
+        y_arr[cls] = 1
+        err_arr = y_arr-softmax_values
+        err_vec = err_arr.reshape(-1, 1)
+        err = np.diag(err_arr)
 
-        for k in range(self.out_dim):
-          err = error(k)
-          # self.weights += np.pad(alpha*err*x_vec, ((0, 0), (k, self.out_dim-1-k)), \
-          #                                             'constant', constant_values=0)
-          weights_grad[k] = (alpha*err*x_vec)[:, 0]
-          # print(self.weights)
-          self.b[k, 0] += alpha*err
-        self.weights += np.array(weights_grad).T
-
-        del(data_index[rand_index])
+        self.weights += alpha*np.dot(x, err)
+        self.b += alpha*err_vec
 
     self.is_trained = True
 
-
   def predict(self, predict_data):
     if self.is_trained:
       predict_num = len(predict_data)
       result = np.empty(predict_num)
-      for i in range(predict_num):
-        # x_vec = np.vstack(predict_data[i])
-        x_vec = predict_data[i].reshape(-1, 1)
-        result[i] = self.classes[np.argmax(softmax(np.dot(self.weights.T, x_vec)+self.b))]
 
-      return result
+      result = [self.classes[np.argmax(softmax(np.dot(self.weights.T, \
+                            predict_data[i].reshape(-1, 1))+self.b))] \
+                                            for i in range(predict_num)]
+
+      return np.array(result)
     else:
       print('Need training before predicting!!')
 
+
 if __name__ == '__main__':
   # test binary classsfication
   import matplotlib.pyplot as plt