Fix typo

Author: shiro615

ch08/deep_convnet.py

@@ -27,17 +27,17 @@ def __init__(self, input_dim=(1, 28, 28),
         # 重みの初期化===========
         # 各層のニューロンひとつあたりが、前層のニューロンといくつのつながりがあるか（TODO:自動で計算する）
         pre_node_nums = np.array([1*3*3, 16*3*3, 16*3*3, 32*3*3, 32*3*3, 64*3*3, 64*4*4, hidden_size])
-        wight_init_scales = np.sqrt(2.0 / pre_node_nums)  # ReLUを使う場合に推奨される初期値
+        weight_init_scales = np.sqrt(2.0 / pre_node_nums)  # ReLUを使う場合に推奨される初期値
 
         self.params = {}
         pre_channel_num = input_dim[0]
         for idx, conv_param in enumerate([conv_param_1, conv_param_2, conv_param_3, conv_param_4, conv_param_5, conv_param_6]):
-            self.params['W' + str(idx+1)] = wight_init_scales[idx] * np.random.randn(conv_param['filter_num'], pre_channel_num, conv_param['filter_size'], conv_param['filter_size'])
+            self.params['W' + str(idx+1)] = weight_init_scales[idx] * np.random.randn(conv_param['filter_num'], pre_channel_num, conv_param['filter_size'], conv_param['filter_size'])
             self.params['b' + str(idx+1)] = np.zeros(conv_param['filter_num'])
             pre_channel_num = conv_param['filter_num']
-        self.params['W7'] = wight_init_scales[6] * np.random.randn(64*4*4, hidden_size)
+        self.params['W7'] = weight_init_scales[6] * np.random.randn(64*4*4, hidden_size)
         self.params['b7'] = np.zeros(hidden_size)
-        self.params['W8'] = wight_init_scales[7] * np.random.randn(hidden_size, output_size)
+        self.params['W8'] = weight_init_scales[7] * np.random.randn(hidden_size, output_size)
         self.params['b8'] = np.zeros(output_size)
 
         # レイヤの生成===========

common/trainer.py

@@ -21,7 +21,7 @@ def __init__(self, network, x_train, t_train, x_test, t_test,
         self.batch_size = mini_batch_size
         self.evaluate_sample_num_per_epoch = evaluate_sample_num_per_epoch
 
-        # optimzer
+        # optimizer
         optimizer_class_dict = {'sgd':SGD, 'momentum':Momentum, 'nesterov':Nesterov,
                                 'adagrad':AdaGrad, 'rmsprpo':RMSprop, 'adam':Adam}
         self.optimizer = optimizer_class_dict[optimizer.lower()](**optimizer_param)