加入说明

Author: lawlite19

CNNModel_EarlyStopping_Save_Restore/CNNModel_EarlyStopping_Save_Restore.py

@@ -170,7 +170,7 @@ def cls_accuracy(correct):
 def validation_accuracy():
     correct, _ = predict_cls_validation()
     return cls_accuracy(correct)
-'''define a function to'''
+'''define a function to calculate test acc'''
 def print_test_accuracy(show_example_errors=False,
                         show_confusion_matrix=False):
     correct, cls_pred = predict_cls_test()
@@ -204,14 +204,14 @@ def plot_conv_weights(weights, input_channel=0):
         ax.set_xticks([])
         ax.set_yticks([])
     plt.show()
-#print_test_accuracy()
-#plot_conv_weights(weights=weights_conv1)
-#optimize(10000)
-#print_test_accuracy(show_example_errors=True,
-                    #show_confusion_matrix=True)
-#plot_conv_weights(weights=weights_conv1)
-'''restore the variables that saved on disk'''
-saver.restore(sess=session, save_path=save_path)
-print_test_accuracy(True, True)
+print_test_accuracy()
+plot_conv_weights(weights=weights_conv1)
+optimize(10000)
+print_test_accuracy(show_example_errors=True,
+                    show_confusion_matrix=True)
+plot_conv_weights(weights=weights_conv1)
+#'''restore the variables that saved on disk'''
+#saver.restore(sess=session, save_path=save_path)
+#print_test_accuracy(True, True)
 
 

CNNModel_EarlyStopping_Save_Restore/checkpoints/best_validation.data-00000-of-00001

@@ -1345,6 +1345,30 @@ def optimize(num_iterations):
     time_diff = end_time-start_time
     print("Time usage:" + str(timedelta(seconds=int(round(time_diff)))))
 ```
+- 调用`optimize(10000)`输出信息
+
+``` stylus
+Iter:   5100, Train_batch accuracy:100.0%, validation acc: 98.8% *
+Iter:   5200, Train_batch accuracy:100.0%, validation acc: 98.3% 
+Iter:   5300, Train_batch accuracy:100.0%, validation acc: 98.7% 
+Iter:   5400, Train_batch accuracy: 98.4%, validation acc: 98.6% 
+Iter:   5500, Train_batch accuracy: 98.4%, validation acc: 98.6% 
+Iter:   5600, Train_batch accuracy:100.0%, validation acc: 98.7% 
+Iter:   5700, Train_batch accuracy: 96.9%, validation acc: 98.9% *
+Iter:   5800, Train_batch accuracy:100.0%, validation acc: 98.6% 
+Iter:   5900, Train_batch accuracy:100.0%, validation acc: 98.6% 
+Iter:   6000, Train_batch accuracy: 98.4%, validation acc: 98.7% 
+Iter:   6100, Train_batch accuracy:100.0%, validation acc: 98.7% 
+Iter:   6200, Train_batch accuracy:100.0%, validation acc: 98.7% 
+Iter:   6300, Train_batch accuracy: 98.4%, validation acc: 98.8% 
+Iter:   6400, Train_batch accuracy: 98.4%, validation acc: 98.8% 
+Iter:   6500, Train_batch accuracy:100.0%, validation acc: 98.7% 
+Iter:   6600, Train_batch accuracy:100.0%, validation acc: 98.7% 
+Iter:   6700, Train_batch accuracy:100.0%, validation acc: 98.8% 
+No improvement found in a while, stop running
+Time usage:0:18:43
+```
+可以看到最后10次输出（没100次输出一次）在验证集上准确度都没有提高，直接保存
 
 ### 3、 小批量预测并计算准确率
 - 因为需要预测**测试集和验证集**，这里参数指定需要的images