perf(draw): 保存绘制图片

Author: zjZSTU

examples/2_nn_mnist.py

@@ -35,5 +35,5 @@
     plt = Draw()
     plt(solver.loss_history)
     plt.multi_plot((solver.train_acc_history, solver.val_acc_history), ('train', 'val'),
-                   title='准确率', xlabel='迭代/次', ylabel='准确率')
+                   title='准确率', xlabel='迭代/次', ylabel='准确率', save_path='acc.png')
     print('best_train_acc: %f; best_val_acc: %f' % (solver.best_train_acc, solver.best_val_acc))

examples/3_nn_cifar10.py

@@ -27,5 +27,5 @@
     plt = Draw()
     plt(solver.loss_history)
     plt.multi_plot((solver.train_acc_history, solver.val_acc_history), ('train', 'val'),
-                   title='准确率', xlabel='迭代/次', ylabel='准确率')
+                   title='准确率', xlabel='迭代/次', ylabel='准确率', save_path='acc.png')
     print('best_train_acc: %f; best_val_acc: %f' % (solver.best_train_acc, solver.best_val_acc))

examples/3_nn_iris.py

@@ -39,5 +39,5 @@
     plt = Draw()
     plt(solver.loss_history)
     plt.multi_plot((solver.train_acc_history, solver.val_acc_history), ('train', 'val'),
-                   title='准确率', xlabel='迭代/次', ylabel='准确率')
+                   title='准确率', xlabel='迭代/次', ylabel='准确率', save_path='acc.png')
     print('best_train_acc: %f; best_val_acc: %f' % (solver.best_train_acc, solver.best_val_acc))

examples/3_nn_mnist.py

@@ -35,5 +35,5 @@
     plt = Draw()
     plt(solver.loss_history)
     plt.multi_plot((solver.train_acc_history, solver.val_acc_history), ('train', 'val'),
-                   title='准确率', xlabel='迭代/次', ylabel='准确率')
+                   title='准确率', xlabel='迭代/次', ylabel='准确率', save_path='acc.png')
     print('best_train_acc: %f; best_val_acc: %f' % (solver.best_train_acc, solver.best_val_acc))

examples/3_nn_orl.py

@@ -37,5 +37,5 @@
     plt = Draw()
     plt(solver.loss_history)
     plt.multi_plot((solver.train_acc_history, solver.val_acc_history), ('train', 'val'),
-                   title='准确率', xlabel='迭代/次', ylabel='准确率')
+                   title='准确率', xlabel='迭代/次', ylabel='准确率', save_path='acc.png')
     print('best_train_acc: %f; best_val_acc: %f' % (solver.best_train_acc, solver.best_val_acc))

examples/lenet5_mnist.py

@@ -37,5 +37,5 @@
     plt = Draw()
     plt(solver.loss_history)
     plt.multi_plot((solver.train_acc_history, solver.val_acc_history), ('train', 'val'),
-                   title='准确率', xlabel='迭代/次', ylabel='准确率')
+                   title='准确率', xlabel='迭代/次', ylabel='准确率', save_path='acc.png')
     print('best_train_acc: %f; best_val_acc: %f' % (solver.best_train_acc, solver.best_val_acc))

examples/nin_cifar10.py

@@ -78,7 +78,8 @@ def nin_train():
 
     draw = vision.Draw()
     draw(loss_list, xlabel='迭代/20次')
-    draw.multi_plot((train_list, test_list), ('训练集', '测试集'), title='精度图', xlabel='迭代/20次', ylabel='精度值')
+    draw.multi_plot((train_list, test_list), ('训练集', '测试集'),
+                    title='精度图', xlabel='迭代/20次', ylabel='精度值', save_path='acc.png')
 
 
 if __name__ == '__main__':

pynet/vision/draw.py

@@ -12,15 +12,16 @@ class Draw(object):
     def __call__(self, values, title='损失图', xlabel='迭代/次', ylabel='损失值'):
         self.forward(values, title='损失图', xlabel='迭代/次', ylabel='损失值')
 
-    def forward(self, values, title='损失图', xlabel='迭代/次', ylabel='损失值'):
+    def forward(self, values, title='损失图', xlabel='迭代/次', ylabel='损失值', save_path='./loss.png'):
         assert isinstance(values, list)
         plt.title(title)
         plt.ylabel(ylabel)
         plt.xlabel(xlabel)
         plt.plot(values)
+        plt.savefig(save_path)
         plt.show()
 
-    def multi_plot(self, values_list, labels_list, title='损失图', xlabel='迭代/次', ylabel='损失值'):
+    def multi_plot(self, values_list, labels_list, title='损失图', xlabel='迭代/次', ylabel='损失值', save_path='./loss.png'):
         assert isinstance(values_list, tuple)
         assert isinstance(labels_list, tuple)
         assert len(values_list) == len(labels_list)
@@ -31,4 +32,5 @@ def multi_plot(self, values_list, labels_list, title='损失图', xlabel='迭代
         for i in range(len(values_list)):
             plt.plot(values_list[i], label=labels_list[i])
         plt.legend()
+        plt.savefig(save_path)
         plt.show()