更新第二版readme

README.md

@@ -1,5 +1,7 @@
 
 
+
+
 # num_mnist 手写数字识别
 
 
@@ -94,7 +96,7 @@ $$
 
 #### 实现过程
 
-
+构建网络模型
 
 ```python
 # 构建网络 开始
@@ -116,6 +118,73 @@ model.compile(optimizer='adam', loss=tf.losses.sparse_categorical_crossentropy,
 
 ```
 
+训练并评估
+
+```python
+# 训练
+history = model.fit(
+    train_images_scaled.reshape(-1, 28, 28, 1),
+    train_labels,
+    epochs=8,
+    validation_data=(test_images.reshape(-1, 28, 28, 1), test_labels),
+    callbacks=[cp_callback]
+)
+
+# 评估
+results = model.evaluate(test_images.reshape(-1, 28, 28, 1), test_labels)
+```
+
+将训练后的模型保存
+
+```python
+# 保存模型
+model.save(model_path)
+```
+
+加载模型
+
+```python
+# 读取训练好的模型
+model = load_model(model_path)
+```
+
+可视化预测效果
+
+```
+# 可视化预测效果
+show_num = 200
+testShow = test_labels[:show_num]
+
+pred = model.predict(test_images.reshape(-1, 28, 28, 1))
+predict = []
+for item in pred:
+    predict.append(np.argmax(item))
+
+# 显示折线图
+plt.figure()
+plt.title('Conv Predict')
+plt.ylabel('true number')
+plt.xlabel('img num')
+plt.plot(range(testShow.size), predict[:show_num], marker='^', color='coral', label='predict')
+plt.plot(range(testShow.size), testShow,  marker='o', color='deepskyblue', label='result')
+plt.legend()
+
+# 挑选出预测错误的图片，并显示预测值
+wrongImg = []
+wrongNum = []
+for i in range(testShow.size):
+    if (predict[i] != testShow[i]):
+        wrongImg.append(test_images[i])
+        wrongNum.append(predict[i])
+
+for i in range(len(wrongImg)):
+    plt.figure()
+    plt.title(str(wrongNum[i]))
+    plt.imshow(wrongImg[i])
+
+plt.show()
+```
+
 
 
 ### 系统测试

predict.py

@@ -15,7 +15,7 @@ sess = tf.compat.v1.Session(config=config)
 num_mnist = tf.keras.datasets.mnist
 (train_images, train_labels), (test_images, test_labels) = num_mnist.load_data()
 
-# 读取训练好点模型
+# 读取训练好的模型
 model = load_model(model_path)
 
 # 打印网络结构