diff --git a/README.md b/README.md
index c4dcd66..45f0655 100644
--- a/README.md
+++ b/README.md
@@ -108,3 +108,45 @@ model.evaluate(input_fn)
 
 # 预测单个图像
 n_images = 6
+# 从数据集得到测试图像
+test_images = mnist.test.images[:n_images]
+# 准备输入数据
+input_fn = tf.estimator.inputs.numpy_input_fn(
+    x={'images': test_images}, shuffle=False)
+# 用训练好的模型预测图片类别
+preds = list(model.predict(input_fn))
+
+# 可视化显示
+for i in range(n_images):
+    plt.imshow(np.reshape(test_images[i], [28, 28]), cmap='gray')
+    plt.show()
+    print("Model prediction:", preds[i])
+
+
+ # 从数据集得到测试图像
+test_images = mnist.test.images[:n_images]
+# 准备输入数据
+input_fn = tf.estimator.inputs.numpy_input_fn(
+    x={'images': test_images}, shuffle=False)
+# 用训练好的模型预测图片类别
+preds = list(model.predict(input_fn))
+
+# 可视化显示
+for i in range(n_images):
+    plt.imshow(np.reshape(test_images[i], [28, 28]), cmap='gray')
+    plt.show()
+    print("Model prediction:", preds[i])
+
+# 从数据集得到测试图像
+test_images = mnist.test.images[:n_images]
+# 准备输入数据
+input_fn = tf.estimator.inputs.numpy_input_fn(
+    x={'images': test_images}, shuffle=False)
+# 用训练好的模型预测图片类别
+preds = list(model.predict(input_fn))
+
+# 可视化显示
+for i in range(n_images):
+    plt.imshow(np.reshape(test_images[i], [28, 28]), cmap='gray')
+    plt.show()
+    print("Model prediction:", preds[i])