From c39e527d1ad2e1d967cfee9ceeb155d4c8dada4a Mon Sep 17 00:00:00 2001
From: p9n6cg2k8 <zhoumubaiyu@qq.com>
Date: Thu, 5 May 2022 18:15:19 +0800
Subject: [PATCH] Update README.md

---
 README.md | 16 ++++++++++++++++
 1 file changed, 16 insertions(+)

diff --git a/README.md b/README.md
index de770d9..4f888fe 100644
--- a/README.md
+++ b/README.md
@@ -37,7 +37,23 @@ import numpy as np
 learning_rate = 0.001 #学习率
 num_steps = 2000 # 训练步数
 batch_size = 128 # 训练数据批的大小
+# Network Parameters，网络参数
+num_input = 784 # MNIST数据输入 (img shape: 28*28)
+num_classes = 10 # MNIST所有类别 (0-9 digits)
+dropout = 0.75 # Dropout, probability to keep units = (1-p)，保留神经元相应的概率为(1-p)=(1-0.75)=0.25
+
+# Create the neural network，创建深度神经网络
+def conv_net(x_dict, n_classes, dropout, reuse, is_training):
+    
+    # Define a scope for reusing the variables，确定命名空间
+    with tf.variable_scope('ConvNet', reuse=reuse):
+        # TF Estimator类型的输入为像素
+        x = x_dict['images']
 
+        # MNIST数据输入格式为一位向量，包含784个特征 (28*28像素)
+        # 用reshape函数改变形状以匹配图像的尺寸 [高 x 宽 x 通道数]
+        # 输入张量的尺度为四维: [(每一)批数据的数目, 高，宽，通道数]
+        x = tf.reshape(x, shape=[-1, 28, 28, 1])
 
 
         # 卷积层，32个卷积核，尺寸为5x5