diff --git a/README.md b/README.md
index 0857c95..4f888fe 100644
--- a/README.md
+++ b/README.md
@@ -1,2 +1,176 @@
 # pattern_recognition
+from __future__ import division, print_function, absolute_import
+# Import MNIST data，MNIST数据集导入
+from tensorflow.examples.tutorials.mnist import input_data
+mnist = input_data.read_data_sets("MNIST_data", one_hot=False)
 
+import tensorflow as tf
+import matplotlib.pyplot as plt
+import numpy as np  
+
+# Training Parameters，超参数
+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']
+# Import MNIST data，MNIST数据集导入
+from tensorflow.examples.tutorials.mnist import input_data
+mnist = input_data.read_data_sets("MNIST_data", one_hot=False)
+
+import tensorflow as tf
+import matplotlib.pyplot as plt
+import numpy as np  
+
+# Training Parameters，超参数
+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
+        conv1 = tf.layers.conv2d(x, 32, 5, activation=tf.nn.relu)
+        # 最大池化层，步长为2，无需学习任何参量
+        conv1 = tf.layers.max_pooling2d(conv1, 2, 2)
+
+        # 卷积层，64个卷积核，尺寸为3x3
+        conv2 = tf.layers.conv2d(conv1, 64, 3, activation=tf.nn.relu)
+        # 最大池化层，步长为2，无需学习任何参量
+        conv2 = tf.layers.max_pooling2d(conv2, 2, 2)
+
+        # 展开特征为一维向量，以输入全连接层
+        fc1 = tf.contrib.layers.flatten(conv2)
+
+        # 全连接层
+        fc1 = tf.layers.dense(fc1, 1024)
+        # 应用Dropout (训练时打开，测试时关闭)
+        fc1 = tf.layers.dropout(fc1, rate=dropout, training=is_training)
+
+        # 输出层，预测类别
+        out = tf.layers.dense(fc1, n_classes)
+
+    return out
+
+# 确定模型功能 (参照TF Estimator模版)
+def model_fn(features, labels, mode):
+    
+    # 构建神经网络
+    # 因为dropout在训练与测试时的特性不一，我们此处为训练和测试过程创建两个独立但共享权值的计算图
+    logits_train = conv_net(features, num_classes, dropout, reuse=False, is_training=True)
+    logits_test = conv_net(features, num_classes, dropout, reuse=True, is_training=False)
+    
+    # 预测
+    pred_classes = tf.argmax(logits_test, axis=1)
+    pred_probas = tf.nn.softmax(logits_test)
+    
+    if mode == tf.estimator.ModeKeys.PREDICT:
+        return tf.estimator.EstimatorSpec(mode, predictions=pred_classes) 
+        
+    # 确定误差函数与优化器
+    loss_op = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(
+        logits=logits_train, labels=tf.cast(labels, dtype=tf.int32)))
+    optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
+    train_op = optimizer.minimize(loss_op, global_step=tf.train.get_global_step())
+    
+    # 评估模型精确度
+    acc_op = tf.metrics.accuracy(labels=labels, predictions=pred_classes)
+    
+    # TF Estimators需要返回EstimatorSpec
+    estim_specs = tf.estimator.EstimatorSpec(
+      mode=mode,
+      predictions=pred_classes,
+      loss=loss_op,
+      train_op=train_op,
+      eval_metric_ops={'accuracy': acc_op})
+
+    return estim_specs
+
+# 构建Estimator
+model = tf.estimator.Estimator(model_fn)
+
+# 确定训练输入函数
+input_fn = tf.estimator.inputs.numpy_input_fn(
+    x={'images': mnist.train.images}, y=mnist.train.labels,
+    batch_size=batch_size, num_epochs=None, shuffle=True)
+# 开始训练模型
+model.train(input_fn, steps=num_steps)   
+
+# 评判模型
+# 确定评判用输入函数
+input_fn = tf.estimator.inputs.numpy_input_fn(
+    x={'images': mnist.test.images}, y=mnist.test.labels,
+    batch_size=batch_size, shuffle=False)
+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])