diff --git a/ANN_TF2.py b/ANN_TF2.py
new file mode 100644
index 0000000..8eeb77b
--- /dev/null
+++ b/ANN_TF2.py
@@ -0,0 +1,64 @@
+# try your best to do it!
+# @Time : 2022/9/24 21:55
+# @Author : LianghengZhang
+# @File : MINIST_Classsification.py
+import tensorflow as tf
+from tensorflow.keras import datasets, layers, models
+import matplotlib.pyplot as plt
+
+import matplotlib
+matplotlib.use('TkAgg')  # 使用 TkAgg 后端
+import matplotlib.pyplot as plt
+
+if __name__ == '__main__':
+    (train_images, train_labels), (test_images, test_labels) = datasets.mnist.load_data()
+
+    train_images, test_images = train_images / 255.0, test_images / 255.0
+    # 查看数据维数信息
+    print("data_shpe:",train_images.shape,test_images.shape,train_labels.shape,test_labels.shape)
+    """
+    输出：((60000, 28, 28), (10000, 28, 28), (60000,), (10000,))
+    """
+
+    plt.figure(figsize=(20, 10))
+    for i in range(20):
+        plt.subplot(2, 10, i + 1)
+        plt.xticks([])
+        plt.yticks([])
+        plt.grid(False)
+        plt.imshow(train_images[i], cmap=plt.cm.binary)
+        plt.xlabel(train_labels[i])
+    plt.show()
+
+    # 调整数据到我们需要的格式
+    train_images = train_images.reshape((60000, 28, 28, 1))
+    test_images = test_images.reshape((10000, 28, 28, 1))
+
+    print("data_shpe:",train_images.shape, test_images.shape, train_labels.shape, test_labels.shape)
+    """
+    输出：((60000, 28, 28, 1), (10000, 28, 28, 1), (60000,), (10000,))
+    """
+
+    model = models.Sequential([
+        layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),
+        layers.MaxPooling2D((2, 2)),
+        layers.Conv2D(64, (3, 3), activation='relu'),
+        layers.MaxPooling2D((2, 2)),
+
+        layers.Flatten(),
+        layers.Dense(64, activation='relu'),
+        layers.Dense(10)
+    ])
+    model.summary()
+
+
+    model.compile(
+        optimizer='adam',
+        loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
+        metrics=['accuracy'])
+
+    history = model.fit(
+        train_images,
+        train_labels,
+        epochs=10,
+        )
diff --git a/ANN_mnist.py b/ANN_mnist.py
new file mode 100644
index 0000000..50167f9
--- /dev/null
+++ b/ANN_mnist.py
@@ -0,0 +1,51 @@
+#===============================================
+# 训练简单的神经网络,并显示运行时间
+# 数据集：mnnist
+#===============================================
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import datetime
+starttime = datetime.datetime.now()
+
+import tensorflow as tf
+import numpy as np
+
+# Import data
+from tensorflow.examples.tutorials.mnist import input_data
+flags = tf.app.flags
+FLAGS = flags.FLAGS
+flags.DEFINE_string('data_dir', '/learn/tensorflow/python/data/', 'Directory for storing data') # 把数据放在/data文件夹中
+mnist_data = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)   # 读取数据集
+
+# 建立抽象模型
+x = tf.placeholder(tf.float32, [None, 784]) # 占位符
+y = tf.placeholder(tf.float32, [None, 10])
+W = tf.Variable(tf.zeros([784, 10]))
+b = tf.Variable(tf.zeros([10]))
+a = tf.nn.softmax(tf.matmul(x, W) + b)
+
+# 定义损失函数和训练方法
+cross_entropy = tf.reduce_mean(-tf.reduce_sum(y * tf.log(a), reduction_indices=[1]))  # 损失函数为交叉熵，学习速率要设为0.3量级
+#cross_entropy = -tf.reduce_sum(y * tf.log(a))   # 损失函数为交叉熵
+optimizer = tf.train.GradientDescentOptimizer(0.3) # 梯度下降法，学习速率要设为0.001量级
+train_next = optimizer.minimize(cross_entropy) # 训练目标：最小化损失函数
+
+# Train
+sess = tf.InteractiveSession()      # 建立交互式会话
+# tf.global_variables_initializer().run()
+sess.run(tf.global_variables_initializer())
+for i in range(1000):
+    batch_xs, batch_ys = mnist_data.train.next_batch(100) # 随机抓取100个数据
+#    train_next.run({x: batch_xs, y: batch_ys})
+    sess.run(train_next, feed_dict={x: batch_xs, y: batch_ys})
+
+#测试
+correct_prediction = tf.equal(tf.argmax(a, 1), tf.argmax(y, 1))
+# tf.cast先将数据转换成float，防止求平均不准确:比如 tf.float32就是正确，写成tf.float16导致不准确，超出范围。
+accuracy = tf.reduce_mean(tf.cast(correct_prediction, 'float'))
+print(sess.run(accuracy,feed_dict={x:mnist_data.test.images,y:mnist_data.test.labels}))
+
+endtime=datetime.datetime.now()
+print('total time endtime-starttime:', endtime-starttime)
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diff --git a/IO.py b/IO.py
new file mode 100644
index 0000000..ef11238
--- /dev/null
+++ b/IO.py
@@ -0,0 +1,35 @@
+#====================================================================
+# 保存和输入matlat格式数据，并画图显示
+#
+#====================================================================
+import scipy.io as sio
+import numpy as np
+import matplotlib.pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D
+
+# 创建4个变量，并赋值
+#sio.savemat('data/testpython.mat', {'a': 1, 'b': 5, 'c': 3, 'd': 4})
+# 创建了一个变量x，并赋予一个矩阵
+#sio.savemat('data/testpython2.mat', {'x': [[1, 3, 5, 4], [5, 3, 2, 8]]})
+
+data = sio.loadmat('data/testpython.mat')
+data2 = sio.loadmat('data/testpython2.mat')
+x=np.zeros([1,4])
+x[0][0] = data['a']
+x[0][1] = data['b']
+x[0][2] = data['c']
+x[0][3] = data['d']
+y=data2['x']
+print(x,x.dtype,y[0],y[0].dtype)
+
+fig = plt.figure()
+ax = fig.add_subplot(111, projection='3d')
+xs = x
+ys = y[0]
+zs = y[1]
+ax.scatter(xs, ys, zs, c='b', marker='o')
+ax.set_xlabel('x')
+ax.set_ylabel('y')
+ax.set_zlabel('z')
+plt.show()
+plt.close()
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diff --git a/ne_PM.py b/ne_PM.py
new file mode 100644
index 0000000..626c3a8
--- /dev/null
+++ b/ne_PM.py
@@ -0,0 +1,83 @@
+#====================================================================
+# 使用 Python API , 拟合一个平面，训练、测试，并画图显示
+# 麻雀虽小五脏俱全!
+#====================================================================
+import tensorflow as tf
+import numpy as np
+import matplotlib.pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D
+
+# 训练数据
+x_train = np.float32(np.random.rand(2, 100)) # 随机输入
+y_train = np.dot([5.000, 7.000], x_train) + 2.000
+
+# 构造一个线性模型
+b = tf.Variable(tf.zeros([1]))
+W = tf.Variable(tf.zeros([1,2]))
+y = tf.matmul(W, x_train) + b
+
+# 最小化方差
+loss = tf.reduce_mean(tf.square(y - y_train))
+optimizer = tf.train.GradientDescentOptimizer(0.5)
+train = optimizer.minimize(loss)
+
+# 启动图 (graph)
+sess = tf.Session()
+sess.run(tf.global_variables_initializer())
+
+# 训练：拟合平面
+m = 101
+n = 0
+W_temp = np.zeros([m // 20 + 1, 2])
+b_temp = np.zeros([1, m // 20 + 1])
+for step in range(0, m):
+    sess.run(train)
+    if step % 20 == 0:
+        temp = sess.run(W)
+        W_temp[n] = temp[0]   # 注意：列表和数组属于不同类型数据，否则赋值报错！！
+        b_temp[0][n] = sess.run(b)
+        print(step, sess.run(W), sess.run(b))
+        n = n + 1
+
+W_temp = np.transpose(W_temp)
+
+# 参数画图
+fig = plt.figure()
+step=np.arange(0,m,20)
+plt.plot(step,W_temp[0], marker='o', mec='b', mfc='b',label=u'W1')
+plt.plot(step,W_temp[1], marker='*', ms=10,label=u'W2')
+plt.plot(step,b_temp[0], marker='^', ms=10,label=u'b')
+plt.legend()
+plt.xlabel('step')
+plt.ylabel('value')
+plt.title('Parameter')
+
+
+# 测试数据.
+x_test = np.float32(np.random.rand(2, 100))
+y_test = np.dot([5.000, 7.000], x_test) + 2.000
+
+# 测试：Test trained model
+y_=sess.run(tf.matmul(W, x_test) + b)
+y = abs(y_-y_test)
+y=np.where(y>0.01,y,1)       # 数组y_中，如果元素大于一个给定的数0.001，则值保留，否则元素被1替代。
+accuracy=np.sum(y==1)/len(y[0,:])
+print('accuracy:',accuracy)
+
+sess.close()
+
+# 测试结果数据画图对比
+fig = plt.figure()
+ax = fig.add_subplot(111, projection='3d')
+xs = x_test[0]
+ys = x_test[1]
+zs1 = y_test
+zs2 = y_
+ax.scatter(xs, ys, zs1, c='r', marker='v')
+ax.scatter(xs, ys, zs2, c='b', marker='^')
+ax.set_xlabel('x')
+ax.set_ylabel('y')
+ax.set_zlabel('z')
+
+# 显示以上画的两个图
+plt.show()
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diff --git a/ne_ZL_TF2.py b/ne_ZL_TF2.py
new file mode 100644
index 0000000..6ed21a8
--- /dev/null
+++ b/ne_ZL_TF2.py
@@ -0,0 +1,40 @@
+#=======================================================================
+'''
+# 实现线性回归模型: 拟合线性函数
+# 所谓线性回归模型就是y = W * x + b的形式的表达式拟合的模型。
+'''
+#=======================================================================
+
+#import tensorflow as tf
+import tensorflow.compat.v1 as tf
+import matplotlib.pyplot as plt
+import numpy as np
+tf.compat.v1.disable_eager_execution()
+
+# 初始数据准备
+x_data = np.float32(np.random.rand(100)) # np.random.rand 默认数据类型为float64
+y_data = x_data * 2 + 1.6
+
+# 权重、偏置这些不断更新的值用tf变量存储
+weights = tf.Variable(tf.zeros([1])) # tf.zeros 默认数据类型为float32
+biases = tf.Variable(tf.zeros([1]))
+
+# 定义线性模型
+y = weights * x_data + biases
+
+# 损失函数。tf.reduce_mean()是取均值。square是平方。
+loss = tf.reduce_mean(tf.square(y - y_data))
+
+# 用梯度优化方法最小化损失函数;
+# 训练用到的优化算法里面的数值参数：学习速率,优化速率
+# loss为优化指标
+#optimizer = tf.train.GradientDescentOptimizer(0.5)
+optimizer = tf.compat.v1.train.GradientDescentOptimizer(0.5)
+train = optimizer.minimize(loss)
+
+with tf.Session() as sess:
+    sess.run(tf.global_variables_initializer())
+    for step in range(201):
+        sess.run(train)
+        if step % 20 == 0:
+            print (step, sess.run(weights), sess.run(biases))
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diff --git a/picture.py b/picture.py
new file mode 100644
index 0000000..fcd06a3
--- /dev/null
+++ b/picture.py
@@ -0,0 +1,117 @@
+#===============================================================
+# 画图：柱状图，散点图，拆线图，3D图，概率分布图，累计概率分布图
+#===============================================================
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+#===============================================================
+# 柱状图
+plt.subplot(221)
+size = 5
+x = np.arange(size)
+a = np.random.random(size)
+b = np.random.random(size)
+width=0.3
+plt.bar(x+width/2,   a, label='a',width=width)
+plt.bar(x+width*3/2, b, label='b',width=width)
+plt.legend()
+plt.xlabel('x-axis')
+plt.ylabel('y-axis')
+plt.title('plot')
+#plt.show()
+
+#===============================================================
+# 散点图
+import matplotlib.pyplot as plt
+plt.subplot(222)
+#plt.figure(figsize=(6,5))
+x=np.random.random(10)
+y=np.random.random(10)
+plt.scatter(x,y,s=15,c='b',marker=(6,1),alpha=0.7,lw=2)
+plt.xlim(0,1)
+plt.ylim(0,1)
+#plt.show()
+
+#===============================================================
+# 拆线图
+import matplotlib.pyplot as plt
+plt.subplot(223)
+size = 5
+x = np.arange(size)
+a = np.random.random(size)
+b = np.random.random(size)
+plt.plot(x,a, marker='o', mec='b', mfc='b',label=u'y=a')
+plt.plot(x,b, marker='*', ms=10,label=u'y=b')
+plt.legend()
+plt.xlabel('x-axis')
+plt.ylabel('y-axis')
+plt.title('plot')
+plt.show()
+
+#===============================================================
+# 3D图
+#===============================================================
+# plt.subplot(224)
+from mpl_toolkits.mplot3d import Axes3D
+import matplotlib.pyplot as plt
+
+# 定义函数
+def rand_range(n, vmin, vmax):
+    '''
+    make an array of random numbers having shape (n, )
+    with each number distributed Uniform(vmin, vmax).
+    '''
+    return (vmax - vmin) * np.random.rand(n) + vmin
+
+fig = plt.figure()
+ax = fig.add_subplot(111, projection='3d')
+# plot n random points in the box
+# defined by x in [23, 32], y in [0, 100], z in [zlow, zhigh].
+n = 5
+for c, m, zlow, zhigh in [('r', 'o', -50, -25), ('b', '^', -30, -5)]:
+    xs = rand_range(n, 20, 30)
+    ys = rand_range(n, 30, 100)
+    zs = rand_range(n, zlow, zhigh)
+    ax.scatter(xs, ys, zs, c=c, marker=m)
+
+# 单独一个散点
+ax.scatter(25, 50, -25, c='g', marker="*")
+
+ax.set_xlabel('x')
+ax.set_ylabel('y')
+ax.set_zlabel('z')
+plt.show()
+
+#===============================================================
+# 概率分布图,累计概率分布图
+#===============================================================
+
+import matplotlib.pyplot as plt
+import numpy as np
+#概率分布直方图
+#高斯分布
+
+mean = 0 #均值为0
+sigma = 1 #标准差为1，反应数据集中还是分散的值
+x=mean+sigma*np.random.randn(10000)
+
+#第二个参数是柱子宽一些还是窄一些，越大越窄越密
+fig,(ax0,ax1) = plt.subplots(nrows=2,figsize=(6,6))
+
+##pdf概率分布图，一万个数落在某个区间内的数有多少个
+ax0.hist(x,bins=40,density=True,histtype='bar',facecolor='green',alpha=0.8,rwidth=0.8) # bins参数表示将数据分成几组
+ax0.set_title('pdf')
+
+#cdf累计概率函数，cumulative累计。比如需要统计小于5的数的概率
+# bins参数表示将数据分成几组
+# normed 是否对y轴数据进行标准化:True表是在本区间的点在所有的点中所占的概率，如果 normed 为False， 则是显示点的数量
+ax1.hist(x,bins=20,density=False,histtype='step',facecolor='blue',alpha=0.8,cumulative=True,rwidth=0.8)
+ax1.set_title("cdf")
+fig.subplots_adjust(hspace=0.4)
+plt.show()
+
+#plt.draw()
+
+# 可用 help(function) 查看函数帮助，如：help(ax.plot_surface)
+#===============================================================
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