Initialization
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Shreya_Gert
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0f281d487d
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# try your best to do it!
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# @Time : 2022/9/24 21:55
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# @Author : LianghengZhang
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# @File : MINIST_Classsification.py
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import tensorflow as tf
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from tensorflow.keras import datasets, layers, models
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import matplotlib.pyplot as plt
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import matplotlib
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matplotlib.use('TkAgg') # 使用 TkAgg 后端
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import matplotlib.pyplot as plt
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if __name__ == '__main__':
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(train_images, train_labels), (test_images, test_labels) = datasets.mnist.load_data()
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train_images, test_images = train_images / 255.0, test_images / 255.0
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# 查看数据维数信息
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print("data_shpe:",train_images.shape,test_images.shape,train_labels.shape,test_labels.shape)
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"""
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输出：((60000, 28, 28), (10000, 28, 28), (60000,), (10000,))
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"""
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plt.figure(figsize=(20, 10))
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for i in range(20):
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plt.subplot(2, 10, i + 1)
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plt.xticks([])
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plt.yticks([])
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plt.grid(False)
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plt.imshow(train_images[i], cmap=plt.cm.binary)
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plt.xlabel(train_labels[i])
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plt.show()
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# 调整数据到我们需要的格式
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train_images = train_images.reshape((60000, 28, 28, 1))
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test_images = test_images.reshape((10000, 28, 28, 1))
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print("data_shpe:",train_images.shape, test_images.shape, train_labels.shape, test_labels.shape)
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"""
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输出：((60000, 28, 28, 1), (10000, 28, 28, 1), (60000,), (10000,))
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"""
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model = models.Sequential([
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layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),
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layers.MaxPooling2D((2, 2)),
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layers.Conv2D(64, (3, 3), activation='relu'),
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layers.MaxPooling2D((2, 2)),
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layers.Flatten(),
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layers.Dense(64, activation='relu'),
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layers.Dense(10)
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])
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model.summary()
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model.compile(
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optimizer='adam',
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loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
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metrics=['accuracy'])
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history = model.fit(
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train_images,
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train_labels,
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epochs=10,
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)
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#===============================================
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# 训练简单的神经网络,并显示运行时间
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# 数据集：mnnist
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#===============================================
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import datetime
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starttime = datetime.datetime.now()
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import tensorflow as tf
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import numpy as np
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# Import data
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from tensorflow.examples.tutorials.mnist import input_data
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flags = tf.app.flags
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FLAGS = flags.FLAGS
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flags.DEFINE_string('data_dir', '/learn/tensorflow/python/data/', 'Directory for storing data') # 把数据放在/data文件夹中
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mnist_data = input_data.read_data_sets(FLAGS.data_dir, one_hot=True) # 读取数据集
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# 建立抽象模型
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x = tf.placeholder(tf.float32, [None, 784]) # 占位符
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y = tf.placeholder(tf.float32, [None, 10])
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W = tf.Variable(tf.zeros([784, 10]))
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b = tf.Variable(tf.zeros([10]))
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a = tf.nn.softmax(tf.matmul(x, W) + b)
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# 定义损失函数和训练方法
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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
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#cross_entropy = -tf.reduce_sum(y * tf.log(a)) # 损失函数为交叉熵
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optimizer = tf.train.GradientDescentOptimizer(0.3) # 梯度下降法，学习速率要设为0.001量级
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train_next = optimizer.minimize(cross_entropy) # 训练目标：最小化损失函数
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# Train
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sess = tf.InteractiveSession() # 建立交互式会话
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# tf.global_variables_initializer().run()
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sess.run(tf.global_variables_initializer())
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for i in range(1000):
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batch_xs, batch_ys = mnist_data.train.next_batch(100) # 随机抓取100个数据
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# train_next.run({x: batch_xs, y: batch_ys})
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sess.run(train_next, feed_dict={x: batch_xs, y: batch_ys})
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#测试
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correct_prediction = tf.equal(tf.argmax(a, 1), tf.argmax(y, 1))
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# 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。
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accuracy = tf.reduce_mean(tf.cast(correct_prediction, 'float'))
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print(sess.run(accuracy,feed_dict={x:mnist_data.test.images,y:mnist_data.test.labels}))
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endtime=datetime.datetime.now()
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print('total time endtime-starttime:', endtime-starttime)
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#====================================================================
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# 保存和输入matlat格式数据，并画图显示
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#
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#====================================================================
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import scipy.io as sio
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import numpy as np
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import matplotlib.pyplot as plt
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from mpl_toolkits.mplot3d import Axes3D
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# 创建4个变量，并赋值
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#sio.savemat('data/testpython.mat', {'a': 1, 'b': 5, 'c': 3, 'd': 4})
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# 创建了一个变量x，并赋予一个矩阵
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#sio.savemat('data/testpython2.mat', {'x': [[1, 3, 5, 4], [5, 3, 2, 8]]})
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data = sio.loadmat('data/testpython.mat')
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data2 = sio.loadmat('data/testpython2.mat')
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x=np.zeros([1,4])
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x[0][0] = data['a']
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x[0][1] = data['b']
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x[0][2] = data['c']
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x[0][3] = data['d']
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y=data2['x']
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print(x,x.dtype,y[0],y[0].dtype)
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fig = plt.figure()
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ax = fig.add_subplot(111, projection='3d')
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xs = x
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ys = y[0]
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zs = y[1]
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ax.scatter(xs, ys, zs, c='b', marker='o')
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ax.set_xlabel('x')
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ax.set_ylabel('y')
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ax.set_zlabel('z')
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plt.show()
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plt.close()
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#====================================================================
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# 使用 Python API , 拟合一个平面，训练、测试，并画图显示
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# 麻雀虽小五脏俱全!
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#====================================================================
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import tensorflow as tf
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import numpy as np
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import matplotlib.pyplot as plt
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from mpl_toolkits.mplot3d import Axes3D
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# 训练数据
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x_train = np.float32(np.random.rand(2, 100)) # 随机输入
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y_train = np.dot([5.000, 7.000], x_train) + 2.000
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# 构造一个线性模型
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b = tf.Variable(tf.zeros([1]))
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W = tf.Variable(tf.zeros([1,2]))
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y = tf.matmul(W, x_train) + b
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# 最小化方差
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loss = tf.reduce_mean(tf.square(y - y_train))
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optimizer = tf.train.GradientDescentOptimizer(0.5)
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train = optimizer.minimize(loss)
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# 启动图 (graph)
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sess = tf.Session()
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sess.run(tf.global_variables_initializer())
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# 训练：拟合平面
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m = 101
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n = 0
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W_temp = np.zeros([m // 20 + 1, 2])
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b_temp = np.zeros([1, m // 20 + 1])
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for step in range(0, m):
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sess.run(train)
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if step % 20 == 0:
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temp = sess.run(W)
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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！！
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b_temp[0][n] = sess.run(b)
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print(step, sess.run(W), sess.run(b))
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n = n + 1
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W_temp = np.transpose(W_temp)
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# 参数画图
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fig = plt.figure()
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step=np.arange(0,m,20)
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plt.plot(step,W_temp[0], marker='o', mec='b', mfc='b',label=u'W1')
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plt.plot(step,W_temp[1], marker='*', ms=10,label=u'W2')
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plt.plot(step,b_temp[0], marker='^', ms=10,label=u'b')
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plt.legend()
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plt.xlabel('step')
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plt.ylabel('value')
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plt.title('Parameter')
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# 测试数据.
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x_test = np.float32(np.random.rand(2, 100))
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y_test = np.dot([5.000, 7.000], x_test) + 2.000
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# 测试：Test trained model
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y_=sess.run(tf.matmul(W, x_test) + b)
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y = abs(y_-y_test)
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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。
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accuracy=np.sum(y==1)/len(y[0,:])
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print('accuracy:',accuracy)
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sess.close()
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# 测试结果数据画图对比
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fig = plt.figure()
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ax = fig.add_subplot(111, projection='3d')
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xs = x_test[0]
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ys = x_test[1]
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zs1 = y_test
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zs2 = y_
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ax.scatter(xs, ys, zs1, c='r', marker='v')
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ax.scatter(xs, ys, zs2, c='b', marker='^')
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ax.set_xlabel('x')
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ax.set_ylabel('y')
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ax.set_zlabel('z')
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# 显示以上画的两个图
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plt.show()
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#=======================================================================
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'''
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# 实现线性回归模型: 拟合线性函数
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# 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。
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'''
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#=======================================================================
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#import tensorflow as tf
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import tensorflow.compat.v1 as tf
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import matplotlib.pyplot as plt
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import numpy as np
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tf.compat.v1.disable_eager_execution()
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# 初始数据准备
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x_data = np.float32(np.random.rand(100)) # np.random.rand 默认数据类型为float64
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y_data = x_data * 2 + 1.6
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# 权重、偏置这些不断更新的值用tf变量存储
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weights = tf.Variable(tf.zeros([1])) # tf.zeros 默认数据类型为float32
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biases = tf.Variable(tf.zeros([1]))
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# 定义线性模型
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y = weights * x_data + biases
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# 损失函数。tf.reduce_mean()是取均值。square是平方。
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loss = tf.reduce_mean(tf.square(y - y_data))
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# 用梯度优化方法最小化损失函数;
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# 训练用到的优化算法里面的数值参数：学习速率,优化速率
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# loss为优化指标
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#optimizer = tf.train.GradientDescentOptimizer(0.5)
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optimizer = tf.compat.v1.train.GradientDescentOptimizer(0.5)
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train = optimizer.minimize(loss)
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with tf.Session() as sess:
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sess.run(tf.global_variables_initializer())
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for step in range(201):
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sess.run(train)
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if step % 20 == 0:
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print (step, sess.run(weights), sess.run(biases))
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#===============================================================
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# 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
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#===============================================================
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import numpy as np
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import matplotlib.pyplot as plt
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#===============================================================
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# 柱状图
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plt.subplot(221)
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size = 5
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x = np.arange(size)
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a = np.random.random(size)
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b = np.random.random(size)
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width=0.3
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plt.bar(x+width/2, a, label='a',width=width)
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plt.bar(x+width*3/2, b, label='b',width=width)
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plt.legend()
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plt.xlabel('x-axis')
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plt.ylabel('y-axis')
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plt.title('plot')
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#plt.show()
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#===============================================================
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# 散点图
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import matplotlib.pyplot as plt
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plt.subplot(222)
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#plt.figure(figsize=(6,5))
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x=np.random.random(10)
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y=np.random.random(10)
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plt.scatter(x,y,s=15,c='b',marker=(6,1),alpha=0.7,lw=2)
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plt.xlim(0,1)
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plt.ylim(0,1)
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#plt.show()
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#===============================================================
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# 拆线图
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import matplotlib.pyplot as plt
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plt.subplot(223)
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size = 5
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x = np.arange(size)
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a = np.random.random(size)
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b = np.random.random(size)
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plt.plot(x,a, marker='o', mec='b', mfc='b',label=u'y=a')
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plt.plot(x,b, marker='*', ms=10,label=u'y=b')
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plt.legend()
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plt.xlabel('x-axis')
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plt.ylabel('y-axis')
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plt.title('plot')
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plt.show()
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#===============================================================
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# 3D图
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#===============================================================
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# plt.subplot(224)
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from mpl_toolkits.mplot3d import Axes3D
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import matplotlib.pyplot as plt
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# 定义函数
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def rand_range(n, vmin, vmax):
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'''
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make an array of random numbers having shape (n, )
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with each number distributed Uniform(vmin, vmax).
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'''
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return (vmax - vmin) * np.random.rand(n) + vmin
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fig = plt.figure()
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ax = fig.add_subplot(111, projection='3d')
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# plot n random points in the box
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# defined by x in [23, 32], y in [0, 100], z in [zlow, zhigh].
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n = 5
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for c, m, zlow, zhigh in [('r', 'o', -50, -25), ('b', '^', -30, -5)]:
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xs = rand_range(n, 20, 30)
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ys = rand_range(n, 30, 100)
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zs = rand_range(n, zlow, zhigh)
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ax.scatter(xs, ys, zs, c=c, marker=m)
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# 单独一个散点
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ax.scatter(25, 50, -25, c='g', marker="*")
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ax.set_xlabel('x')
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ax.set_ylabel('y')
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ax.set_zlabel('z')
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plt.show()
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#===============================================================
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# 概率分布图,累计概率分布图
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#===============================================================
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import matplotlib.pyplot as plt
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import numpy as np
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#概率分布直方图
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#高斯分布
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mean = 0 #均值为0
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sigma = 1 #标准差为1，反应数据集中还是分散的值
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x=mean+sigma*np.random.randn(10000)
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#第二个参数是柱子宽一些还是窄一些，越大越窄越密
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fig,(ax0,ax1) = plt.subplots(nrows=2,figsize=(6,6))
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##pdf概率分布图，一万个数落在某个区间内的数有多少个
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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
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ax0.set_title('pdf')
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#cdf累计概率函数，cumulative累计。比如需要统计小于5的数的概率
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# bins参数表示将数据分成几组
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# 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
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ax1.hist(x,bins=20,density=False,histtype='step',facecolor='blue',alpha=0.8,cumulative=True,rwidth=0.8)
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ax1.set_title("cdf")
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fig.subplots_adjust(hspace=0.4)
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plt.show()
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#plt.draw()
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# 可用 help(function) 查看函数帮助，如：help(ax.plot_surface)
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#===============================================================
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