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成都建立网站,cms wordpress 国内,网站专题模板,微网站与微信的关系文章目录 使用TensorFlow完成线性回归1. 导入TensorFlow库2. 构造数据集3. 定义基本模型4. 训练模型5. 线性回归图 附#xff1a;系列文章 使用TensorFlow完成线性回归 TensorFlow是由Google开发的一个开源的机器学习框架。它可以让开发者更加轻松地构建和训练深度学习模型系列文章 使用TensorFlow完成线性回归 TensorFlow是由Google开发的一个开源的机器学习框架。它可以让开发者更加轻松地构建和训练深度学习模型从而解决各种自然语言处理、计算机视觉、语音识别、推荐系统等领域的问题。 TensorFlow的主要特点是灵活性和可伸缩性。它实现了一种基于数据流图的计算模型使得用户可以定义自己的计算图控制模型的计算过程。同时TensorFlow支持分布式计算使得用户可以在多台机器上运行大规模计算任务从而提高计算效率。 TensorFlow包含了许多高级API例如Keras和Estimator使得用户可以更加轻松地构建和训练深度学习模型。Keras提供了一个易于使用的高级API使得用户可以在不需要深入了解TensorFlow的情况下构建和训练深度学习模型。Estimator则提供了一种更加低级的API使得用户可以更加灵活地定义模型的结构和训练过程。 TensorFlow还提供了一个交互式开发环境称为TensorBoard可以帮助用户可视化模型的计算图、训练过程和性能指标从而更加直观地理解和调试深度学习模型。 由于TensorFlow的灵活性和可伸缩性它已经被广泛应用于各个领域包括自然语言处理、计算机视觉、语音识别、推荐系统等。例如在自然语言处理领域TensorFlow被用于构建和训练各种强大的模型例如机器翻译模型、文本分类模型、语言生成模型等。 总的来说TensorFlow是一个强大的机器学习框架可以帮助用户更加轻松地构建和训练深度学习模型。随着深度学习技术的不断发展TensorFlow将继续发挥重要的作用推动各个领域的发展和创新。

1. 导入TensorFlow库

   导入相关库

   %matplotlib inline import numpy as np import tensorflow as tf import matplotlib.pyplot as plt2. 构造数据集

   产出样本点个数

   n_observations 100

   产出-3~3之间的样本点

   xs np.linspace(-3, 3, n_observations)

   sin扰动

   ys np.sin(xs) np.random.uniform(-0.5, 0.5, n_observations) xsarray([-3. , -2.93939394, -2.87878788, -2.81818182, -2.75757576,-2.6969697 , -2.63636364, -2.57575758, -2.51515152, -2.45454545,-2.39393939, -2.33333333, -2.27272727, -2.21212121, -2.15151515,-2.09090909, -2.03030303, -1.96969697, -1.90909091, -1.84848485,-1.78787879, -1.72727273, -1.66666667, -1.60606061, -1.54545455,-1.48484848, -1.42424242, -1.36363636, -1.3030303 , -1.24242424,-1.18181818, -1.12121212, -1.06060606, -1. , -0.93939394,-0.87878788, -0.81818182, -0.75757576, -0.6969697 , -0.63636364,-0.57575758, -0.51515152, -0.45454545, -0.39393939, -0.33333333,-0.27272727, -0.21212121, -0.15151515, -0.09090909, -0.03030303,0.03030303, 0.09090909, 0.15151515, 0.21212121, 0.27272727,0.33333333, 0.39393939, 0.45454545, 0.51515152, 0.57575758,0.63636364, 0.6969697 , 0.75757576, 0.81818182, 0.87878788,0.93939394, 1. , 1.06060606, 1.12121212, 1.18181818,1.24242424, 1.3030303 , 1.36363636, 1.42424242, 1.48484848,1.54545455, 1.60606061, 1.66666667, 1.72727273, 1.78787879,1.84848485, 1.90909091, 1.96969697, 2.03030303, 2.09090909,2.15151515, 2.21212121, 2.27272727, 2.33333333, 2.39393939,2.45454545, 2.51515152, 2.57575758, 2.63636364, 2.6969697 ,2.75757576, 2.81818182, 2.87878788, 2.93939394, 3. ])ysarray([-0.62568008, 0.01486274, -0.29232541, -0.05271084, -0.53407957,-0.37199581, -0.40235236, -0.80005504, -0.2280913 , -0.96111433,-0.58732159, -0.71310851, -1.19817878, -0.93036437, -1.02682804,-1.33669261, -1.36873043, -0.44500172, -1.38769079, -0.52899793,-0.78090929, -1.1470421 , -0.79274726, -0.95139505, -1.3536293 ,-1.15097615, -1.04909201, -0.89071026, -0.81181765, -0.70292996,-0.49732344, -1.22800179, -1.21280414, -0.59583172, -1.05027515,-0.56369191, -0.68680323, -0.20454038, -0.32429566, -0.84640122,-0.08175012, -0.76910728, -0.59206189, -0.09984673, -0.52465978,-0.30498277, 0.08593627, -0.29488864, 0.24698113, -0.07324925,0.12773032, 0.55508531, 0.14794648, 0.40155342, 0.31717698,0.63213964, 0.35736413, 0.05264068, 0.39858619, 1.00710311,0.73844747, 1.12858026, 0.59779567, 1.22131999, 0.80849061,0.72796849, 1.0990044 , 0.45447096, 1.15217952, 1.31846002,1.27140258, 0.65264777, 1.15205186, 0.90705463, 0.82489198,0.50572125, 1.47115594, 0.98209434, 0.95763951, 0.50225094,1.40415029, 0.74618984, 0.90620692, 0.40593222, 0.62737999,1.05236579, 1.20041249, 1.14784273, 0.54798933, 0.18167682,0.50830766, 0.92498585, 0.9778136 , 0.42331405, 0.88163729,0.67235809, -0.00539421, -0.06219493, 0.26436412, 0.51978602])# 可视化图长和宽 plt.rcParamsfigure.figsize

   绘制散点图

   plt.scatter(xs, ys) plt.show()3. 定义基本模型

   占位

   X tf.placeholder(tf.float32, nameX) Y tf.placeholder(tf.float32, nameY)# 随机采样出变量 W tf.Variable(tf.random_normal([1]), nameweight) b tf.Variable(tf.random_normal([1]), namebias)# 手写y wxb Y_pred tf.add(tf.multiply(X, W), b) # 定义损失函数mse loss tf.square(Y - Y_pred, nameloss) # 学习率 learning_rate 0.01

   优化器就是tensorflow中梯度下降的策略

   定义梯度下降,申明学习率和针对那个loss求最小化

   optimizer tf.train.GradientDescentOptimizer(learning_rate).minimize(loss) 4. 训练模型

   去样本数量

   n_samples xs.shape[0] init tf.global_variables_initializer() with tf.Session() as sess:# 记得初始化所有变量sess.run(init) writer tf.summary.FileWriter(../graphs/linear_reg, sess.graph)# 训练模型for i in range(50):#初始化损失函数total_loss 0for x, y in zip(xs, ys):# 通过feeddic把数据灌进去, l sess.run([optimizer, loss], feed_dict{X: x, Y:y}) #_是optimizer的返回在这没有用就省略total_loss l #统计每轮样本的损失print(Epoch {0}: {1}.format(i, total_loss/n_samples)) #求损失平均# 关闭writerwriter.close() # 取出w和b的值W, b sess.run([W, b]) Epoch 0: [0.48447946] Epoch 1: [0.20947962] Epoch 2: [0.19649307] Epoch 3: [0.19527708] Epoch 4: [0.19514856] Epoch 5: [0.19513479] Epoch 6: [0.19513334] Epoch 7: [0.19513316] Epoch 8: [0.19513315] Epoch 9: [0.19513315] Epoch 10: [0.19513315] Epoch 11: [0.19513315] Epoch 12: [0.19513315] Epoch 13: [0.19513315] Epoch 14: [0.19513315] Epoch 15: [0.19513315] Epoch 16: [0.19513315] Epoch 17: [0.19513315] Epoch 18: [0.19513315] Epoch 19: [0.19513315] Epoch 20: [0.19513315] Epoch 21: [0.19513315] Epoch 22: [0.19513315] Epoch 23: [0.19513315] Epoch 24: [0.19513315] Epoch 25: [0.19513315] Epoch 26: [0.19513315] Epoch 27: [0.19513315] Epoch 28: [0.19513315] Epoch 29: [0.19513315] Epoch 30: [0.19513315] Epoch 31: [0.19513315] Epoch 32: [0.19513315] Epoch 33: [0.19513315] Epoch 34: [0.19513315] Epoch 35: [0.19513315] Epoch 36: [0.19513315] Epoch 37: [0.19513315] Epoch 38: [0.19513315] Epoch 39: [0.19513315] Epoch 40: [0.19513315] Epoch 41: [0.19513315] Epoch 42: [0.19513315] Epoch 43: [0.19513315] Epoch 44: [0.19513315] Epoch 45: [0.19513315] Epoch 46: [0.19513315] Epoch 47: [0.19513315] Epoch 48: [0.19513315] Epoch 49: [0.19513315]print(W,b) print(W:str(W[0])) print(b:str(b[0]))[0.23069778] [-0.12590201] W:0.23069778 b:-0.125902015. 线性回归图

   线性回归图

   plt.plot(xs, ys, bo, labelReal data) plt.plot(xs, xs * W b, r, labelPredicted data) plt.legend() plt.show()附系列文章 序号文章目录直达链接1波士顿房价预测https://want595.blog.csdn.net/article/details/1321819502鸢尾花数据集分析https://want595.blog.csdn.net/article/details/1321820573特征处理https://want595.blog.csdn.net/article/details/1321821654交叉验证https://want595.blog.csdn.net/article/details/1321822385构造神经网络示例https://want595.blog.csdn.net/article/details/1321823416使用TensorFlow完成线性回归https://want595.blog.csdn.net/article/details/1321824177使用TensorFlow完成逻辑回归https://want595.blog.csdn.net/article/details/1321824968TensorBoard案例https://want595.blog.csdn.net/article/details/1321825849使用Keras完成线性回归https://want595.blog.csdn.net/article/details/13218272310使用Keras完成逻辑回归https://want595.blog.csdn.net/article/details/13218279511使用Keras预训练模型完成猫狗识别https://want595.blog.csdn.net/article/details/13224392812使用PyTorch训练模型https://want595.blog.csdn.net/article/details/13224398913使用Dropout抑制过拟合https://want595.blog.csdn.net/article/details/13224411114使用CNN完成MNIST手写体识别(TensorFlow)https://want595.blog.csdn.net/article/details/13224449915使用CNN完成MNIST手写体识别(Keras)https://want595.blog.csdn.net/article/details/13224455216使用CNN完成MNIST手写体识别(PyTorch)https://want595.blog.csdn.net/article/details/13224464117使用GAN生成手写数字样本https://want595.blog.csdn.net/article/details/13224476418自然语言处理https://want595.blog.csdn.net/article/details/132276591