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 plt
2. 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)
xs
array([-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. ])
ys
array([-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.rcParams["figure.figsize"] = (6,4) # 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, name='X') Y = tf.placeholder(tf.float32, name='Y')
# 随机采样出变量 W = tf.Variable(tf.random_normal([1]), name='weight') b = tf.Variable(tf.random_normal([1]), name='bias')
# 手写y = wx+b Y_pred = tf.add(tf.multiply(X, W), b)
# 定义损失函数mse loss = tf.square(Y - Y_pred, name='loss')
# 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 = 0 for x, y in zip(xs, ys): # 通过feed_dic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writer writer.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:-0.12590201
5. 线性回归图
# 线性回归图 plt.plot(xs, ys, 'bo', label='Real data') plt.plot(xs, xs * W + b, 'r', label='Predicted data') plt.legend() plt.show()
