TensorFlow 2.0 代码实战专栏（三）：逻辑回归

2019 年 11 月 22 日
筆記

作者 | Aymeric Damien

编辑 | 奇予纪

专栏目录：

第一章：TensorFlow 2.0 代码实战专栏开篇机器学习介绍 MNIST数据集介绍
第二章：TensorFlow 2.0 介绍 Hello World 基础操作
第三章：基础模型线性回归逻辑回归 Word2Vec（Word Embedding）
第四章：神经网络

逻辑斯谛回归示例：

使用TensorFlow v2库实现逻辑斯谛回归，此示例使用简单方法来更好地理解训练过程背后的所有机制。

MNIST数据集概览

此示例使用MNIST手写数字。该数据集包含60,000个用于训练的样本和10,000个用于测试的样本。这些数字已经过尺寸标准化并位于图像中心，图像是固定大小(28×28像素)，其值为0到255。

在此示例中，每个图像将转换为float32，归一化为[0,1]，并展平为784个特征（28 * 28）的1维数组。

mark

from __future__ import absolute_import,division,print_function    import tensorflow as tf  import numpy as np

# MNIST 数据集参数  num_classes = 10 # 数字0-9  num_features = 784 # 28*28    # 训练参数  learning_rate = 0.01  training_steps = 1000  batch_size = 256  display_step = 50

# 准备MNIST数据  from tensorflow.keras.datasets import mnist  (x_train, y_train),(x_test,y_test) = mnist.load_data()  # 转换为float32  x_train, x_test = np.array(x_train, np.float32), np.array(x_test, np.float32)  # 将图像平铺成784个特征的一维向量(28*28)  x_train, x_test = x_train.reshape([-1, num_features]), x_test.reshape([-1, num_features])  # 将像素值从[0,255]归一化为[0,1]  x_train,x_test = x_train / 255, x_test / 255

# 使用tf.data api 对数据随机分布和批处理  train_data = tf.data.Dataset.from_tensor_slices((x_train, y_train))  train_data = train_data.repeat().shuffle(5000).batch(batch_size).prefetch(1)

# 权值矩阵形状[784,10]，28 * 28图像特征数和类别数目  W = tf.Variable(tf.ones([num_features, num_classes]), name="weight")  # 偏置形状[10], 类别数目  b = tf.Variable(tf.zeros([num_classes]), name="bias")    # 逻辑斯谛回归（Wx+b)  def logistic_regression(x):      #应用softmax将logits标准化为概率分布      return tf.nn.softmax(tf.matmul(x,W) + b)    # 交叉熵损失函数  def cross_entropy(y_pred, y_true):      # 将标签编码为一个独热编码向量      y_true = tf.one_hot(y_true, depth=num_classes)      # 压缩预测值以避免log（0）错误      y_pred = tf.clip_by_value(y_pred, 1e-9, 1.)      # 计算交叉熵      return tf.reduce_mean(-tf.reduce_sum(y_true * tf.math.log(y_pred)))    # 准确率度量  def accuracy(y_pred, y_true):      # 预测的类别是预测向量中最高分的索引（即argmax）      correct_prediction = tf.equal(tf.argmax(y_pred, 1), tf.cast(y_true, tf.int64))      return tf.reduce_mean(tf.cast(correct_prediction, tf.float32))    # 随机梯度下降优化器  optimizer = tf.optimizers.SGD(learning_rate)

# 优化过程  def run_optimization(x, y):      #将计算封装在GradientTape中以实现自动微分      with tf.GradientTape() as g:          pred = logistic_regression(x)          loss = cross_entropy(pred, y)        # 计算梯度      gradients = g.gradient(loss, [W, b])        # 根据gradients更新 W 和 b      optimizer.apply_gradients(zip(gradients, [W, b]))

# 针对给定训练步骤数开始训练  for step, (batch_x,batch_y) in enumerate(train_data.take(training_steps), 1):      # 运行优化以更新W和b值      run_optimization(batch_x, batch_y)        if step % display_step == 0:          pred = logistic_regression(batch_x)          loss = cross_entropy(pred, batch_y)          acc = accuracy(pred, batch_y)          print("step: %i, loss: %f, accuracy: %f" % (step, loss, acc))

output:

step: 50, loss: 608.584717, accuracy: 0.824219  step: 100, loss: 828.206482, accuracy: 0.765625  step: 150, loss: 716.329407, accuracy: 0.746094  step: 200, loss: 584.887634, accuracy: 0.820312  step: 250, loss: 472.098114, accuracy: 0.871094  step: 300, loss: 621.834595, accuracy: 0.832031  step: 350, loss: 567.288818, accuracy: 0.714844  step: 400, loss: 489.062988, accuracy: 0.847656  step: 450, loss: 496.466675, accuracy: 0.843750  step: 500, loss: 465.342224, accuracy: 0.875000  step: 550, loss: 586.347168, accuracy: 0.855469  step: 600, loss: 95.233109, accuracy: 0.906250  step: 650, loss: 88.136490, accuracy: 0.910156  step: 700, loss: 67.170349, accuracy: 0.937500  step: 750, loss: 79.673691, accuracy: 0.921875  step: 800, loss: 112.844872, accuracy: 0.914062  step: 850, loss: 92.789581, accuracy: 0.894531  step: 900, loss: 80.116165, accuracy: 0.921875  step: 950, loss: 45.706650, accuracy: 0.925781  step: 1000, loss: 72.986969, accuracy: 0.925781

# 在验证集上测试模型  pred = logistic_regression(x_test)  print("Test Accuracy: %f" % accuracy(pred, y_test))

output:

Test Accuracy: 0.901100

# 可视化预测  import matplotlib.pyplot as plt    # 在验证集上中预测5张图片  n_images = 5  test_images = x_test[:n_images]  predictions = logistic_regression(test_images)    # 可视化图片和模型预测结果  for i in range(n_images):      plt.imshow(np.reshape(test_images[i],[28,28]), cmap='gray')      plt.show()      print("Model prediction: %i" % np.argmax(predictions.numpy()[i]))

output: