使用TensorFlow完成逻辑回归
TensorFlow是一种开源的机器学习框架，由Google Brain团队于2015年开发。它被广泛应用于图像和语音识别、自然语言处理、推荐系统等领域。
TensorFlow的核心是用于计算的数据流图。在数据流图中，节点表示数学操作，边表示张量（多维数组）。将操作和数据组合在一起的数据流图可以使 TensorFlow 对复杂的数学模型进行优化，同时支持分布式计算。
TensorFlow提供了Python，C++，Java，Go等多种编程语言的接口，让开发者可以更便捷地使用TensorFlow构建和训练深度学习模型。此外，TensorFlow还具有丰富的工具和库，包括TensorBoard可视化工具、TensorFlow Serving用于生产环境的模型服务、Keras高层封装API等。
TensorFlow已经发展出了许多优秀的模型，如卷积神经网络、循环神经网络、生成对抗网络等。这些模型已经在许多领域取得了优秀的成果，如图像识别、语音识别、自然语言处理等。
除了开源的TensorFlow，Google还推出了基于TensorFlow的云端机器学习平台Google Cloud ML，为用户提供了更便捷的训练和部署机器学习模型的服务。
解决分类问题里最普遍的baseline model就是逻辑回归，简单同时可解释性好，使得它大受欢迎，我们来用tensorflow完成这个模型的搭建。
1. 环境设定
import os os.environ['TF_CPP_MIN_LOG_LEVEL']='2' import warnings warnings.filterwarnings("ignore") import numpy as np import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data import time
2. 数据读取
#使用tensorflow自带的工具加载MNIST手写数字集合 mnist = input_data.read_data_sets('./data/mnist', one_hot=True)
Extracting ./data/mnist/train-images-idx3-ubyte.gz Extracting ./data/mnist/train-labels-idx1-ubyte.gz Extracting ./data/mnist/t10k-images-idx3-ubyte.gz Extracting ./data/mnist/t10k-labels-idx1-ubyte.gz
#查看一下数据维度 mnist.train.images.shape
(55000, 784)
#查看target维度 mnist.train.labels.shape
(55000, 10)
3. 准备好placeholder
batch_size = 128 X = tf.placeholder(tf.float32, [batch_size, 784], name='X_placeholder') Y = tf.placeholder(tf.int32, [batch_size, 10], name='Y_placeholder')
4. 准备好参数/权重
w = tf.Variable(tf.random_normal(shape=[784, 10], stddev=0.01), name='weights') b = tf.Variable(tf.zeros([1, 10]), name="bias")
logits = tf.matmul(X, w) + b
5. 计算多分类softmax的loss function
# 求交叉熵损失 entropy = tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=Y, name='loss') # 求平均 loss = tf.reduce_mean(entropy)
6. 准备好optimizer
这里的最优化用的是随机梯度下降，我们可以选择AdamOptimizer这样的优化器
learning_rate = 0.01 optimizer = tf.train.AdamOptimizer(learning_rate).minimize(loss)
7. 在session里执行graph里定义的运算
#迭代总轮次 n_epochs = 30 with tf.Session() as sess: # 在Tensorboard里可以看到图的结构 writer = tf.summary.FileWriter('../graphs/logistic_reg', sess.graph) start_time = time.time() sess.run(tf.global_variables_initializer()) n_batches = int(mnist.train.num_examples/batch_size) for i in range(n_epochs): # 迭代这么多轮 total_loss = 0 for _ in range(n_batches): X_batch, Y_batch = mnist.train.next_batch(batch_size) _, loss_batch = sess.run([optimizer, loss], feed_dict={X: X_batch, Y:Y_batch}) total_loss += loss_batch print('Average loss epoch {0}: {1}'.format(i, total_loss/n_batches)) print('Total time: {0} seconds'.format(time.time() - start_time)) print('Optimization Finished!') # 测试模型 preds = tf.nn.softmax(logits) correct_preds = tf.equal(tf.argmax(preds, 1), tf.argmax(Y, 1)) accuracy = tf.reduce_sum(tf.cast(correct_preds, tf.float32)) n_batches = int(mnist.test.num_examples/batch_size) total_correct_preds = 0 for i in range(n_batches): X_batch, Y_batch = mnist.test.next_batch(batch_size) accuracy_batch = sess.run([accuracy], feed_dict={X: X_batch, Y:Y_batch}) total_correct_preds += accuracy_batch[0] print('Accuracy {0}'.format(total_correct_preds/mnist.test.num_examples)) writer.close()
Average loss epoch 0: 0.36748782022571785 Average loss epoch 1: 0.2978815356126198 Average loss epoch 2: 0.27840628396797845 Average loss epoch 3: 0.2783186247437706 Average loss epoch 4: 0.2783641471138923 Average loss epoch 5: 0.2750668214473413 Average loss epoch 6: 0.2687560408126502 Average loss epoch 7: 0.2713795114126239 Average loss epoch 8: 0.2657588795522154 Average loss epoch 9: 0.26322007090686916 Average loss epoch 10: 0.26289192279735646 Average loss epoch 11: 0.26248606019989873 Average loss epoch 12: 0.2604622903056356 Average loss epoch 13: 0.26015280702939403 Average loss epoch 14: 0.2581879366319496 Average loss epoch 15: 0.2590309207117085 Average loss epoch 16: 0.2630510463581219 Average loss epoch 17: 0.25501730025578767 Average loss epoch 18: 0.2547102673000945 Average loss epoch 19: 0.258298404375851 Average loss epoch 20: 0.2549241428330784 Average loss epoch 21: 0.2546788509283866 Average loss epoch 22: 0.259556887067837 Average loss epoch 23: 0.25428259843365575 Average loss epoch 24: 0.25442713139565676 Average loss epoch 25: 0.2553852511383159 Average loss epoch 26: 0.2503043229415978 Average loss epoch 27: 0.25468004046828596 Average loss epoch 28: 0.2552785321479633 Average loss epoch 29: 0.2506257003663859 Total time: 28.603315353393555 seconds Optimization Finished! Accuracy 0.9187
