机器学习进阶笔记之一 | TensorFlow安装与入门
引言
TensorFlow是机器进阶Google基于DistBelief进行研发的第二代人工智能学习系统，被广泛用于语音识别或图像识别等多项机器深度学习领域。学习其命名来源于本身的笔记运行原理。Tensor(张量)意味着N维数组，装入Flow(流)意味着基于数据流图的机器进阶计算，TensorFlow代表着张量从图象的学习一端流动到另一端计算过程，是笔记将复杂的数据结构传输至人工智能神经网中进行分析和处理的过程。
TensorFlow完全开源，装入任何人都可以使用。机器进阶可在小到一部智能手机、学习大到数千台数据中心服务器的笔记各种设备上运行。
『机器学习进阶笔记』系列是装入将深入解析TensorFlow系统的技术实践，从零开始，机器进阶由浅入深，学习与大家一起走上机器学习的笔记进阶之路。
CUDA与TensorFlow安装
按以往经验，TensorFlow安装一条pip命令就可以解决，前提是有fq工具，没有的话去找找墙内别人分享的地址。服务器托管而坑多在安装支持gpu，需预先安装英伟达的cuda，这里坑比较多，推荐使用ubuntu deb的安装方式来安装cuda，run.sh的方式总感觉有很多问题，cuda的安装具体可以参考。 注意链接里面的tensorflow版本是以前的，tensorflow 现在官方上的要求是cuda7.5+cudnnV4，请在安装的时候注意下。
Hello World
import tensorflow as tf hello = tf.constant(Hello, TensorFlow!) sess = tf.Session() print sess.run(hello)首先，通过tf.constant创建一个常量，然后启动Tensorflow的Session，调用sess的run方法来启动整个graph。
接下来我们做下简单的数学的方法：
import tensorflow as tf a = tf.constant(2) b = tf.constant(3) with tf.Session() as sess: print "a=2, b=3" print "Addition with constants: %i" % sess.run(a+b) print "Multiplication with constants: %i" % sess.run(a*b) # output a=2, b=3 Addition with constants: 5 Multiplication with constants: 6接下来用tensorflow的placeholder来定义变量做类似计算：
placeholder的使用见https://www.tensorflow.org/versions/r0.8/api_docs/python/io_ops.html#placeholder
import tensorflow as tf a = tf.placeholder(tf.int16) b = tf.placeholder(tf.int16) add = tf.add(a, b) mul = tf.mul(a, b) with tf.Session() as sess: # Run every operation with variable input print "Addition with variables: %i" % sess.run(add, feed_dict={ a: 2, b: 3}) print "Multiplication with variables: %i" % sess.run(mul, feed_dict={ a: 2, b: 3}) # output: Addition with variables: 5 Multiplication with variables: 6 matrix1 = tf.constant([[3., 3.]]) matrix2 = tf.constant([[2.],[2.]]) with tf.Session() as sess: result = sess.run(product) print result线性回归
以下代码来自GitHub - aymericdamien/TensorFlow-Examples: TensorFlow Tutorial and Examples for beginners,仅作学习用
activation = tf.add(tf.mul(X, W), b) # Minimize the squared errors cost = tf.reduce_sum(tf.pow(activation-Y, 2))/(2*n_samples) #L2 loss optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) #Gradient descent # Initializing the variables init = tf.initialize_all_variables() # Launch the graph with tf.Session() as sess: sess.run(init) # Fit all training data for epoch in range(training_epochs): for (x, y) in zip(train_X, train_Y): sess.run(optimizer, feed_dict={ X: x, Y: y}) #Display logs per epoch step if epoch % display_step == 0: print "Epoch:", %04d % (epoch+1), "cost=", \ "{ :.9f}".format(sess.run(cost, feed_dict={ X: train_X, Y:train_Y})), \ "W=", sess.run(W), "b=", sess.run(b) print "Optimization Finished!" print "cost=", sess.run(cost, feed_dict={ X: train_X, Y: train_Y}), \ "W=", sess.run(W), "b=", sess.run(b) #Graphic display plt.plot(train_X, train_Y, ro, label=Original data) plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label=Fitted line) plt.legend() plt.show()逻辑回归
import tensorflow as tf # Import MINST data from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) # Parameters learning_rate = 0.01 training_epochs = 25 batch_size = 100 display_step = 1 # tf Graph Input x = tf.placeholder(tf.float32, [None, 784]) # mnist data image of shape 28*28=784 y = tf.placeholder(tf.float32, [None, 10]) # 0-9 digits recognition => 10 classes # Set model weights W = tf.Variable(tf.zeros([784, 10])) b = tf.Variable(tf.zeros([10])) # Construct model pred = tf.nn.softmax(tf.matmul(x, W) + b) # Softmax # Minimize error using cross entropy cost = tf.reduce_mean(-tf.reduce_sum(y*tf.log(pred), reduction_indices=1)) # Gradient Descent optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) # Initializing the variables init = tf.initialize_all_variables() # Launch the graph with tf.Session() as sess: sess.run(init) # Training cycle for epoch in range(training_epochs): avg_cost = 0. total_batch = int(mnist.train.num_examples/batch_size) # Loop over all batches for i in range(total_batch): batch_xs, batch_ys = mnist.train.next_batch(batch_size) # Run optimization op (backprop) and cost op (to get loss value) _, c = sess.run([optimizer, cost], feed_dict={ x: batch_xs, y: batch_ys}) # Compute average loss avg_cost += c / total_batch # Display logs per epoch step if (epoch+1) % display_step == 0: print "Epoch:", %04d % (epoch+1), "cost=", "{ :.9f}".format(avg_cost) print "Optimization Finished!" # Test model correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1)) # Calculate accuracy accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) print "Accuracy:", accuracy.eval({ x: mnist.test.images, y: mnist.test.labels}) # result : Epoch: 0001 cost= 29.860467369 Epoch: 0002 cost= 22.001451784 Epoch: 0003 cost= 21.019925554 Epoch: 0004 cost= 20.561320320 Epoch: 0005 cost= 20.109135756 Epoch: 0006 cost= 19.927862290 Epoch: 0007 cost= 19.548687116 Epoch: 0008 cost= 19.429119071 Epoch: 0009 cost= 19.397068211 Epoch: 0010 cost= 19.180813479 Epoch: 0011 cost= 19.026808132 Epoch: 0012 cost= 19.057875510 Epoch: 0013 cost= 19.009575057 Epoch: 0014 cost= 18.873240641 Epoch: 0015 cost= 18.718575359 Epoch: 0016 cost= 18.718761925 Epoch: 0017 cost= 18.673640560 Epoch: 0018 cost= 18.562128253 Epoch: 0019 cost= 18.458205289 Epoch: 0020 cost= 18.538211225 Epoch: 0021 cost= 18.443384213 Epoch: 0022 cost= 18.428727668 Epoch: 0023 cost= 18.304270616 Epoch: 0024 cost= 18.323529782 Epoch: 0025 cost= 18.247192113 Optimization Finished! (10000, 784) Accuracy 0.9206这里有个小插曲，ipython notebook在一个notebook打开时，一直在占用GPU资源，可能是站群服务器之前有一个notebook一直打开着，然后占用着GPU资源，然后在计算Accuracy的”InternalError: Dst tensor is not initialized.” 然后找了github上面也有这个问题InternalError: Dst tensor is not initialized.,可以肯定是GPU的memory相关的问题，所以就尝试加上tf.device(‘/cpu:0’)，将Accuracy这步拉到cpu上计算，但是又出现OOM的问题，***nvidia-smi时，发现有一个python脚本一直占用3g多的显存，把它kill之后恢复了，之前还比较吐槽怎么可能10000*784个float就把显存撑爆呢，原来是自己的问题。
这里逻辑回归，model是一个softmax函数用来做多元分类，大概意思是选择10当中***预测概率***作为最终的分类。
其实基本的tensorflow没有特别好讲的，语法的课程什么可以去看看基本的文档，之后我会找一点经典有趣的亿华云tensorflow的代码应用来看看，毕竟『show me the code 』才是程序猿应有的态度。
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