【博客存檔】TensorFlow入門一

cuda與tensorflow安裝

按以往經驗，tensorflow安裝一條pip命令就可以解決，前提是有fq工具，沒有的話去找找牆內別人分享的地址。而坑多在安裝支持gpu，需預先安裝英偉達的cuda，這裡坑比較多，推薦使用ubuntu deb的安裝方式來安裝cuda，run.sh的方式總感覺有很多問題，cuda的安裝具體可以參考。 注意鏈接裡面的tensorflow版本是以前的，tensorflow 現在官方上的要求是cuda7.5+cudnnV4，請在安裝的時候注意下。

Hello World

import tensorflow as tfhello = tf.constant(Hello, TensorFlow!)sess = tf.Session()print sess.run(hello)

首先，通過tf.constant創建一個常量，然後啟動Tensorflow的Session，調用sess的run方法來啟動整個graph。 接下來我們做下簡單的數學的方法：

import tensorflow as tfa = 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)# outputa=2, b=3Addition with constants: 5Multiplication with constants: 6

接下來用tensorflow的placeholder來定義變數做類似計算： placeholder的使用見https://www.tensorflow.org/versions/r0.8/api_docs/python/io_ops.html#placeholder

import tensorflow as tfa = 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: 5Multiplication with variables: 6matrix1 = tf.constant([[3., 3.]])matrix2 = tf.constant([[2.],[2.]])product=tf.matmul(matrix1,matrix2)with tf.Session() as sess: result = sess.run(product) print result #result: 12

線性回歸

以下代碼來自https://github.com/aymericdamien/TensorFlow-Examples/,僅作學慣用

import tensorflow as tfimport numpyimport matplotlib.pyplot as pltrng = numpy.random# Parameterslearning_rate = 0.01training_epochs = 2000display_step = 50# Training Datatrain_X = numpy.asarray([3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167,7.042,10.791,5.313,7.997,5.654,9.27,3.1])train_Y = numpy.asarray([1.7,2.76,2.09,3.19,1.694,1.573,3.366,2.596,2.53,1.221,2.827,3.465,1.65,2.904,2.42,2.94,1.3])n_samples = train_X.shape[0]# tf Graph InputX = tf.placeholder("float")Y = tf.placeholder("float")# Create Model# Set model weightsW = tf.Variable(rng.randn(), name="weight")b = tf.Variable(rng.randn(), name="bias")# Construct a linear modelactivation = tf.add(tf.mul(X, W), b)# Minimize the squared errorscost = tf.reduce_sum(tf.pow(activation-Y, 2))/(2*n_samples) #L2 lossoptimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) #Gradient descent# Initializing the variablesinit = tf.initialize_all_variables()# Launch the graphwith 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()

邏輯回歸

import tensorflow as tf# Import MINST datafrom tensorflow.examples.tutorials.mnist import input_datamnist = input_data.read_data_sets("/tmp/data/", one_hot=True)# Parameterslearning_rate = 0.01training_epochs = 25batch_size = 100display_step = 1# tf Graph Inputx = tf.placeholder(tf.float32, [None, 784]) # mnist data image of shape 28*28=784y = tf.placeholder(tf.float32, [None, 10]) # 0-9 digits recognition => 10 classes# Set model weightsW = tf.Variable(tf.zeros([784, 10]))b = tf.Variable(tf.zeros([10]))# Construct modelpred = tf.nn.softmax(tf.matmul(x, W) + b) # Softmax# Minimize error using cross entropycost = tf.reduce_mean(-tf.reduce_sum(y*tf.log(pred), reduction_indices=1))# Gradient Descentoptimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)# Initializing the variablesinit = tf.initialize_all_variables()# Launch the graphwith 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

這裡有個小插曲，ipython notebook在一個notebook打開時，一直在佔用GPU資源，可能是之前有一個notebook一直打開著，然後佔用著GPU資源，然後在計算Accuracy的」InternalError: Dst tensor is not initialized.」 然後找了github上面也有這個問題InternalError: Dst tensor is not initialized.,可以肯定是GPU的memory相關的問題，所以就嘗試加上tf.device(『/cpu:0』)，將Accuracy這步拉到cpu上計算，但是又出現OOM的問題，最後nvidia-smi時，發現有一個python腳本一直佔用3g多的顯存，把它kill之後恢復了，之前還比較吐槽怎麼可能10000*784個float就把顯存撐爆呢，原來是自己的問題。

這裡邏輯回歸，model是一個softmax函數用來做多元分類，大概意思是選擇10當中最後預測概率最高作為最終的分類。

其實基本的tensorflow沒有特別好講的，語法的課程什麼可以去看看基本的文檔，之後我會找一點經典有趣的tensorflow的代碼應用來看看，畢竟『show me the code 』才是程序猿應有的態度。