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。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 tfhello = 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 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 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
线性回归
以下代码来自https://github.com/aymericdamien/TensorFlow-Examples/,仅作学习用
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()
逻辑回归
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
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，最后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当中最后预测概率最高作为最终的分类。
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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