為什么80%的碼農都做不了架構師？>>> ??

例子1

先從helloworld開始:?

t@ubuntu:~$ python Python 2.7.6 (default, Oct 26 2016, 20:30:19) [GCC 4.8.4] on linux2 Type "help", "copyright", "credits" or "license" for more information. >>> import tensorflow as tf >>> hello=tf.constant('hello,tensorFlow!') >>> sess = tf.Session() >>> print sess.run(hello) hello,tensorFlow! >>> a = tf.constant(10) >>> b = tf.constant(122) >>> print sess.run(a+b) 132

接下去兩個步驟：1，學python；2，看ts；

例子2

手寫數字識別，在ubuntu中安裝部署好環境；

代碼源自https://github.com/niektemme/tensorflow-mnist-predict

創建訓練用python代碼

# Copyright 2016 Niek Temme. # Adapted form the on the MNIST biginners tutorial by Google. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # =============================================================================="""A very simple MNIST classifier. Documentation at http://niektemme.com/ @@to doThis script is based on the Tensoflow MNIST beginners tutorial See extensive documentation for the tutorial at https://www.tensorflow.org/versions/master/tutorials/mnist/beginners/index.html """#import modules import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data#import data mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)# Create the model x = tf.placeholder(tf.float32, [None, 784]) W = tf.Variable(tf.zeros([784, 10])) b = tf.Variable(tf.zeros([10])) y = tf.nn.softmax(tf.matmul(x, W) + b)# Define loss and optimizer y_ = tf.placeholder(tf.float32, [None, 10]) cross_entropy = -tf.reduce_sum(y_*tf.log(y)) train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)# init_op = tf.global_variables_initializer() 看版本，使用該行還是使用下面那行 init_op = tf.initialize_all_variables() saver = tf.train.Saver()# Train the model and save the model to disk as a model.ckpt file # file is stored in the same directory as this python script is started """ The use of 'with tf.Session() as sess:' is taken from the Tensor flow documentation on on saving and restoring variables. https://www.tensorflow.org/versions/master/how_tos/variables/index.html """ with tf.Session() as sess:sess.run(init_op)for i in range(1000):batch_xs, batch_ys = mnist.train.next_batch(100)sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})save_path = saver.save(sess, "/tmp/model.ckpt")print ("Model saved in file: ", save_path)

測試代碼

# Copyright 2016 Niek Temme. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # =============================================================================="""Predict a handwritten integer (MNIST beginners).Script requires 1) saved model (model.ckpt file) in the same location as the script is run from. (requried a model created in the MNIST beginners tutorial) 2) one argument (png file location of a handwritten integer)Documentation at: http://niektemme.com/ @@to do """#import modules import sys import tensorflow as tf from PIL import Image,ImageFilterdef predictint(imvalue):"""This function returns the predicted integer.The imput is the pixel values from the imageprepare() function."""# Define the model (same as when creating the model file)x = tf.placeholder(tf.float32, [None, 784])W = tf.Variable(tf.zeros([784, 10]))b = tf.Variable(tf.zeros([10]))y = tf.nn.softmax(tf.matmul(x, W) + b)init_op = tf.global_variables_initializer()saver = tf.train.Saver()"""Load the model.ckpt filefile is stored in the same directory as this python script is startedUse the model to predict the integer. Integer is returend as list.Based on the documentatoin athttps://www.tensorflow.org/versions/master/how_tos/variables/index.html"""with tf.Session() as sess:sess.run(init_op)saver.restore(sess, "/tmp/model.ckpt")#print ("Model restored.")prediction=tf.argmax(y,1)return prediction.eval(feed_dict={x: [imvalue]}, session=sess)def imageprepare(argv):"""This function returns the pixel values.The imput is a png file location."""im = Image.open(argv).convert('L')width = float(im.size[0])height = float(im.size[1])newImage = Image.new('L', (28, 28), (255)) #creates white canvas of 28x28 pixelsif width > height: #check which dimension is bigger#Width is bigger. Width becomes 20 pixels.nheight = int(round((20.0/width*height),0)) #resize height according to ratio widthif (nheigth == 0): #rare case but minimum is 1 pixelnheigth = 1 # resize and sharpenimg = im.resize((20,nheight), Image.ANTIALIAS).filter(ImageFilter.SHARPEN)wtop = int(round(((28 - nheight)/2),0)) #caculate horizontal pozitionnewImage.paste(img, (4, wtop)) #paste resized image on white canvaselse:#Height is bigger. Heigth becomes 20 pixels. nwidth = int(round((20.0/height*width),0)) #resize width according to ratio heightif (nwidth == 0): #rare case but minimum is 1 pixelnwidth = 1# resize and sharpenimg = im.resize((nwidth,20), Image.ANTIALIAS).filter(ImageFilter.SHARPEN)wleft = int(round(((28 - nwidth)/2),0)) #caculate vertical pozitionnewImage.paste(img, (wleft, 4)) #paste resized image on white canvas#newImage.save("sample.png")tv = list(newImage.getdata()) #get pixel values#normalize pixels to 0 and 1. 0 is pure white, 1 is pure black.tva = [ (255-x)*1.0/255.0 for x in tv] return tva#print(tva)def main(argv):"""Main function."""imvalue = imageprepare(argv)predint = predictint(imvalue)print (predint[0]) #first value in listif __name__ == "__main__":main(sys.argv[1])

運行結果：

矩陣-線性代數-http://www2.edu-edu.com.cn/lesson_crs78/self/j_0022/soft/ch0605.html

?

這本書不錯：超智能體https://yjango.gitbooks.io/superorganism/content/dai_ma_yan_shi_2.html

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轉載于:https://my.oschina.net/u/856051/blog/869692

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