tensorflow 学习笔记 2

Github上有个学习tensorflow的git

https://github.com/nfmcclure/tensorflow_cookbook

内容完整，买了中文版，但是却很多细节，所以还是直接看github原版好了。电子书是ipynb格式的，ubuntu看起来很容易。

从第一个example开始：

电子书

https://github.com/nfmcclure/tensorflow_cookbook/tree/master/01_Introduction/02_Creating_and_Using_Tensors

代码链接：

https://github.com/nfmcclure/tensorflow_cookbook/blob/master/01_Introduction/02_Creating_and_Using_Tensors/02_tensors.py

代码比较短，贴一下好了，对照相关部分的电子书和tensorboard的输出还是能加强一些理解的

# Tensors #---------------------------------- # # This function introduces various ways to create # tensors in TensorFlow   import tensorflow as tf from tensorflow.python.frameworkimport ops ops.reset_default_graph()   # Introduce tensors in tf   # Get graph handle sess = tf.Session()   my_tensor = tf.zeros([1,20])   # Declare a variable my_var = tf.Variable(tf.zeros([1,20]))   # Different kinds of variables row_dim = 2 col_dim = 3   # Zero initialized variable zero_var = tf.Variable(tf.zeros([row_dim, col_dim]))   # One initialized variable ones_var = tf.Variable(tf.ones([row_dim, col_dim]))   # shaped like other variable sess.run(zero_var.initializer) sess.run(ones_var.initializer) zero_similar = tf.Variable(tf.zeros_like(zero_var)) ones_similar = tf.Variable(tf.ones_like(ones_var))   sess.run(ones_similar.initializer) sess.run(zero_similar.initializer)   # Fill shape with a constant fill_var = tf.Variable(tf.fill([row_dim, col_dim],-1))   # Create a variable from a constant const_var = tf.Variable(tf.constant([8,6,7, 5, 3, 0, 9])) # This can also be used to fill an array: const_fill_var = tf.Variable(tf.constant(-1,shape=[row_dim, col_dim]))   # Sequence generation linear_var = tf.Variable(tf.linspace(start=0.0,stop=1.0,num=3))# Generates [0.0, 0.5, 1.0] includes the end   sequence_var = tf.Variable(tf.range(start=6,limit=15,delta=3))# Generates [6, 9, 12] doesn't include the end   # Random Numbers   # Random Normal rnorm_var = tf.random_normal([row_dim, col_dim],mean=0.0,stddev=1.0)   # Add summaries to tensorboard merged = tf.summary.merge_all()   # Initialize graph writer:   writer = tf.summary.FileWriter("/tmp/variable_logs",graph=sess.graph)   # Initialize operation initialize_op = tf.global_variables_initializer()   # Run initialization of variable sess.run(initialize_op)

在贴个tensorboard的graph：

如果点击一个具体的变量，会看到与语句的关系，比如

tf.Variable(tf.zeros([1,20]))

对应的就是：

所以很简单的一个理解就是tensorflow框架对tensor做了运算，然后通过tensorboard可以直观的观察到。

一些额外的学习资料：

# Additional Resources###Official Resources: - [TensorFlow Python API](https://www.tensorflow.org/api_docs/python/) - [TensorFlow on Github](https://github.com/tensorflow/tensorflow) - [TensorFlow Tutorials](https://www.tensorflow.org/tutorials/) - [Udacity Deep Learning Class](https://www.udacity.com/course/deep-learning--ud730) - [TensorFlow Playground](http://playground.tensorflow.org/)###Github Tutorials and Examples: - [Tutorials by pkmital](https://github.com/pkmital/tensorflow_tutorials) - [Tutorials by nlintz](https://github.com/nlintz/TensorFlow-Tutorials) - [Examples by americdamien](https://github.com/aymericdamien/TensorFlow-Examples) - [TensorFlow Workshop by amygdala](https://github.com/amygdala/tensorflow-workshop)###Deep Learning Resources - [Efficient Back Prop by Yann LeCun, et. al.](http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf) - [Online Deep Learning Book, MIT Press](http://www.deeplearningbook.org/) - [An Overview of Gradient Descent Algorithms by Sebastian Ruder](http://sebastianruder.com/optimizing-gradient-descent/) - [Stochastic Optimization by John Duchi, et. al.](http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf) - [ADADELTA Method by Matthew Zeiler](http://arxiv.org/abs/1212.5701) - [A Friendly Introduction to Cross-Entropy Loss by Rob DiPietro](http://rdipietro.github.io/friendly-intro-to-cross-entropy-loss/)###Additional Resources - [A Curated List of Dedicated TensorFlow Resources](https://github.com/jtoy/awesome-tensorflow/)###Arxiv Papers - [TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems](http://arxiv.org/abs/1603.04467) - [TensorFlow: A system for large-scale machine learning](http://arxiv.org/abs/1605.08695) - [Distributed TensorFlow with MPI](https://arxiv.org/abs/1603.02339) - [Comparative Study of Deep Learning Software Frameworks](https://arxiv.org/abs/1511.06435) - [Wide & Deep Learning for Recommender Systems](https://arxiv.org/abs/1606.07792)