Tensorflow快速入门教程

什么是Tensorflow?

• Tensorflow是google发布的深度学习开源框架
• Tensorflow提供了定义张量的函数，而且可以自动的计算导数

TensorFlow VS. Numpy

• Few people make this comparison, but TensorFlow and
  Numpy are quite similar. (Both are N-d array libraries!)
• Numpy has Ndarray support, but doesn’t offer methods to
  create tensor functions and automatically compute
  derivatives (+ no GPU support).

Simple Numpy Recap

In [23]: import numpy as npIn [24]: a = np.zeros((2,2)); b = np.ones((2,2))In [25]: np.sum(b, axis=1)Out[25]: array([ 2., 2.])In [26]: a.shapeOut[26]: (2, 2)In [27]: np.reshape(a, (1,4))Out[27]: array([[ 0., 0., 0., 0.]])

Repeat in TensorFlow

In [31]: import tensorflow as tfIn [32]: tf.InteractiveSession()    ##More on SessionsoonIn [33]: a = tf.zeros((2,2)); b = tf.ones((2,2))In [34]: tf.reduce_sum(b, reduction_indices=1).eval()Out[34]: array([ 2., 2.], dtype=float32)In [35]: a.get_shape()      ##TensorShape behaveslike a python tuple.Out[35]: TensorShape([Dimension(2), Dimension(2)])In [36]: tf.reshape(a, (1, 4)).eval()Out[36]: array([[ 0., 0., 0., 0.]], dtype=float32)

● tf.InteractiveSession() is just convenient syntactic sugar for keeping a default session open in ipython.
● sess.run(c) is an example of a TensorFlow Fetch. Will say more on this soon

Tensorflow Computation Graph

• TensorFlow programs are usually structured into a construction phase, that assembles a graph, and an execution phase that uses a session to execute ops in the graph.
• All computations add nodes to global default graph

Tensorflow Variables

-“When you train a model you use variables to hold and update parameters. Variables are in-memory buffers containing tensors
- All tensors we’ve used previously have been constant tensors, not variables

Updating Variable State

Fetch Variable State

Inputing Data

Variable Scope

Understanding get_variable()

Tensorflow实战：线性回归

计算流图如下所示：

Tips: 关于更多的Tensorflow教程请参考Tensorflow中文社区http://www.tensorfly.cn/以及莫烦老师一系列的Tensorflow教程http://list.youku.com/albumlist/show?id=27327189&asce，谢谢大家！