TensorFlow良心入门教程

All the matrials come from Machine Learning class in Polyu,HK and I reorganize them and add reference materials.I promise that I only use them to study and non-proft

.ipynb源文件可通过我的onedrive下载：https://1drv.ms/u/s!Al86h1dThXMNxF-J7FKHKTPkf5yr?e=SAgALh

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Warm up

A short example for Tensorflow:

import tensorflow as tf

node1=tf.placeholder(tf.float32)
node2=tf.placeholder(tf.float32)
node3=tf.add(node1,node2)
tf.Session().run(node3,{node1:4,node2:4})

8.0

Basic ideas

It's okay if you don't understand the content well in this section.Section 3rd will be much more detailed and basic

Core TensorFlow constructs

• Dataflow Graphs: entire computation
• Data Nodes: individual data or operations
• Edges: implicit dependencies between nodes
• Operations: any computation
• Constants: single values (tensors)

Choose the place to run code

The whole point of having a dataflow representation is flexibility in choosing location. Tensorflow lets you choose the device to run the code:

Computational graph

A TensorFlow Core programs contains two sections:

Building the computational graph.

Running the computational graph.

So what is a computational graph?

A computational graph is a series of TensorFlow operations arranged into a graph of nodes.

Node

Each node takes zero or more tensors as inputs and produces a tensor as an output.The type of node could be constant,variable,operations and so on.

All nodes return tensors, or higher-dimensional matrices

Tensor

A tensor consists of a set of primitive(原始) values shaped into an array of any number of dimensions.

Variable

To make the model trainable, we need to be able to modify the graph to get new outputs with the same input.So we should use variable:

Variables allow us to add trainable parameters to a graph.They are constructed with a type and initial value.

Placeholder

A placeholder is a promise to provide a value later.

Initializer

To initialize all the variables in a TensorFlow program, tf.global_variables_initializer() is needed.

Model Evaluation and Training

To evaluate the model on training data: loss function

TensorFlow provides optimizers that slowly change each variable in order to minimize the loss function. (In the following example we use gradient descent.)

Session(会话)

To actually evaluate the nodes, we must run the computational graph within a session.

A session encapsulates(封装) the control and state of the TensorFlow runtime.

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A linear Rgression Example:

import numpy as np
# Model parameters
W = tf.Variable([.3], dtype=tf.float32)
b = tf.Variable([-.3], dtype=tf.float32)
#Model input and output
x = tf.placeholder(tf.float32)
linear_model = W * x + b #Operator Overloading!
y=tf.placeholder(tf.float32)
#loss
loss=tf.reduce_sum(tf.square(linear_model-y))
#optimizer
optimizer=tf.train.GradientDescentOptimizer(0.01)
train=optimizer.minimize(loss)
#training data
X_train=[1,2,3,4]
y_train=[0,-1,-2,-3]
#training loop
init = tf.global_variables_initializer()
with tf.Session() as sess:
    sess.run(init)
    for i in range(1000):
        sess.run(train,{x:X_train,y:y_train})
    #evaluate training accuracy
    curr_W,curr_b,curr_loss=sess.run([W,b,loss],{x:X_train,y:y_train})
print("W:%s b:%s loss:%s"%(curr_W,curr_b,curr_loss))

W:[-0.9999969] b:[0.9999908] loss:5.6999738e-11

Suggested System Design

Parameter server

it focus:

• Hold Mutable state
• Apply updates
• Maintain availability
• Group Name: ps

Worker

it focus:

• Perform “active” actions
• Checkpoint state to FS
• Mostly stateless; can be restarted
• Group name: worker

The implemnetation of TensorFlow

Distributed Master: compiles the graph, including specialization. Think of it kind of like a query optimizer, but honestly it’s really an impoverished compiler Dataflow executor: the scheduler and coordinator, responsible for invoking kernels on various devices.

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Detailed understanding

Introducing Tensors

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The term "tensor" in ML,especially tensorflow, has no relation with the term "tensor(called 张量 in Chinese" in physics!

I attach a link which introduces the tensor used in physics and mathematics.After reading it you will find out that maybe google misuse the "tensor"

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To understand tensors well, it’s good to have some working knowledge of linear algebra and vector calculus. You already read in the introduction that tensors are implemented in TensorFlow as multidimensional data arrays, but some more introduction is maybe needed in order to completely grasp tensors and their use in machine learning.

Plane Vectors(平面向量)

Please be aware that the content in this section is very very simple

Before you go into plane vectors, it’s a good idea to shortly revise the concept of “vectors”; Vectors are special types of matrices, which are rectangular arrays of numbers. Because vectors are ordered collections of numbers, they are often seen as column matrices(列向量): they have just one column and a certain number of rows. In other terms, you could also consider vectors as scalar(标量) magnitudes that have been given a direction.

Remember: an example of a scalar is “5 meters” or “60 m/sec”, while a vector is, for example, “5 meters north” or “60 m/sec East”. The difference between these two is obviously that the vector has a direction. Nevertheless, these examples that you have seen up until now might seem far off from the vectors that you might encounter when you’re working with machine learning problems. This is normal; The length of a mathematical vector is a pure number: it is absolute. The direction, on the other hand, is relative: it is measured relative to some reference direction and has units of radians or degrees. You usually assume that the direction is positive and in counterclockwise rotation from the reference direction.

Visually, of course, you represent vectors as arrows, as you can see in the picture above. This means that you can consider vectors also as arrows that have direction and length. The direction is indicated by the arrow’s head, while the length is indicated by the length of the arrow.

So what about plane vectors then?

Plane vectors are the most straightforward setup of tensors. They are much like regular vectors as you have seen above, with the sole difference that they find themselves in a vector space. To understand this better, let’s start with an example: you have a vector that is 2 X 1. This means that the vector belongs to the set of real numbers that come paired two at a time. Or, stated differently, they are part of two-space. In such cases, you can represent vectors on the coordinate (x,y) plane with arrows or rays.

Working from this coordinate(坐标) plane in a standard position where vectors have their endpoint at the origin (0,0), you can derive the x coordinate by looking at the first row of the vector, while you’ll find the y coordinate in the second row. Of course, this standard position doesn’t always need to be maintained: vectors can move parallel to themselves in the plane without experiencing changes.

Note that similarly, for vectors that are of size 3 X 1, you talk about the three-space. You can represent the vector as a three-dimensional figure with arrows pointing to positions in the vectors pace: they are drawn on the standard x, y and z axes.

It’s nice to have these vectors and to represent them on the coordinate plane, but in essence(本质), you have these vectors so that you can perform operations on them and one thing that can help you in doing this is by expressing your vectors as bases or unit vectors(单位向量).

Unit vectors are vectors with a magnitude(大小) of one. You’ll often recognize the unit vector by a lowercase letter with a circumflex, or “hat”. Unit vectors will come in convenient if you want to express a 2-D or 3-D vector as a sum of two or three orthogonal components, such as the x− and y−axes, or the z−axis.

And when you are talking about expressing one vector, for example, as sums of components, you’ll see that you’re talking about component vectors, which are two or more vectors whose sum is that given vector.

(now the very very easy part ends)

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Tensors

Next to plane vectors, also covectors and linear operators are two other cases that all three together have one thing in common: they are specific cases of tensors. You still remember how a vector was characterized in the previous section as scalar magnitudes that have been given a direction. A tensor, then, is the mathematical representation of a physical entity that may be characterized by magnitude and multiple directions.

And, just like you represent a scalar with a single number and a vector with a sequence of three numbers in a 3-dimensional space, for example, a tensor can be represented by an array of 3^R numbers in a 3-dimensional space.

The “R” in this notation represents the rank of the tensor: this means that in a 3-dimensional space, a second-rank tensor can be represented by 3 to the power of 2 or 9 numbers. In an N-dimensional space, scalars will still require only one number, while vectors will require N numbers, and tensors will require N^R numbers. This explains why you often hear that scalars are tensors of rank 0: since they have no direction, you can represent them with one number.

With this in mind, it’s relatively easy to recognize scalars, vectors, and tensors and to set them apart: scalars can be represented by a single number, vectors by an ordered set of numbers, and tensors by an array of numbers.

What makes tensors so unique is the combination of components and basis vectors(向量基): basis vectors transform one way between reference frames and the components transform in just such a way as to keep the combination between components and basis vectors the same.

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This article introduce the "sensor" in physics and mathematics:

source:https://www.jianshu.com/p/2a0f7f7735ad

And this picture summary what is sensor in tensorflow:

So the sensor in ML actually is just a kind of Multidimensional Arrays

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Getting Started With TensorFlow: Basics

You’ll generally write TensorFlow programs, which you run as a chunk; This is at first sight kind of contradictory when you’re working with Python. However, if you would like, you can also use TensorFlow’s Interactive Session, which you can use to work more interactively with the library. This is especially handy when you’re used to working with IPython.

For this tutorial, you’ll focus on the second option: this will help you to get kickstarted with deep learning in TensorFlow. But before you go any further into this, let’s first try out some minor stuff before you start with the heavy lifting.

First, import the tensorflow library under the alias(别名) tf, as you have seen in the previous section. Then initialize two variables that are actually constants. Pass an array of four numbers to the constant() function.

Note that you could potentially also pass in an integer, but that more often than not, you’ll find yourself working with arrays. As you saw in the introduction, tensors are all about arrays! So make sure that you pass in an array