CS20SI Tensorflow for Deeplearning課程筆記(一)

一、 課程目標

1. 理解TF的計算圖方法
2. 探索TF的內置函數
3. 學會怎麼去構建和組織最好的模型去做深度學習項目

二、書籍

1. TensorFlow for Machine Intelligence (TFFMI)
2. Hands-On Machine Learning with Scikit-Learn and TensorFlow. Chapter 9
   Up and running with TensorFlow
3. Fundamentals of Deep Learning. Chapter 3: Implementing Neural Networks in TensorFlow (FODL)
   書籍容易過時，推薦tensorflow官網

三、 簡化版的Tensorflow

1. TF Learn (tf.contrib.learn)
2. TF Slim (tf.contrib.slim):
3. 高級版 API: Keras, TFLearn, Pretty Tensor

四、Graphs and Sessions

1. tensor

一種n維的矩陣
0-d tensor: scalar (number)
1-d tensor: vector
2-d tensor: matrix
and so on

2. Data Flow Graphs

計算a的值的方式

import tensorflow as tf
a = tf.add(3, 5)
# with clause takes care
# of sess.close()
with tf.Session() as sess:
    print sess.run(a)

數據流圖如下所示
 

3. tf.Session()

主要用於執行需要計算的tensor。

x = 2
y = 3
op1 = tf.add(x, y)
op2 = tf.mul(x, y)
op3 = tf.pow(op2, op1)
with tf.Session() as sess:
    op3 = sess.run(op3)

可以將圖分成幾部分，然後並行地計算在CPU,GPU上，如下圖所示
 

4. tf.Graph()

添加一個運算給圖，並將其設爲默認圖

g = tf.Graph()
with g.as_default():
    a = 3
    b = 5
    x = tf.add(a, b)
    sess = tf.Session(graph=g) # session is run on the graph g
# run session
    sess.close()

獲取默認的圖

g = tf.get_default_graph()

不要將默認的圖和用戶創建的圖混合在一起，如下情況會報錯

g = tf.Graph()
# add ops to the default graph
a = tf.constant(3)
# add ops to the user created graph
with g.as_default():
    b = tf.constant(5)
#Prone to errors

正確的使用方式

g1 = tf.get_default_graph()
g2 = tf.Graph()
# add ops to the default graph
with g1.as_default():
    a = tf.Constant(3)
# add ops to the user created graph
    with g2.as_default():
b = tf.Constant(5)

爲什麼使用Graph？
1. Save computation (only run subgraphs that lead to the values you want to fetch)
2. Break computation into small, differential pieces to facilitates auto-differentiation
3. Facilitate distributed computation, spread the work across multiple CPUs, GPUs, or devices
4. Many common machine learning models are commonly taught and visualized as directed graphs already