[Tensorflow] Cookbook - The Tensorflow Way

本章介绍tf基础知识，主要包括cookbook的第一、二章节。

方针：先会用，后定制

Ref: TensorFlow 如何入门？

Ref: 如何高效的学习 TensorFlow 代码?

 

顺便推荐该领域三件装备：

 

 

How TensorFlow Works?

Steps

  1. Import or generate datasets
  2. Transform and normalize data
  3. Partition datasets into train, test, and validation sets
  4. Set algorithm parameters (hyperparameters)
  5. Initialize variables and placeholders
  6. Define the model structure
  7. Declare the loss functions
  8. Initialize and train the model
  9. Evaluate the model
  10. Tune hyperparameters
  11. Deploy/predict new outcomes

 

Declaring Variables and Tensors

Tensor 张量 【相当于常数的设置】

import tensorflow as tf

# Fixed tensors 四种创建
zero_tsr = tf.zeros([2, 3])
zero_tsr

ones_tsr = tf.ones([2, 3])
ones_tsr

filled_tsr = tf.fill([2, 3], 42)
filled_tsr

constant_tsr = tf.constant([1,2,3])
constant_tsr

# Tensors of similar shape 拷贝创建
zeros_similar = tf.zeros_like(constant_tsr)
zeros_similar

ones_similar = tf.ones_like(constant_tsr)
ones_similar

# 对比，变量的拷贝式创建
w2 = tf.Variable(w1.initialized_value())　　

# Sequence tensors 线性创建
linear_tsr = tf.linspace(start=0.0, stop=1.0, num=3)
linear_tsr

integer_seq_tsr = tf.range(start=6, limit=15, delta=3)
integer_seq_tsr

# Random tensors
randunif_tsr = tf.random_uniform([2, 3], minval=0, maxval=1)
randunif_tsr

randnorm_tsr = tf.random_normal([2, 3], mean=0.0, stddev=1.0)
randnorm_tsr

runcnorm_tsr = tf.truncated_normal([2, 3], mean=0.0, stddev=1.0)
runcnorm_tsr

# 拷贝创建
input_tensor = runcnorm_tsr
shuffled_output = tf.random_shuffle(input_tensor)
shuffled_output

crop_size = 10
cropped_output = tf.random_crop(input_tensor, crop_size)
cropped_output

#cropped_image = tf.random_crop(my_image, [height/2, width/2, 3])

my_var = tf.Variable(tf.zeros([2, 3]))
my_var

#We can convert any numpy array to a Python list, or
#constant to a tensor using the function convert_to_tensor(). 
#convert_to_tensor()

打印/显示 tensor 的内容：通过sess将tensor动（运算）起来，形成流，之后便能打印出内容。

# How to print detail in tensor
# <Sol 1>
sess = tf.Session()
print(sess.run(zero_tsr))
close(sess)
# <Sol 2> 推荐!
with tf.Session():
    print(zero_tsr.eval())

 

Computational graph and Placeholder 

import tensorflow as tf

#定义‘符号’变量，也称为占位符
a = tf.placeholder("float")
b = tf.placeholder("float")

y = tf.mul(a, b) #构造一个op节点

sess = tf.Session()#建立会话

#运行会话，输入数据，并计算节点，同时打印结果
print(sess.run(y, feed_dict={a:3, b:3}))

# 任务完成, 关闭会话.
sess.close()

 

通俗易懂：http://blog.csdn.net/fei13971414170/article/details/73309106

在 TensorFlow 中，数据不是以整数，浮点数或者字符串形式存在的，而是被封装在一个叫做 tensor 的对象中。【tensor是个Object】

Tensor是张量的意思，张量包含了0到任意维度的量，其中：

    • 零维的叫作常数；
    • 一维的叫作向量；
    • 二维的叫作矩阵；
    • 多维度的就直接叫作张量。
# tensor1 是一个零维的 int32 tensor
tensor1 = tf.constant(1234) 

# tensor2 是一个一维的 int32 tensor
tensor2 = tf.constant([123,456,789]) 

 # tensor3 是一个二维的 int32 tensor
tensor3 = tf.constant([ [123,456,789], [222,333,444] ])

 

Placeholder占位符

使用feed_dict设置tensor的时候，需要你给出的值类型与占位符定义的类型相同。

x = tf.placeholder(tf.string)
y = tf.placeholder(tf.int32)
z = tf.placeholder(tf.float32)

with tf.Session() as sess:
    output = sess.run(x, feed_dict={x: 'Test String', y: 123, z: 45.67})

 

Using Placeholders and Variables

What's the difference between tf.placeholder and tf.Variable

In short, you use tf.Variable for trainable variables such as weights (W) and biases (B) for your model. 【权重和偏移，要训练的数据】

weights = tf.Variable(tf.truncated_normal([IMAGE_PIXELS, hidden1_units], stddev=1.0 / math.sqrt(float(IMAGE_PIXELS))), name='weights')
biases  = tf.Variable(tf.zeros([hidden1_units]), name='biases')

tf.placeholder is used to feed actual training examples. 【用于得到传递进来的真实的训练样本】

images_placeholder = tf.placeholder(tf.float32, shape=(batch_size, IMAGE_PIXELS))
labels_placeholder = tf.placeholder(tf.int32,   shape=(batch_size))

This is how you feed the training examples during the training:

for step in xrange(FLAGS.max_steps):
    feed_dict = {　　# 这么写，看上去友好些
       images_placeholder: images_feed,
       labels_placeholder: labels_feed,
     }
    _, loss_value = sess.run([train_op, loss], feed_dict=feed_dict)

Your tf.variables will be trained (modified) as the result of this training.

 

The difference is that with tf.Variable you have to provide an initial value when you declare it.

With tf.placeholder you don't have to provide an initial value and you can specify it at run time with the feed_dict argument inside Session.run .

 

Working with Matrices 

矩阵计算

# Matrices and Matrix Operations
#----------------------------------
#
# This function introduces various ways to create
# matrices and how to use them in Tensorflow

import numpy as np
import tensorflow as tf
from tensorflow.python.framework import ops
ops.reset_default_graph()

# Declaring matrices
sess = tf.Session()

# Declaring matrices

# Identity matrix
identity_matrix = tf.diag([1.0,1.0,1.0])
print(sess.run(identity_matrix))
#[[ 1.  0.  0.]
# [ 0.  1.  0.]
# [ 0.  0.  1.]]

# 2x3 random norm matrix
A = tf.truncated_normal([2,3])
print(sess.run(A))
#[[  4.79249517e-04  -6.00280046e-01   1.36713833e-01]
# [ -1.25442386e+00   8.82814229e-02  -2.52978474e-01]]

# 2x3 constant matrix
B = tf.fill([2,3], 5.0)
print(sess.run(B))
#[[ 5.  5.  5.]
# [ 5.  5.  5.]]

# 3x2 random uniform matrix
C = tf.random_uniform([3,2])
print(sess.run(C))
#[[ 0.07532465  0.23328125]
# [ 0.21761775  0.35856724]
# [ 0.88200712  0.27035964]]

print(sess.run(C)) # Note that we are reinitializing, hence the new random variabels

# Create matrix from np array [python lib]
D = tf.convert_to_tensor(np.array([[1., 2., 3.], [-3., -7., -1.], [0., 5., -2.]]))
print(sess.run(D))
#[[ 1.  2.  3.]
# [-3. -7. -1.]
# [ 0.  5. -2.]]

# Matrix addition/subtraction
print(sess.run(A+B))
#[[ 6.08604956  4.14716625  4.46166086]
# [ 3.24823093  4.94008398  5.14025211]]
 
print(sess.run(B-B))
#[[ 0.  0.  0.]
# [ 0.  0.  0.]]

# Matrix Multiplication
print(sess.run(tf.matmul(B, identity_matrix)))
#[[ 5.  5.  5.]
# [ 5.  5.  5.]]

# Matrix Transpose 因为C是随即变量，故sess一次，就会重新随机，故，这里选择D做实验会好些
print(sess.run(tf.transpose(C))) # Again, new random variables

# Matrix Determinant 方阵的行列式
print(sess.run(tf.matrix_determinant(D)))

# Matrix Inverse
print(sess.run(tf.matrix_inverse(D)))

# Cholesky Decomposition　-->
print(sess.run(tf.cholesky(identity_matrix)))

# Eigenvalues and Eigenvectors 特征值 and 特征向量
print(sess.run(tf.self_adjoint_eig(D)))
#( array([-10.65907521, -0.22750691, 2.88658212]), 
#  array([[ 0.21749542, 0.63250104, -0.74339638],
#         [ 0.84526515, 0.2587998 ,  0.46749277],
#         [-0.4880805 , 0.73004459,  0.47834331]]) )

补充：Cholesky Decomposition

将一个正定Hermite矩阵分解成为一个下三角阵和它的共轭转置阵的乘积。
如果矩阵A是正定Hermite阵，那么矩阵A可以做如下分解：

\mathbf{A} = \mathbf{L} \mathbf{L}^{*},

其中L是一个下三角矩阵且主对角线元素严格正定，L*是L的共轭转置矩阵。

 

Declaring Operations

矩阵计算

# Operations
#----------------------------------
#
# This function introduces various operations
# in Tensorflow

# Declaring Operations
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from tensorflow.python.framework import ops
ops.reset_default_graph()

# Open graph session
sess = tf.Session()

# div() vs truediv() vs floordiv()
print(sess.run(tf.div(3,4)))
#0
print(sess.run(tf.truediv(3,4)))
#0.75
print(sess.run(tf.floordiv(3.0,4.0)))
#0.0

# Mod function
print(sess.run(tf.mod(22.0,5.0)))
#2.0

# Cross Product
print(sess.run(tf.cross([1.,0.,0.],[0.,1.,0.])))
#[ 0.  0.  1.]



# Trig functions
print(sess.run(tf.sin(3.1416)))
#-7.23998e-06

print(sess.run(tf.cos(3.1416)))
#-1.0

# Tangemt
print(sess.run(tf.div(tf.sin(3.1416/4.), tf.cos(3.1416/4.))))
#1.0

# Custom operation
test_nums = range(15)
#from tensorflow.python.ops import math_ops
#print(sess.run(tf.equal(test_num, 3)))
def custom_polynomial(x_val):
    # Return 3x^2 - x + 10
    return(tf.sub(3 * tf.square(x_val), x_val) + 10)

print(sess.run(custom_polynomial(11)))

# What should we get with list comprehension
expected_output = [3*x*x-x+10 for x in test_nums]
print(expected_output)

# Tensorflow custom function output
for num in test_nums:
    print(sess.run(custom_polynomial(num)))

这为之后激活函数的实现打下了基础。

 

Implementing Activation Functions

# Activation Functions
#----------------------------------
#
# This function introduces activation
# functions in Tensorflow

# Implementing Activation Functions
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from tensorflow.python.framework import ops
ops.reset_default_graph()

# Open graph session
sess = tf.Session()

# X range
x_vals = np.linspace(start=-10., stop=10., num=100)
x_vals

# ReLU activation
print(sess.run(tf.nn.relu([-3., 3., 10.])))
y_relu = sess.run(tf.nn.relu(x_vals))
#[  0.   3.  10.]

# ReLU-6 activation
# This is defined as min(max(0,x),6). 
# This will come in handy when we
# discuss deeper neural networks in Chapters 8, Convolutional Neural Networks and
# Chapter 9, Recurrent Neural Networks.
print(sess.run(tf.nn.relu6([-3., 3., 10.])))
y_relu6 = sess.run(tf.nn.relu6(x_vals))

# Sigmoid activation
print(sess.run(tf.nn.sigmoid([-1., 0., 1.])))
y_sigmoid = sess.run(tf.nn.sigmoid(x_vals))

# Hyper Tangent activation
print(sess.run(tf.nn.tanh([-1., 0., 1.])))
y_tanh = sess.run(tf.nn.tanh(x_vals))

# Softsign activation
print(sess.run(tf.nn.softsign([-1., 0., 1.])))
y_softsign = sess.run(tf.nn.softsign(x_vals))

# Softplus activation
print(sess.run(tf.nn.softplus([-1., 0., 1.])))
y_softplus = sess.run(tf.nn.softplus(x_vals))

# Exponential linear activation
print(sess.run(tf.nn.elu([-1., 0., 1.])))
y_elu = sess.run(tf.nn.elu(x_vals))

# Plot the different functions
plt.plot(x_vals, y_softplus, 'r--', label='Softplus', linewidth=2)
plt.plot(x_vals, y_relu,     'b:',  label='ReLU',     linewidth=2)
plt.plot(x_vals, y_relu6,    'g-.', label='ReLU6',    linewidth=2)
plt.plot(x_vals, y_elu,      'k-',  label='ExpLU',    linewidth=0.5)
plt.ylim([-1.5,7])
plt.legend(loc='top left')
plt.show()

plt.plot(x_vals, y_sigmoid,  'r--', label='Sigmoid',  linewidth=2)
plt.plot(x_vals, y_tanh,     'b:',  label='Tanh',     linewidth=2)
plt.plot(x_vals, y_softsign, 'g-.', label='Softsign', linewidth=2)
plt.ylim([-2,2])
plt.legend(loc='top left')
plt.show()

Result:  

 

 

Working with Data Sources

# Data gathering
#----------------------------------
#
# This function gives us the ways to access
# the various data sets we will need

# Data Gathering
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from tensorflow.python.framework import ops
ops.reset_default_graph()


# Iris Data
from sklearn import datasets

iris = datasets.load_iris()
print(len(iris.data))
print(len(iris.target))
print(iris.data[0])
print(set(iris.target))

# Low Birthrate Data
import requests

birthdata_url = 'https://www.umass.edu/statdata/statdata/data/lowbwt.dat'
birth_file = requests.get(birthdata_url)
birth_data = birth_file.text.split('\r\n')[5:]
birth_header = [x for x in birth_data[0].split(' ') if len(x)>=1]
birth_data = [[float(x) for x in y.split(' ') if len(x)>=1] for y in birth_data[1:] if len(y)>=1]
print(len(birth_data))
print(len(birth_data[0]))


# Housing Price Data
import requests

housing_url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/housing/housing.data'
housing_header = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT', 'MEDV']
housing_file = requests.get(housing_url)
housing_data = [[float(x) for x in y.split(' ') if len(x)>=1] for y in housing_file.text.split('\n') if len(y)>=1]
print(len(housing_data))
print(len(housing_data[0]))


# MNIST Handwriting Data
from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)　　# 本地加载
print(len(mnist.train.images))
print(len(mnist.test.images))
print(len(mnist.validation.images))
print(mnist.train.labels[1,:])


# Ham/Spam Text Data
import requests
import io
from zipfile import ZipFile

# Get/read zip file
zip_url = 'http://archive.ics.uci.edu/ml/machine-learning-databases/00228/smsspamcollection.zip'
r = requests.get(zip_url)
z = ZipFile(io.BytesIO(r.content))
file = z.read('SMSSpamCollection')
# Format Data
text_data = file.decode()
text_data = text_data.encode('ascii',errors='ignore')
text_data = text_data.decode().split('\n')
text_data = [x.split('\t') for x in text_data if len(x)>=1]
[text_data_target, text_data_train] = [list(x) for x in zip(*text_data)]
print(len(text_data_train))
print(set(text_data_target))
print(text_data_train[1])


# Movie Review Data
import requests
import io
import tarfile

movie_data_url = 'http://www.cs.cornell.edu/people/pabo/movie-review-data/rt-polaritydata.tar.gz'
r = requests.get(movie_data_url)
# Stream data into temp object
stream_data = io.BytesIO(r.content)
tmp = io.BytesIO()
while True:
    s = stream_data.read(16384)
    if not s:  
        break
    tmp.write(s)
stream_data.close()
tmp.seek(0)
# Extract tar file
tar_file = tarfile.open(fileobj=tmp, mode="r:gz")
pos = tar_file.extractfile('rt-polaritydata/rt-polarity.pos')
neg = tar_file.extractfile('rt-polaritydata/rt-polarity.neg')
# Save pos/neg reviews
pos_data = []
for line in pos:
    pos_data.append(line.decode('ISO-8859-1').encode('ascii',errors='ignore').decode())
neg_data = []
for line in neg:
    neg_data.append(line.decode('ISO-8859-1').encode('ascii',errors='ignore').decode())
tar_file.close()

print(len(pos_data))
print(len(neg_data))
print(neg_data[0])


# The Works of Shakespeare Data
import requests

shakespeare_url = 'http://www.gutenberg.org/cache/epub/100/pg100.txt'
# Get Shakespeare text
response = requests.get(shakespeare_url)
shakespeare_file = response.content
# Decode binary into string
shakespeare_text = shakespeare_file.decode('utf-8')
# Drop first few descriptive paragraphs.
shakespeare_text = shakespeare_text[7675:]
print(len(shakespeare_text))


# English-German Sentence Translation Data
import requests
import io
from zipfile import ZipFile
sentence_url = 'http://www.manythings.org/anki/deu-eng.zip'
r = requests.get(sentence_url)
z = ZipFile(io.BytesIO(r.content))
file = z.read('deu.txt')
# Format Data
eng_ger_data = file.decode()
eng_ger_data = eng_ger_data.encode('ascii',errors='ignore')
eng_ger_data = eng_ger_data.decode().split('\n')
eng_ger_data = [x.split('\t') for x in eng_ger_data if len(x)>=1]
[english_sentence, german_sentence] = [list(x) for x in zip(*eng_ger_data)]
print(len(english_sentence))
print(len(german_sentence))
print(eng_ger_data[10])

 

  

 

Outlines

  • Operations in a Computational Graph
  • Layering Nested Operations
  • Working with Multiple Layers
  • Implementing Loss Functions
  • Implementing Back Propagation
  • Working with Batch and Stochastic Training
  • Combining Everything Together
  • Evaluating Models

这里通过简单的线性回归来做一些基本的算法练习。

 

基本的"计算图"构建

Operations in a Computational Graph

# Operations on a Computational Graph
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from tensorflow.python.framework import ops
ops.reset_default_graph()

# Create graph
sess = tf.Session()

# Create tensors

# Create data to feed in
x_vals = np.array([1., 3., 5., 7., 9.])

# 计算图的构造
x_data = tf.placeholder(tf.float32)　　# 变量：输入不能确定
m      = tf.constant(3.)　　           # 常量
prod   = tf.mul(x_data, m)　　         # Operation

# 喂数据
for x_val in x_vals:
    print(sess.run(prod, feed_dict={x_data: x_val}))　　# 往 x_data（变量）里面喂x_val（输入数据）

merged = tf.merge_all_summaries()
my_writer = tf.train.SummaryWriter('/home/unsw/Programmer/1-python/Tensorflow/TF/TensreFlowMachineLearningCookbook_Code/myTest', sess.graph)

所有可用的 summary 操作详细信息，可以查看summary_operation文档。

在TensorFlow中，所有的操作只有当你执行，或者另一个操作依赖于它的输出时才会运行。我们刚才创建的这些节点（summary nodes）都围绕着你的图像：没有任何操作依赖于它们的结果。因此，为了生成汇总信息，我们需要运行所有这些节点。这样的手动工作是很乏味的，因此可以使用tf.merge_all_summaries来将他们合并为一个操作。

然后你可以执行合并命令，它会依据特点步骤将所有数据生成一个序列化的Summary protobuf对象。最后，为了将汇总数据写入磁盘，需要将汇总的protobuf对象传递给tf.train.Summarywriter。

SummaryWriter 的构造函数中包含了参数 logdir。这个 logdir 非常重要，所有事件都会写到它所指的目录下。此外，SummaryWriter 中还包含了一个可选择的参数 GraphDef。如果输入了该参数，那么 TensorBoard 也会显示你的图像。

 

Layering Nested Operations

# Layering Nested Operations

import numpy as np
import tensorflow as tf
from tensorflow.python.framework import ops
ops.reset_default_graph()

# Create graph
sess = tf.Session()

# Create tensors

# Create data to feed in
my_array = np.array([[1., 3., 5., 7., 9.],
                    [-2., 0., 2., 4., 6.],
                    [-6., -3., 0., 3., 6.]])

x_vals = np.array([my_array, my_array + 1])
x_data = tf.placeholder(tf.float32, shape=(3, 5))

m1 = tf.constant([[1.],[0.],[-1.],[2.],[4.]])
sess.run(m1)
m2 = tf.constant([[2.]])
sess.run(m2)
a1 = tf.constant([[10.]])
sess.run(a1)

# 1st Operation Layer = Multiplication
prod1 = tf.matmul(x_data, m1)

# 2nd Operation Layer = Multiplication
prod2 = tf.matmul(prod1, m2)

# 3rd Operation Layer = Addition
add1 = tf.add(prod2, a1)

for x_val in x_vals:
    print(sess.run(add1, feed_dict={x_data: x_val}))

merged = tf.merge_all_summaries()
my_writer = tf.train.SummaryWriter('/home/nick/OneDrive/Documents/tensor_flow_book/Code/2_Tensorflow_Way', sess.graph)

 

Working with Multiple Layers

Figure, 简单的卷积网络

 

# Layering Nested Operations
import numpy as np
import tensorflow as tf
from tensorflow.python.framework import ops
ops.reset_default_graph()

# Create graph
sess = tf.Session()

# Create tensors

# Create a small random 'image' of size 4x4
x_shape = [1, 4, 4, 1]
x_val   = np.random.uniform(size=x_shape)
x_data  = tf.placeholder(tf.float32, shape=x_shape)
# 【x_data <--feed-- x_val】

# 卷积
my_filter    = tf.constant(0.25, shape=[2, 2, 1, 1])
my_strides   = [1, 2, 2, 1]
mov_avg_layer= tf.nn.conv2d(x_data, my_filter, my_strides, padding='SAME', name='Moving_Avg_Window')

# Define a custom layer which will be sigmoid(Ax+b) where
# x is a 2x2 matrix and A and b are 2x2 matrices
def custom_layer(input_matrix):
    input_matrix_sqeezed = tf.squeeze(input_matrix)
    A     = tf.constant([[1., 2.], [-1., 3.]])    # Const
    b     = tf.constant(1., shape=[2, 2])         # Const_1
    # 以上：节点输入；以下，节点计算
    temp1 = tf.matmul(A, input_matrix_sqeezed)    # [MatMul]
    temp  = tf.add(temp1, b) # Ax + b             # [Add]
    return(tf.sigmoid(temp))                      # [Sigmoid]

# Add custom layer to graph
with tf.name_scope('Custom_Layer') as scope:
    custom_layer1 = custom_layer(mov_avg_layer)   # <-- 卷积的结果给于自定义层来处理

# The output should be an array that is 2x2, but size (1,2,2,1)
#print(sess.run(mov_avg_layer, feed_dict={x_data: x_val}))

# After custom operation, size is now 2x2 (squeezed out size 1 dims)
print(sess.run(custom_layer1, feed_dict={x_data: x_val}))

merged = tf.merge_all_summaries()
my_writer = tf.train.SummaryWriter('/home/unsw/Programmer/1-python/Tensorflow/TF/TensreFlowMachineLearningCookbook_Code/myTest', sess.graph)

tf.squeeze()

给定张量输入，此操作返回相同类型的张量，并删除所有尺寸为1的尺寸。

如果不想删除所有大小是1的维度，可以通过squeeze_dims指定。

# 't' is a tensor of shape [1, 2, 1, 3, 1, 1]
shape(squeeze(t))  # => [2, 3]

# 't' is a tensor of shape [1, 2, 1, 3, 1, 1]
shape(squeeze(t, [2, 4]))  # => [1, 2, 3, 1]　　指定了删除的‘1’.

 

  

损失函数

Implementing Loss Functions 

看样子就是scikit一套的接口【之前的激活函数和这里的损失函数需单独总结数学原理】

# Loss Functions
#----------------------------------
#
#  This python script illustrates the different
#  loss functions for regression and classification.

import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.python.framework import ops
ops.reset_default_graph()

# Create graph
sess = tf.Session()

###### Numerical Predictions ######
x_vals = tf.linspace(-1., 1., 500)
target = tf.constant(0.)

# L2 loss
# L = (pred - actual)^2
l2_y_vals = tf.square(target - x_vals)
l2_y_out  = sess.run(l2_y_vals)

# L1 loss
# L = abs(pred - actual)
l1_y_vals = tf.abs(target - x_vals)
l1_y_out  = sess.run(l1_y_vals)

# Pseudo-Huber loss
# L = delta^2 * (sqrt(1 + ((pred - actual)/delta)^2) - 1)
delta1 = tf.constant(0.25)
phuber1_y_vals = tf.mul(tf.square(delta1), tf.sqrt(1. + tf.square((target - x_vals)/delta1)) - 1.)
phuber1_y_out  = sess.run(phuber1_y_vals)

delta2 = tf.constant(5.)
phuber2_y_vals = tf.mul(tf.square(delta2), tf.sqrt(1. + tf.square((target - x_vals)/delta2)) - 1.)
phuber2_y_out = sess.run(phuber2_y_vals)

# Plot the output:
x_array = sess.run(x_vals)　　# <-- 横轴代表x_vals的取值
plt.plot(x_array, l2_y_out,      'b-',  label='L2 Loss')
plt.plot(x_array, l1_y_out,      'r--', label='L1 Loss')
plt.plot(x_array, phuber1_y_out, 'k-.', label='P-Huber Loss (0.25)')
plt.plot(x_array, phuber2_y_out, 'g:',  label='P-Huber Loss (5.0)')
plt.ylim(-0.2, 0.4)
plt.legend(loc='lower right', prop={'size': 11})
plt.show()

###############################################################################################

###### Categorical Predictions ######
x_vals  = tf.linspace(-3., 5., 500)
target  = tf.constant(1.)
targets = tf.fill([500,], 1.)

# Hinge loss
# Use for predicting binary (-1, 1) classes
# L = max(0, 1 - (pred * actual))
hinge_y_vals = tf.maximum(0., 1. - tf.mul(target, x_vals))
hinge_y_out  = sess.run(hinge_y_vals)

# Cross entropy loss
# L = -actual * (log(pred)) - (1-actual)(log(1-pred))
xentropy_y_vals = - tf.mul(target, tf.log(x_vals)) - tf.mul((1. - target), tf.log(1. - x_vals))
xentropy_y_out  = sess.run(xentropy_y_vals)

# Sigmoid entropy loss
# L = -actual * (log(sigmoid(pred))) - (1-actual)(log(1-sigmoid(pred)))
# or
# L = max(actual, 0) - actual * pred + log(1 + exp(-abs(actual)))
xentropy_sigmoid_y_vals = tf.nn.sigmoid_cross_entropy_with_logits(x_vals, targets)
xentropy_sigmoid_y_out  = sess.run(xentropy_sigmoid_y_vals)

# Weighted (softmax) cross entropy loss
# L = -actual * (log(pred)) * weights - (1-actual)(log(1-pred))
# or
# L = (1 - pred) * actual + (1 + (weights - 1) * pred) * log(1 + exp(-actual))
weight = tf.constant(0.5)
xentropy_weighted_y_vals = tf.nn.weighted_cross_entropy_with_logits(x_vals, targets, weight)
xentropy_weighted_y_out = sess.run(xentropy_weighted_y_vals)

# Plot the output
x_array = sess.run(x_vals)
plt.plot(x_array, hinge_y_out, 'b-', label='Hinge Loss')
plt.plot(x_array, xentropy_y_out, 'r--', label='Cross Entropy Loss')
plt.plot(x_array, xentropy_sigmoid_y_out, 'k-.', label='Cross Entropy Sigmoid Loss')
plt.plot(x_array, xentropy_weighted_y_out, 'g:', label='Weighted Cross Entropy Loss (x0.5)')
plt.ylim(-1.5, 3)
#plt.xlim(-1, 3)
plt.legend(loc='lower right', prop={'size': 11})
plt.show()

# Softmax entropy loss
# L = -actual * (log(softmax(pred))) - (1-actual)(log(1-softmax(pred)))
unscaled_logits = tf.constant([[1., -3., 10.]])
target_dist = tf.constant([[0.1, 0.02, 0.88]])
softmax_xentropy = tf.nn.softmax_cross_entropy_with_logits(unscaled_logits, target_dist)
print(sess.run(softmax_xentropy))

# Sparse entropy loss
# Use when classes and targets have to be mutually exclusive
# L = sum( -actual * log(pred) )
unscaled_logits = tf.constant([[1., -3., 10.]])
sparse_target_dist = tf.constant([2])
sparse_xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(unscaled_logits, sparse_target_dist)
print(sess.run(sparse_xentropy)) 

Result: 

 

Loss function Use Benefits Disadvantages
L2 Regression More stable  Less robust
L1 Regression More robust Less stable
Psuedo-Huber Regression More robust and stable One more parameter
Hinge Classification Creates a max margin for use in SVM Unbounded loss affected by outliers
Cross-entropy Classification More stable Unbounded loss, less robust

 

Implementing Back Propagation 

  • Online training - 非batch

# Back Propagation
#----------------------------------
#
# This python function shows how to implement back propagation
# in regression and classification models.

import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from tensorflow.python.framework import ops
ops.reset_default_graph()

# Create graph
sess = tf.Session()

# Regression Example:
# We will create sample data as follows:
# x-data: 100 random samples from a normal ~ N(1, 0.1)
# target: 100 values of the value 10.
# We will fit the model:
# x-data * A = target
# Theoretically, A = 10.

# Create data
x_vals   = np.random.normal(1, 0.1, 100)
y_vals   = np.repeat(10., 100)
x_data   = tf.placeholder(shape=[1], dtype=tf.float32)
y_target = tf.placeholder(shape=[1], dtype=tf.float32)

# Create variable (one model parameter = A)
A = tf.Variable(tf.random_normal(shape=[1]))

# Add operation to graph
my_output = tf.mul(x_data, A)

# Add L2 loss operation to graph
loss = tf.square(my_output - y_target)

# Initialize variables
init = tf.initialize_all_variables()
sess.run(init)

# Create Optimizer (solver)
my_opt = tf.train.GradientDescentOptimizer(0.02)
train_step = my_opt.minimize(loss)

# Run Loop: 100 times
for i in range(100):
    rand_index = np.random.choice(100)
    rand_x = [ x_vals[rand_index] ]
    rand_y = [ y_vals[rand_index] ]
    sess.run(train_step, feed_dict={x_data: rand_x, y_target: rand_y})
    if (i+1)%25==0:
        print('Step #' + str(i+1) + ' A = ' + str(sess.run(A)))
        print('Loss = ' + str(sess.run(loss, feed_dict={x_data: rand_x, y_target: rand_y})))　　<-- loss查看

Result:

Step #200 A = [ 3.09365487]
Loss = [[ 0.00431256]]
Step #400 A = [ 0.26573253]
Loss = [[ 0.0705369]]
Step #600 A = [-0.67981237]
Loss = [[ 0.03407416]]
Step #800 A = [-0.81504095]
Loss = [[ 0.17166217]]
Step #1000 A = [-1.02729309]
Loss = [[ 0.25573865]]
Step #1200 A = [-0.96181494]
Loss = [[ 0.04576259]]
Step #1400 A = [-1.02739477]
Loss = [[ 0.05697485]]

Ending Accuracy = 0.98

 

  • batch training的batch index

以下示例，主要演示了增加一个维度在数据，专门用于作为batch idx。【expand_dims(...)】

至于batch training的具体内容，将在下一个环节讲解。

# Classification Example
# We will create sample data as follows:
# x-data: sample 50 random values from a normal = N(-1, 1)
#         + sample 50 random values from a normal = N(1, 1)
# target: 50 values of 0 + 50 values of 1.
#         These are essentially 100 values of the corresponding output index
# We will fit the binary classification model:
# If sigmoid(x+A) < 0.5 -> 0 else 1
# Theoretically, A should be -(mean1 + mean2)/2

ops.reset_default_graph()

# Create graph
sess = tf.Session()

# Create data
x_vals   = np.concatenate((np.random.normal(-1, 1, 50), np.random.normal(3, 1, 50)))
y_vals   = np.concatenate((np.repeat(0., 50), np.repeat(1., 50)))
x_data   = tf.placeholder(shape=[1], dtype=tf.float32)
y_target = tf.placeholder(shape=[1], dtype=tf.float32)

# Create variable (one model parameter = A)
A = tf.Variable(tf.random_normal(mean=10, shape=[1]))

# Add operation to graph
# Want to create the operstion sigmoid(x + A)
# Note, the sigmoid() part is in the loss function
my_output = tf.add(x_data, A)
【这里就一个简单的加法运算】

# Now we have to add another dimension to each (batch size of 1)
my_output_expanded = tf.expand_dims(my_output, 0)
y_target_expanded  = tf.expand_dims(y_target,  0)

# Initialize variables
init = tf.initialize_all_variables()
sess.run(init)

# Add classification loss (cross entropy)
xentropy = tf.nn.sigmoid_cross_entropy_with_logits(my_output_expanded, y_target_expanded)

# Create Optimizer
my_opt = tf.train.GradientDescentOptimizer(0.05)
train_step = my_opt.minimize(xentropy)

# Run loop
for i in range(1400):
    rand_index = np.random.choice(100)
    rand_x = [x_vals[rand_index]]
    rand_y = [y_vals[rand_index]]
    
    sess.run(train_step, feed_dict={x_data: rand_x, y_target: rand_y})
    if (i+1)%200==0:
        print('Step #' + str(i+1) + ' A = ' + str(sess.run(A)))
        print('Loss = ' + str(sess.run(xentropy, feed_dict={x_data: rand_x, y_target: rand_y})))
        
# Evaluate Predictions
predictions = []
for i in range(len(x_vals)):
    x_val = [x_vals[i]]
    prediction = sess.run(tf.round(tf.sigmoid(my_output)), feed_dict={x_data: x_val})　　# my_output是实际输出值，sigmoid化后再四舍五入
    predictions.append(prediction[0])
    
accuracy = sum(x==y for x,y in zip(predictions, y_vals))/100.　　# 实际输出值 与 预计值 比较，求正确率
print('Ending Accuracy = ' + str(np.round(accuracy, 2)))

 

Working with Batch and Stochastic Training

根据 np.random.choice(100)，本来就是Stochastic training or Online training.

【这个例子是不错的呦】

# Batch and Stochastic Training
#----------------------------------
#
#  This python function illustrates two different training methods:
#  batch and stochastic training.  For each model, we will use
#  a regression model that predicts one model variable.

import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from tensorflow.python.framework import ops
ops.reset_default_graph()

# We will implement a regression example in stochastic and batch training

# Stochastic Training:
# Create graph
sess = tf.Session()

# Create data
x_vals   = np.random.normal(1, 0.1, 100)
y_vals   = np.repeat(10., 100)
x_data   = tf.placeholder(shape=[1], dtype=tf.float32)
y_target = tf.placeholder(shape=[1], dtype=tf.float32)

# Create variable (one model parameter = A)
A = tf.Variable(tf.random_normal(shape=[1]))

# Add operation to graph
my_output = tf.mul(x_data, A)

# Add L2 loss operation to graph
loss = tf.square(my_output - y_target)

# Initialize variables
init = tf.initialize_all_variables()
sess.run(init)

# Create Optimizer
my_opt = tf.train.GradientDescentOptimizer(0.02)
train_step = my_opt.minimize(loss)

loss_stochastic = []
# Run Loop
for i in range(100):
    rand_index = np.random.choice(100)
    rand_x = [x_vals[rand_index]]
    rand_y = [y_vals[rand_index]]
    sess.run(train_step, feed_dict={x_data: rand_x, y_target: rand_y})
    if (i+1)%5==0:
        print('Step #' + str(i+1) + ' A = ' + str(sess.run(A)))
        temp_loss = sess.run(loss, feed_dict={x_data: rand_x, y_target: rand_y})
        print('Loss = ' + str(temp_loss))
        loss_stochastic.append(temp_loss)
View Code
# Batch Training:
# Re-initialize graph
ops.reset_default_graph()
sess = tf.Session()

# Declare batch size
batch_size = 20

# Create data
x_vals   = np.random.normal(1, 0.1, 100)
y_vals   = np.repeat(10., 100)
x_data   = tf.placeholder(shape=[None, 1], dtype=tf.float32)
y_target = tf.placeholder(shape=[None, 1], dtype=tf.float32)

# Create variable (one model parameter = A)
A = tf.Variable(tf.random_normal(shape=[1,1]))

# Add operation to graph
my_output = tf.matmul(x_data, A)

# Add L2 loss operation to graph 因为是回归问题
loss = tf.reduce_mean(tf.square(my_output - y_target))　　# 因为是batch training

# Initialize variables
init = tf.initialize_all_variables()
sess.run(init)

# Create Optimizer
my_opt = tf.train.GradientDescentOptimizer(0.02)
train_step = my_opt.minimize(loss)

loss_batch = []
# Run Loop
for i in range(100):　　# NB： 这是不是标准的minnbatch and epoch方式
    rand_index = np.random.choice(100, size=batch_size)
    rand_x = np.transpose([x_vals[rand_index]])
    rand_y = np.transpose([y_vals[rand_index]])
    sess.run(train_step, feed_dict={x_data: rand_x, y_target: rand_y})
    if (i+1)%5==0:
        print('Step #' + str(i+1) + ' A = ' + str(sess.run(A)))
        temp_loss = sess.run(loss, feed_dict={x_data: rand_x, y_target: rand_y})　　<-- loss查看print('Loss = ' + str(temp_loss))
        loss_batch.append(temp_loss)
        
plt.plot(range(0, 100, 5), loss_stochastic, 'b-', label='Stochastic Loss')
plt.plot(range(0, 100, 5), loss_batch, 'r--', label='Batch Loss, size=20')
plt.legend(loc='upper right', prop={'size': 11})
plt.show() 

Result: 

 

Binary classifier for Iris Dataset

# Combining Everything Together
#----------------------------------
# This file will perform binary classification on the
# class if iris dataset. We will only predict if a flower is
# I.setosa or not.
#
# We will create a simple binary classifier by creating a line
# and running everything through a sigmoid to get a binary predictor.
# The two features we will use are pedal length and pedal width.
#
# We will use batch training, but this can be easily
# adapted to stochastic training.

import matplotlib.pyplot as plt
import numpy as np
from sklearn import datasets
import tensorflow as tf
from tensorflow.python.framework import ops
ops.reset_default_graph()

# Load the iris data
# iris.target = {0, 1, 2}, where '0' is setosa
# iris.data ~ [sepal.width, sepal.length, pedal.width, pedal.length]
iris = datasets.load_iris()
binary_target = np.array([1. if x==0 else 0. for x in iris.target])
iris_2d = np.array([[x[2], x[3]] for x in iris.data])   # Jeff: only consider two features.

# Declare batch size
batch_size = 20

# Create graph
sess = tf.Session()
Init

Construct graph:

# Declare placeholders 三个feed_dict的入口
x1_data  = tf.placeholder(shape=[None, 1], dtype=tf.float32)
x2_data  = tf.placeholder(shape=[None, 1], dtype=tf.float32)
y_target = tf.placeholder(shape=[None, 1], dtype=tf.float32)

# Create variables A and b (0 = x1 - A*x2 + b)
A = tf.Variable(tf.random_normal(shape=[1, 1]))
b = tf.Variable(tf.random_normal(shape=[1, 1]))

###################################
# Add model to graph:
# x1 - A*x2 + b
###################################
my_mult   = tf.matmul(x2_data, A)
my_add    = tf.add(my_mult, b)
my_output = tf.sub(x1_data, my_add)
#my_output = tf.sub(x_data[0], tf.add(tf.matmul(x_data[1], A), b))


# Add classification loss (cross entropy)
xentropy = tf.nn.sigmoid_cross_entropy_with_logits(my_output, y_target)

# Create Optimizer
my_opt     = tf.train.GradientDescentOptimizer(0.05)
train_step = my_opt.minimize(xentropy)

# Initialize variables
init = tf.initialize_all_variables()
sess.run(init)

Training loop: 

# Run Loop
for i in range(1000):
    rand_index = np.random.choice(len(iris_2d), size=batch_size)
    #rand_x = np.transpose([iris_2d[rand_index]])
    rand_x  = iris_2d[rand_index]
    # 获取一个样本数据
    rand_x1 = np.array([[x[0]] for x in rand_x])
    rand_x2 = np.array([[x[1]] for x in rand_x])
    #rand_y = np.transpose([binary_target[rand_index]])
    rand_y  = np.array([[y] for y in binary_target[rand_index]])

    sess.run(train_step, feed_dict={x1_data: rand_x1, x2_data: rand_x2, y_target: rand_y})
    if (i+1)%200==0:
        print('Step #' + str(i+1) + ' A = ' + str(sess.run(A)) + ', b = ' + str(sess.run(b))) 
# Visualize Results
# Pull out slope/intercept
[[slope]] = sess.run(A)
[[intercept]] = sess.run(b)

# Create fitted line
x = np.linspace(0, 3, num=50)
ablineValues = []
for i in x:
  ablineValues.append(slope*i+intercept)

# Plot the fitted line over the data
setosa_x = [a[1] for i,a in enumerate(iris_2d) if binary_target[i]==1]
setosa_y = [a[0] for i,a in enumerate(iris_2d) if binary_target[i]==1]
non_setosa_x = [a[1] for i,a in enumerate(iris_2d) if binary_target[i]==0]
non_setosa_y = [a[0] for i,a in enumerate(iris_2d) if binary_target[i]==0]
plt.plot(setosa_x, setosa_y, 'rx', ms=10, mew=2, label='setosa')
plt.plot(non_setosa_x, non_setosa_y, 'ro', label='Non-setosa')
plt.plot(x, ablineValues, 'b-')
plt.xlim([0.0, 2.7])
plt.ylim([0.0, 7.1])
plt.suptitle('Linear Separator For I.setosa', fontsize=20)
plt.xlabel('Petal Length')
plt.ylabel('Petal Width')
plt.legend(loc='lower right')
plt.show()
Visualize Results

Result: 

 

Evaluating Models

（略，建议专题讲解） 

 

posted @ 2017-07-18 14:37  郝壹贰叁  阅读(1051)  评论(0)  编辑  收藏  举报