Difference between revisions of "TensorFlow"

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** Layers
 
** Layers
 
** Estimators
 
** Estimators
 +
 +
; Loss functions
 +
* Differentiatable functions that measure differences/error between true and predicted values
 +
* Common types:
 +
** [[:wikipedia:Mean squared error|Mean squared error]] (MSE)
 +
** [[:wikipedia:Cross_entropy#Cross-entropy_error_function_and_logistic_regression|Log loss]]
 +
** [[:wikipedia:Cosine similarity|Cosine distance]]
 +
** [[:wikipedia:Cross entropy|Cross entropy]]
 +
 +
; Optimizers
 +
* Optimizers are algorithms that minimize the loss (or error) of a model
 +
* Local minimum vs. global minimum
 +
* Built-in optimizers inherit from the Optimizer class
 +
* Common types:
 +
** Gradient descent
 +
** Adam
 +
** RMSProp
 +
** Adagrad
 +
** Momentum
 +
** Adadelta
 +
 +
; Layers
 +
* What are they?
 +
** Composed of tensors and operations forming the model
 +
** Generally connected in series
 +
** Pre-made functions for creating layers in a model
 +
* Common types:
 +
** Input
 +
** Convolutional (1d, 2d, 3d)
 +
** Pooling
 +
** Dropout
 +
** Dense
 +
 +
; Estimators
 +
* Training
 +
* Evaluation
 +
* Prediction
 +
* Build Graph
 +
 +
==Examples==
 +
 +
* Basic #1
 +
<pre>
 +
import tensorflow as tf
 +
 +
m = tf.constant(3.0, name='m')
 +
b = tf.constant(1.5, name='b')
 +
x = tf.placeholder(dtype='float32', name='x')
 +
y = m*x + b
 +
 +
sess = tf.Session()
 +
 +
y.eval({x: 2}, session=sess)  # => 7.5
 +
</pre>
 +
 +
* Basic #2
 +
<pre>
 +
import tensorflow as tf
 +
 +
M = tf.constant([[1,2], [3,4]], dtype='float32')
 +
v = tf.constant([5,6], dtype='float32')
 +
sess = tf.Session()
 +
 +
sess.run(M + v)
 +
# array([[ 6.,  8.],
 +
#      [ 8., 10.]], dtype=float32)
 +
 +
sess.run(M * v)
 +
# array([[ 5., 12.],
 +
#        [15., 24.]], dtype=float32)
 +
 +
sess.run(tf.matmul(M, tf.reshape(v, [2, 1])))
 +
# array([[17.],
 +
#        [39.]], dtype=float32)
 +
</pre>
 +
 +
==Machine learning==
 +
 +
; Machine learning lifecycle
 +
# Define objective
 +
# Collect data
 +
# Data cleaning
 +
# Exploratory Data Analysis (EDA)
 +
# Data processing
 +
# Train/evaluate models
 +
# Deploy
 +
# Monitor results
 +
 +
; Machine learning lifecycle example
 +
 +
* 1. Define objective
 +
: Infer how IQ, years experince, and age affects income using linear model
 +
 +
* 2. Collect data
 +
<pre>
 +
import tensorflow as tf
 +
import numpy as np
 +
 +
import pandas as pd
 +
from pandas import DataFrame as DF
 +
 +
# Create dataset
 +
np.random.seed(555)
 +
X1 = np.random.normal(100, 15, 200).astype(int) # IQ
 +
X2 = np.random.normal(10, 4.5, 200) # years experience
 +
X3 = np.random.normal(32, 4, 200).astype(int) # age
 +
dob = np.datetime64('2017-10-31') - 365*X3
 +
b = 5
 +
er = np.random.normal(0, 1.5, 200) # noise/error
 +
 +
Y = np.array([0.3*x1 + 1.5*x2 + 0.83*x3 + b + e for x1,x2,x3,e in zip(X1,X2,X3,er)])
 +
</pre>
 +
 +
* 3. Data Cleaning
 +
<pre>
 +
cols = ['iq', 'years_experience', 'dob']
 +
df = DF(list(zip(X1,X2,dob)), columns=cols)
 +
df['income'] = Y
 +
df.info()
 +
# <class 'pandas.core.frame.DataFrame'>
 +
# RangeIndex: 200 entries, 0 to 199
 +
# Data columns (total 4 columns):
 +
# iq                  200 non-null int64
 +
# years_experience    200 non-null float64
 +
# dob                200 non-null datetime64[ns]
 +
# income              200 non-null float64
 +
# dtypes: datetime64[ns](1), float64(2), int64(1)
 +
# memory usage: 6.3 KB
 +
 +
df.describe()
 +
# Remove any negative values for years of experience
 +
df = df[df.years_experience >= 0]
 +
df.describe() # no more negative values
 +
</pre>
 +
 +
* 4. EDA
 +
<pre>
 +
df.describe(include=['datetime64'])
 +
 +
import matplotlib.pyplot as plt
 +
%matplotlib inline
 +
 +
pd.plotting.scatter_matrix(df, figsize=(16,9));
 +
</pre>
 +
[[File:Tensorflow iq scatter matrix.png]]
  
 
==References==
 
==References==

Latest revision as of 01:34, 30 April 2018

TensorFlow is an open-source software library for dataflow programming across a range of tasks. It is a symbolic math library, and is also used for machine learning applications such as neural networks.[1]

Introduction

Tensors
  • N-dimensional arrays
  • Measured by "rank"
  • All elements are same datatype
# Rank 0:
[1]
# Rank 1:
[1][2][3]
# Rank 2:
[1][2][3]
[4][5][6]
# Rank 3 (3D):
[1][2][3]
 [4][5][6]
  [7][8][9]
Tensor operations
  • Addition and subtraction
  • Multiplication and Division
  • Matrix multiplication
  • Dot product
  • Transpose
[1 2  3  4 ]   [1 5 9 ]T
|5 6  7  8 | = |2 6 10|
[9 10 11 12]   |3 7 11|
               [4 8 12]
TensorFlow building blocks
  • Lower level
    • Tensors
    • Operations
    • Graphs and sessions
  • Higher level
    • Loss functions
    • Optimizers
    • Layers
    • Estimators
Loss functions
Optimizers
  • Optimizers are algorithms that minimize the loss (or error) of a model
  • Local minimum vs. global minimum
  • Built-in optimizers inherit from the Optimizer class
  • Common types:
    • Gradient descent
    • Adam
    • RMSProp
    • Adagrad
    • Momentum
    • Adadelta
Layers
  • What are they?
    • Composed of tensors and operations forming the model
    • Generally connected in series
    • Pre-made functions for creating layers in a model
  • Common types:
    • Input
    • Convolutional (1d, 2d, 3d)
    • Pooling
    • Dropout
    • Dense
Estimators
  • Training
  • Evaluation
  • Prediction
  • Build Graph

Examples

  • Basic #1
import tensorflow as tf

m = tf.constant(3.0, name='m')
b = tf.constant(1.5, name='b')
x = tf.placeholder(dtype='float32', name='x')
y = m*x + b

sess = tf.Session()

y.eval({x: 2}, session=sess)  # => 7.5
  • Basic #2
import tensorflow as tf

M = tf.constant([[1,2], [3,4]], dtype='float32')
v = tf.constant([5,6], dtype='float32')
sess = tf.Session()

sess.run(M + v)
# array([[ 6.,  8.],
#       [ 8., 10.]], dtype=float32)

sess.run(M * v)
# array([[ 5., 12.],
#        [15., 24.]], dtype=float32)

sess.run(tf.matmul(M, tf.reshape(v, [2, 1])))
# array([[17.],
#        [39.]], dtype=float32)

Machine learning

Machine learning lifecycle
  1. Define objective
  2. Collect data
  3. Data cleaning
  4. Exploratory Data Analysis (EDA)
  5. Data processing
  6. Train/evaluate models
  7. Deploy
  8. Monitor results
Machine learning lifecycle example
  • 1. Define objective
Infer how IQ, years experince, and age affects income using linear model
  • 2. Collect data
import tensorflow as tf
import numpy as np

import pandas as pd
from pandas import DataFrame as DF

# Create dataset
np.random.seed(555)
X1 = np.random.normal(100, 15, 200).astype(int) # IQ
X2 = np.random.normal(10, 4.5, 200) # years experience
X3 = np.random.normal(32, 4, 200).astype(int) # age
dob = np.datetime64('2017-10-31') - 365*X3
b = 5
er = np.random.normal(0, 1.5, 200) # noise/error

Y = np.array([0.3*x1 + 1.5*x2 + 0.83*x3 + b + e for x1,x2,x3,e in zip(X1,X2,X3,er)])
  • 3. Data Cleaning
cols = ['iq', 'years_experience', 'dob']
df = DF(list(zip(X1,X2,dob)), columns=cols)
df['income'] = Y
df.info()
# <class 'pandas.core.frame.DataFrame'>
# RangeIndex: 200 entries, 0 to 199
# Data columns (total 4 columns):
# iq                  200 non-null int64
# years_experience    200 non-null float64
# dob                 200 non-null datetime64[ns]
# income              200 non-null float64
# dtypes: datetime64[ns](1), float64(2), int64(1)
# memory usage: 6.3 KB

df.describe()
# Remove any negative values for years of experience
df = df[df.years_experience >= 0]
df.describe() # no more negative values
  • 4. EDA
df.describe(include=['datetime64'])

import matplotlib.pyplot as plt
%matplotlib inline

pd.plotting.scatter_matrix(df, figsize=(16,9));

Tensorflow iq scatter matrix.png

References

  1. ↑ "TensorFlow: Open source machine learning" "It is machine learning software being used for various kinds of perceptual and language understanding tasks" — Jeffrey Dean, minute 0:47 / 2:17 from Youtube clip

External links

Retrieved from "http://wiki.christophchamp.com/index.php?title=TensorFlow&oldid=7236"