Difference between revisions of "TensorFlow"
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* Prediction | * Prediction | ||
* Build Graph | * 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> | ||
==References== | ==References== | ||
Revision as of 00:28, 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
- Differentiatable functions that measure differences/error between true and predicted values
- Common types:
- 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)
References
- ↑ "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
