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 | ||
==References== | ==References== | ||
Revision as of 23:41, 29 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
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
