TensorFlow 101: Your Best Bet for Deep Learning
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TensorFlow is a machine learning library for building and training neural networks. It can handle everything from low-level graph construction using tensors and operations, to high-level neural network models. Key steps in using TensorFlow include defining the network architecture, preprocessing data, training the model to minimize a loss function, and deploying the trained model. Hello world examples demonstrate building simple graphs to multiply and add placeholders and constants.
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TensorFlow 101: Your Best Bet for Deep Learning
- 1. TensorFlow 101 Ashish Bansal, Snr Dir, Data Science @ Capital One @ash_bans | linkedin.com/in/bansalashish
- 2. 1 TensorFlow is still your best bet! Deep Learning Libraries Landscape Source: Getting started with Deep Learning – Review of available tools (https://svds.com/getting-started-deep-learning/)
- 3. 2 What and Why? • Machine Learning Library • Built for Neural Networks • Nascent support for other models • Developed by Google, released as open source in Nov. 2015 • Python & C++ APIs • Multi-platform: • Mac, Linux, Windows, iOS, Android • Support for CPU + GPU and Distributed Computation • Active Community • Built for Research and Production TensorFlow
- 4. 3 • Released v1.0 in February 2017 TensorFlow Development
- 5. 4 Low Level APIs TensorFlow Structure
- 6. 5 High Level APIs TensorFlow Structure Coming Soon!
- 7. 6 Key Steps Step 1: Data pre-processing - Missing value imputation - Mean scaling and normalization - Preparation for mini-batches Step 2: Define the network architecture - Type of NN - Layers, sizes - Activation and output functions Step 3: Train the network - Loss function - Optimization function - Train/CV/Test sets Step 4: Model serving - Store the trained network - Run! TensorFlow Application
- 8. 7 Hello World Step 1: Build the Graph Step 2: Run the Graph
- 9. Step 1: Build the Graph TensorFlow 101
- 10. 9 Graph == Nodes (Ops) through which Tensors “flow” Building the Graph [ 1 2 3 ]Rank 1: Rank 2: 1 2 3 4 5 6 7 8 Rank 3: … Rank 0: [ 2 ] Tensors Nodes Constant/Variable/Placeholder Add/Subtract Matrix Multiplication SoftMax ReLU Loss Functions Optimization Functions … Tensors have type, shape, and value Also known as “Ops”
- 11. 10 Graph representation of y = w*x+b A Simple Graph Add “y” Placehol der “x” Constan t “w” Constan t “b” Multiply “prod”
- 12. 11 Add your own sub-title here Hello World Step 1: Build the Graph Step 2: Run the Graph
- 13. Step 2: Run the Graph TensorFlow 101
- 14. • Session.run([y]) will return the Tensor output of node “y” after evaluating all other nodes in this graph 13 Sessions are used to evaluate node outputs Running the Graph Add “y” Placehol der “x” Constan t “w” Constan t “b” Multiply “prod”
- 15. 14 Hello World Step 1: Build the Graph Step 2: Run the Graph
- 16. 15 2-layer Fully Connected Network Inputs and outputs Layer 1 Layer 2
- 17. Thank you! Ashish Bansal, Snr Dir, Data Science @ Capital One @ash_bans | linkedin.com/in/bansalashish
- 19. • tf.constant • Evaluates to a constant tensor • Tensor must be specified during graph construction • tf.Variable • Evaluates to a Tensor of fixed type and shape with variable values • Initial values must be specified before evaluation • “Trainable” by default • tf.placeholder • Creates a node to allow a Tensor to be passed at runtime • e.g. Used for feeding new data into a graph 17 Feeding data Inputs and model parameters
- 20. • Standard math functions you might expect: • tf.add/tf.subtract (overloaded with +/-) • tf.matmul • Useful “reduce” functions: • tf.reduce_mean • tf.reduce_sum • tf.reduce_max • tf.reduce_min https://www.tensorflow.org/api_guides/python/math_ops 18 High school math, but now with Tensors! Standard Math
- 21. • Activation Functions: • tf.sigmoid • tf.tanh • tf.nn.relu • Classification Functions: • tf.nn.softmax • Alternative NN Architectures: • tf.nn.conv* (many convolution Ops) • tf.nn.pool More info at: https://www.tensorflow.org/versions/r1.0/api_docs/python/nn/ 19 Functions to build up neural networks Neural Network Functions
- 22. • Loss Functions: • tf.nn.l2_loss • tf.nn.softmax_cross_entropy_with_logits • Optimizers: • tf.train.GradientDescentOptimizer • tf.train.AdamOptimizer • tf.train.AdagradOptimizer • Note: Automatic gradient calculation based on the Graph! 20 How well is the model doing? And how can it get better? Loss Functions and Optimizers
Editor's Notes
- When adding an image to the background adjust transparency to 25%
- Deep learning 4J Keras is a high level wrapper on top of TF or Theano Torch is Lua based
- When adding an image to the background adjust transparency to 25%
- When adding an image to the background adjust transparency to 25%
- When adding an image to the background adjust transparency to 25%
- When adding an image to the background adjust transparency to 25%
- When adding an image to the background adjust transparency to 25%