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Introduction to Machine Learning with TensorFlow

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Presentation at the Google Dev Fest Bari 2016

Published in: Data & Analytics

Introduction to Machine Learning with TensorFlow

  1. 1. Introduction to Machine Learning with TensorFlow Paolo Tomeo
  2. 2. Open source Machine Learning library Especially useful for Deep Learning For research and production Apache 2.0 license
  3. 3. Machine Learning Computer algorithms for learning to do something - learning to complete a task - make accurate predictions - to behave intelligently The focus is on automatic methods: learning without any human intervention
  4. 4. Hello World Image from https://github.com/mnielsen/neural-networks-and-deep-learning ?
  5. 5. What we see What the computer “sees”
  6. 6. Complete code import tensorflow as tf mnist = tf.contrib.learn.datasets.load_dataset('mnist') classifier = tf.learn.LinearClassifier(n_classes=10) classifier.fit(mnist.train.images, mnist.train.labels) score = metrics.accuracy_score(mnist.test.labels, classifier.predict(mnist.test.images)) print('Accuracy: {0:f}'.format(score))
  7. 7. Biologically Inspired Artificial Neural Network Image from https://visualstudiomagazine.com/articles/2014/06/01/deep-neural-networks.aspx
  8. 8. Deep Neural Network (DNN)
  9. 9. Iris Dataset
  10. 10. Deep Learning Classifier for Iris Dataset (1/3) Tutorial from https://www.tensorflow.org/versions/r0.11/tutorials/tflearn/index.html#tf-contrib-learn-quickstart import tensorflow as tf import numpy as np # Data sets IRIS_TRAINING = "iris_training.csv“ IRIS_TEST = "iris_test.csv" # Load datasets. training_set = tf.contrib.learn.datasets.base.load_csv(filename=IRIS_TRAINING, target_dtype=np.int) test_set = tf.contrib.learn.datasets.base.load_csv(filename=IRIS_TEST, target_dtype=np.int)
  11. 11. Deep Learning Classifier for Iris Dataset (2/3) Tutorial from https://www.tensorflow.org/versions/r0.11/tutorials/tflearn/index.html#tf-contrib-learn-quickstart # Specify that all features have real-value data feature_columns = [tf.contrib.layers.real_valued_column("", dimension=4)] # Build 3 layer DNN with 10, 20, 10 units respectively. classifier = tf.contrib.learn.DNNClassifier(feature_columns=feature_columns, hidden_units=[10, 20, 10], n_classes=3, model_dir="/tmp/iris_model") # Fit model. classifier.fit(x=training_set.data, y=training_set.target, steps=2000)
  12. 12. Deep Learning Classifier for Iris Dataset (3/3) Tutorial from https://www.tensorflow.org/versions/r0.11/tutorials/tflearn/index.html#tf-contrib-learn-quickstart # Evaluate accuracy. accuracy_score = classifier.evaluate(x=test_set.data, y=test_set.target)["accuracy"] print('Accuracy: {0:f}'.format(accuracy_score)) # Classify two new flower samples. new_samples = np.array([[6.4, 3.2, 4.5, 1.5], [5.8, 3.1, 5.0, 1.7]], dtype=float) y = classifier.predict(new_samples) print('Predictions: {}'.format(str(y)))
  13. 13. Getting Started Exercises
  14. 14. Lots of tutorials at tensorflow.org
  15. 15. Codelab - goo.gl/xGsB9d Video - goo.gl/B2zYWN TensorFlow for Poets
  16. 16. Mobile TensorFlow
  17. 17. Claude Monet - Bouquet of Sunflowers Images from the Metropolitan Museum of Art (with permission) Image by @random_forests
  18. 18. A little more TensorFlow
  19. 19. A multidimensional array. A graph of operations.
  20. 20. Data Flow Graphs Computation is defined as a directed acyclic graph (DAG) to optimize an objective function Graph is defined in high-level language (Python) Graph is compiled and optimized Graph is executed (in parts or fully) on available low level devices (CPU, GPU) Data (tensors) flow through the graph TensorFlow can compute gradients automatically
  21. 21. Architecture Core in C++ Front ends: Python and C++ today, community may add more Core TensorFlow Execution System CPU GPU Android iOS ... C++ front end Python front end ...
  22. 22. tf.contrib.learn TensorFlow’s high-level machine learning API Easy to configure, train, and evaluate a variety of machine learning models Datasets available in tf.contrib.learn.datasets Warning: any code in tf.contrib is not officially supported, and may change or be removed at any time without notice.
  23. 23. tf.contrib.learn TensorFlow’s high-level machine learning API Easy to configure, train, and evaluate a variety of machine learning models Datasets available in tf.contrib.learn.datasets Warning: any code in tf.contrib is not officially supported, and may change or be removed at any time without notice.
  24. 24. Questions?
  25. 25. tensorflow.org github.com/tensorflow Want to learn more? Udacity class on Deep Learning, goo.gl/iHssII Guides, codelabs, videos MNIST for Beginners, goo.gl/tx8R2b TF Learn Quickstart, goo.gl/uiefRn TensorFlow for Poets, goo.gl/bVjFIL ML Recipes, goo.gl/KewA03 TensorFlow and Deep Learning without a PhD, goo.gl/pHeXe7 What's Next

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