Introduction to Machine Learning with TensorFlow

Paolo Tomeo

Open source Machine Learning library

Especially useful for Deep Learning

For research and production

Apache 2.0 license

Machine LearningComputer 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

Hello World

Image from https://github.com/mnielsen/neural-networks-and-deep-learning

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What we see What the computer “sees”

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))

Biologically Inspired Artificial Neural Network

Image from https://visualstudiomagazine.com/articles/2014/06/01/deep-neural-networks.aspx

Deep Neural Network (DNN)

Iris Dataset

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 tfimport numpy as np

# Data setsIRIS_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)

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 datafeature_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)

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)))

Getting Started Exercises

Lots of tutorials at tensorflow.org

Codelab - goo.gl/xGsB9d Video - goo.gl/B2zYWN

TensorFlow for Poets

Mobile TensorFlow

Claude Monet - Bouquet of SunflowersImages from the Metropolitan Museum of Art (with permission) Image by @random_forests

A little more TensorFlow

A multidimensional array.

A graph of operations.

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

ArchitectureCore 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 ...

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.

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.

Questions?

tensorflow.orggithub.com/tensorflow

Want to learn more?Udacity class on Deep Learning, goo.gl/iHssII

Guides, codelabs, videosMNIST for Beginners, goo.gl/tx8R2bTF Learn Quickstart, goo.gl/uiefRnTensorFlow for Poets, goo.gl/bVjFILML Recipes, goo.gl/KewA03TensorFlow and Deep Learning without a PhD, goo.gl/pHeXe7

What's Next