A Gentle Introduction to Deep Learning for Developers at ConFoo Montreal

While we are a long way out from machines that can perform artificial general intelligence (AGI) tasks, deep learning can be used for many narrow artificial intelligence (AI) tasks including image or audio classification, facial recognition, object recognition, image caption generation, and natural language processing. We will explore how developers can easily integrate open source deep learning models into their applications.

Transcript

1. A Gentle Introduction to Deep Learning for Developers Bradley Holt Program Manager, Developer Advocacy Center for Open-Source Data and AI Technologies @BradleyHolt DOC ID / Month XX, 2018 / © 2018 IBM Corporation
2. Is AI overhyped?
3. Yes
4. Thank you for coming to my talk
5. General AI Metal Skull With Terminator Eye by L.C. Nøttaasen, on Flickr <https://flic.kr/p/6xh2Dr> (CC BY-SA 2.0).
6. Broad AI -[ electrIc b88Gal88 ]- by JD Hancock, on Flickr <https://flic.kr/p/6x9t1H> (CC BY 2.0).
7. Narrow AI Danbo on the Lookout by IQRemix, on Flickr <https://flic.kr/p/x5oWjP> (CC BY-SA 2.0).
8. Deep Learning http://codait.org/
9. Demo: Object Identification and Image Segmentation with magicat https://github.com/CODAIT/magicat
10. https://github.com/CODAIT/magicat
11. Install magicat $ npm install -g magicat + magicat@1.4.9 added 255 packages from 209 contributors in 11.798s
12. 23895621638_535be71dee_k.jpg 37038669284_899d7784a9_k.jpg 37489697170_31d05aa027_k.jpg 37699459356_24fd526a5e_k.jpg 37699976806_5ce694be36_k.jpg Animal photos by Susanne Nilsson, on Flickr <https://www.flickr.com/photos/infomastern/> (CC BY-SA 2.0).
13. Scan Directory with magicat $ magicat . Scanning directory '/Users/bradleydholt/tfjs-demos/magicat'... The image '23895621638_535be71dee_k.jpg' contains the following segments: background, cat. The image '37038669284_899d7784a9_k.jpg' contains the following segments: background, sheep. The image '37489697170_31d05aa027_k.jpg' contains the following segments: background, sheep. The image '37699459356_24fd526a5e_k.jpg' contains the following segments: background, cat. The image '37699976806_5ce694be36_k.jpg' contains the following segments: background, horse.
14. background, cat background, sheep background, sheep background, cat background, horse Animal photos by Susanne Nilsson, on Flickr <https://www.flickr.com/photos/infomastern/> (CC BY-SA 2.0).
15. Save Image Segment with magicat $ magicat 37699976806_5ce694be36_k.jpg --save horse The image '37699976806_5ce694be36_k.jpg' contains the following segments: background, horse. saved 37699976806_5ce694be36_k-horse.png Animal photos by Susanne Nilsson, on Flickr <https://www.flickr.com/photos/infomastern/> (CC BY-SA 2.0).
16. Demo: Object Identification and Image Segmentation with the Magic Cropping Tool https://developer.ibm.com/patterns/max-image-segmenter-magic-cropping-tool-web-app/
17. https://developer.ibm.com/patterns/max-image-segmenter-magic-cropping-tool-web-app/
20. Deep Learning Use Cases https://ibm.biz/max-developers
21. Software Programming Untitled by Marcin Wichary, on Flickr <https://flic.kr/p/68Lhc9> (CC BY 2.0).
22. 0101100100101100000110101101 0101001001100000110101101011 0110010010101010011101001011 0101010011100101010101010000 1010101011100000011010101010 1001000110000011010011010101 0010001011010101011101000101 Software Program Business Logic
23. 0101100100101100000110101101 0101001001100000110101101011 0110010010101010011101001011 0101010011100101010101010000 1010101011100000011010101010 1001000110000011010011010101 0010001011010101011101000101 Software Program Input Program Execution Output
24. Deep Learning
25. Input Layer Hidden Layers Output Layer
26. Labeled Training Data Backpropagation Animal photos by Susanne Nilsson, on Flickr <https://www.flickr.com/photos/infomastern/> (CC BY-SA 2.0). Output Errors Cat ❌ Not Cat ❌ Cat ❌ Cat Cat
27. Input Neural Network Inferencing Output Cat Animal photos by Susanne Nilsson, on Flickr <https://www.flickr.com/photos/infomastern/> (CC BY-SA 2.0).
28. Neural Network Inferencing Output Not Cat Input Animal photos by Susanne Nilsson, on Flickr <https://www.flickr.com/photos/infomastern/> (CC BY-SA 2.0).
29. Machine Learning Libraries The Leeds Library by Michael D Beckwith, on Flickr <https://flic.kr/p/r6ip1G> (CC0 1.0).
32. Demo: Basic Classification https://www.tensorflow.org/tutorials/keras/basic_classification
33. Goal & Context • Train a neural network to classify images of clothing • Classifications include dress, sneaker, etc. • We will use the Keras high-level API within TensorFlow https://www.tensorflow.org/tutorials/keras/basic_classification
34. Fashion-MNIST • Training set of 60,000 article of clothing images • Test set of 10,000 examples can be used to evaluate image classification accuracy • Each example is a 28x28 grayscale image • Each example has an associated label from 10 classes (T-shirt/top, trouser, pullover, etc.) https://github.com/zalandoresearch/fashion-mnist
35. Fashion-MNIST Images https://github.com/zalandoresearch/fashion-mnist
36. Fashion-MNIST Image Labels Label Class 0 T-shirt/top 1 Trouser 2 Pullover 3 Dress 4 Coat 5 Sandal 6 Shirt 7 Sneaker 8 Bag 9 Ankle boot https://github.com/zalandoresearch/fashion-mnist
37. https://jupyter.org/
38. Import Libraries In [3]: # TensorFlow and tf.keras import tensorflow as tf from tensorflow import keras # Helper libraries import numpy as np import matplotlib.pyplot as plt print(tf.__version__) https://www.tensorflow.org/tutorials/keras/basic_classification
39. Import Fashion-MNIST Dataset In [4]: fashion_mnist = keras.datasets.fashion_mnist (train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data() In [5]: class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat’, 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot'] https://www.tensorflow.org/tutorials/keras/basic_classification
40. Explore the Data In [6]: train_images.shape Out[6]: (60000, 28, 28) In [7]: len(train_labels) Out[7]: 60000 In [8]: train_labels Out[8]: array([9, 0, 0, ..., 3, 0, 5], dtype=uint8) In [9]: test_images.shape Out[9]: (10000, 28, 28) In [10]: len(test_labels) Out[10]: 10000 https://www.tensorflow.org/tutorials/keras/basic_classification
41. Preprocess the Data In [11]: plt.figure() plt.imshow(train_images[0]) plt.colorbar() plt.grid(False) plt.show() https://www.tensorflow.org/tutorials/keras/basic_classification
42. Preprocess the Data In [12]: train_images = train_images / 255.0 test_images = test_images / 255.0 https://www.tensorflow.org/tutorials/keras/basic_classification
43. Preprocess the Data In [13]: plt.figure(figsize=(10,10)) for i in range(25): plt.subplot(5,5,i+1) plt.xticks([]) plt.yticks([]) plt.grid(False) plt.imshow(train_images[i], cmap=plt.cm.binary) plt.xlabel(class_names[train_labels[i]]) plt.show() https://www.tensorflow.org/tutorials/keras/basic_classification
44. https://www.tensorflow.org/tutorials/keras/basic_classification
45. Set Up the Model Layers In [14]: model = keras.Sequential([ keras.layers.Flatten(input_shape=(28, 28)), keras.layers.Dense(128, activation=tf.nn.relu), keras.layers.Dense(10, activation=tf.nn.softmax) ]) https://www.tensorflow.org/tutorials/keras/basic_classification
46. Compile the Model In [15]: model.compile(optimizer='adam’, loss='sparse_categorical_crossentropy’, metrics=['accuracy']) https://www.tensorflow.org/tutorials/keras/basic_classification
47. Train the Model In [16]: model.fit(train_images, train_labels, epochs=5) https://www.tensorflow.org/tutorials/keras/basic_classification
48. Train the Model Epoch 1/5 60000/60000 [==============================] - 4s 61us/step - loss: 0.4985 - acc: 0.8261 Epoch 2/5 60000/60000 [==============================] - 3s 54us/step - loss: 0.3747 - acc: 0.8646 Epoch 3/5 60000/60000 [==============================] - 4s 58us/step - loss: 0.3359 - acc: 0.8771 Epoch 4/5 60000/60000 [==============================] - 3s 55us/step - loss: 0.3123 - acc: 0.8846 Epoch 5/5 60000/60000 [==============================] - 4s 59us/step - loss: 0.2945 - acc: 0.8912 https://www.tensorflow.org/tutorials/keras/basic_classification
49. Evaluate Accuracy In [17]: test_loss, test_acc = model.evaluate(test_images, test_labels) print('Test accuracy:', test_acc) https://www.tensorflow.org/tutorials/keras/basic_classification
50. Evaluate Accuracy 10000/10000 [==============================] - 0s 26us/stepTest accuracy: 0.8748 https://www.tensorflow.org/tutorials/keras/basic_classification
51. Make Predictions In [18]: predictions = model.predict(test_images) https://www.tensorflow.org/tutorials/keras/basic_classification
52. Make Predictions In [19]: predictions[0] Out[19]: array([9.6376725e-06, 4.5565642e-07, 5.0994299e-06, 4.8001135e-07, 4.9554501e-06, 2.0314518e-02, 5.0287876e-05, 1.6105843e-01, 2.2055059e-04, 8.1833553e-01], dtype=float32) https://www.tensorflow.org/tutorials/keras/basic_classification
53. Make Predictions In [20]: np.argmax(predictions[0]) Out[20]: 9 In [21]: test_labels[0] Out[21]: 9 https://www.tensorflow.org/tutorials/keras/basic_classification
54. https://www.tensorflow.org/tutorials/keras/basic_classification
55. Applying Deep Learning Data Science Expertise Computing Resources High-Quality Training Data Model Deployment Time Model Integration Inferencing Code And more…
56. Model Asset Exchange (MAX) Classify Generate Recognize https://developer.ibm.com/exchanges/models/
57. Choose deployable model Deep Learning asset on MAX Deploy Swagger specification Inference endpoint Metadata endpoint Microservice Input pre-processing, model execution, and output post-processing Deploy model Use model https://developer.ibm.com/exchanges/models/
58. AI Lifecycle Deploy Operate Build & Train
59. Building and Training AI Models Model Asset Exchange (MAX)
61. Deploying AI Models ONNX.js Model Asset Exchange (MAX)
63. Operating AI Systems Fabric for Deep Learning (FfDL) Train and deploy deep learning models on Kubernetes using TensorFlow, Caffe2, PyTorch, and other frameworks AI Fairness 360 (AIF360) Comprehensive set of fairness metrics for machine learning models, explanations for these metrics, and algorithms to mitigate bias in models Adversarial Robustness Toolbox (ART) Python library for adversarial attacks and defenses for neural networks with multiple framework support
65. What will you build using deep learning? Dynamic Earth - Continental Shelf by NASA Goddard Space Flight Center, on Flickr <https://flic.kr/p/ch8t25> (CC BY 2.0).
66. Resources: Machine Learning Libraries • TensorFlow https://www.tensorflow.org/ • Train your first neural network: basic classification | TensorFlow https://www.tensorflow.org/tutorials/keras/basic_classification • Keras https://keras.io/ • PyTorch https://pytorch.org/ • Caffe2 https://caffe2.ai/
67. Resources: IBM Developer • IBM Cloud https://ibm.biz/Bd2A5f • Center for Open-Source Data & AI Technologies (CODAIT) http://codait.org/ • IBM Developer Model Asset Exchange (MAX) https://developer.ibm.com/exchanges/models/ • Model Asset Exchange (MAX) Code Patterns https://ibm.biz/max-developers • Deploy a deep learning-powered ‘Magic cropping tool’ https://developer.ibm.com/patterns/max-image-segmenter-magic-cropping-tool-web-app/ • !" magicat https://github.com/CODAIT/magicat
68. Resources: Building and Training AI Models • Project Jupyter https://jupyter.org/ • IBM Developer Model Asset Exchange (MAX) https://developer.ibm.com/exchanges/models/ • IBM Watson Studio https://www.ibm.com/cloud/watson-studio
69. Resources: Deploying AI Models • Data Mining Group (PMML & PFA) http://dmg.org/ • ONNX https://onnx.ai/ • ONNX.js https://github.com/Microsoft/onnxjs • TensorFlow.js https://js.tensorflow.org/ • TensorFlow Lite https://www.tensorflow.org/lite • Core ML https://developer.apple.com/machine-learning/ • IBM Watson Machine Learning https://www.ibm.com/cloud/machine-learning
70. Resources: Operating AI Systems • Fabric for Deep Learning (FfDL) https://github.com/IBM/FfDL • AI Fairness 360 (AIF360) https://github.com/IBM/AIF360 • Adversarial Robustness Toolbox (ART) https://github.com/IBM/adversarial-robustness-toolbox • IBM Watson OpenScale https://www.ibm.com/cloud/watson-openscale
71. Thank you codait.org twitter.com/codait_datalab medium.com/ibm-watson-data-lab github.com/codait developer.ibm.com