Scikit-learn / Keras Basic Implementation Tutorial

2019.03.20Jacky, Chun-Yen Yeh

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Goal

• Introduce Scikit-learn, Keras Python library(framework).

• Go through the workflow of the classification problem.

• Lead you to step-by-step implement classification problem with Scikit-learn / Keras.

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Overview

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Artificial IntelligenceMachine Learning

Deep LearningDecision Tree

Nearest NegihborsLogistic Regression CNN

RNNTf-idf

Data Engineering . . .

.

.

.

Overview

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Artificial IntelligenceMachine Learning

Deep Learningdecision tree

Nearest NegihborsLogistic Regression CNN

RNNTf-idf

Data Engineering . . .

.

.

.

Scikit-learn

Keras

Scikit-learn

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What’s Scikit-learn?

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• A free software machine learning library for the Python language.

• Simple and efficient tools for data mining and data analysis.

• Derived from SciPy, which is a Python-based ecosystem of open-source software for mathematics, science, and engineering. (e.g. Numpy, pandas, jupyter notebook)

Why’s Scikit-learn• Commitment to documentation and usability.

• Covers most machine-learning tasks: classification, regression, clustering, dimension reduction, data preprocessing, etc…

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Supervised Classification

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Species Features

Iris Dataset

Split the Dataset

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Training Data Testing Data

ALL Data

Typically 75% : 25% = 3 : 1

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Training Data

Supervised Workflow

Model

Prediction

Evaluation

Training phase

Inference phase

Training LabelTraining Labels

Testing Data

Testing Labels

Model Construction

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Training Data

Supervised Workflow

Model

Prediction

Evaluation

Training phase

Inference phase

Training LabelTraining Labels

Testing Data

Testing Labels

Model Construction

classifier.fit(X_train, y_train)

classifier = LogisticRegression()

classifier.predict(X_test)

classifier.score(X_test, y_test)

Exercise time

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Please enter this file and click your link. https://reurl.cc/nN28n

Keras

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What’s Keras?

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Keras is a high-level deep learning API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano.

Why’s Keras?

• Allows for easy and fast prototyping (through user friendliness, modularity, and extensibility).

• Supports both convolutional networks and recurrent networks, as well as combinations of the two.

• Runs seamlessly on CPU and GPU.

• Complete documentation (keras.io)

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Three Steps of Deep Learning

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Step1 Define a set of

function

Step2 Goodness of

function

Step3 Pick the best

function

!18Output

Like lego :)

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Prediction Real Label

loss (difference)

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parameters

choose optimizer (SGD, RMSProp, …)

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Training Data

Supervised Workflow

Model

Prediction

Evaluation

Training phase

Inference phase

Training LabelTraining Labels

Testing Data

Testing Labels

Model Construction

classifier.predict_classes(X_test)

classifier.score(X_test, y_test)

Exercise time

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Please enter this file and click your link. https://reurl.cc/nN28n

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