mathkann / skflow

Simplified interface for TensorFlow (mimicking Scikit Learn)

Scikit Flow

This is a simplified interface for TensorFlow, to get people started on predictive analytics and data mining.

Why TensorFlow?

• TensorFlow provides a good backbone for building different shapes of machine learning applications.
• It will continue to evolve both in the distributed direction and as general pipelinining machinery.

Why Scikit Flow?

• To smooth the transition from the Scikit Learn world of one-liner machine learning into the more open world of building different shapes of ML models. You can start by using fit/predict and slide into TensorFlow APIs as you are getting comfortable.
• To provide a set of reference models that would be easy to integrate with existing code.

Installation

First, make sure you have TensorFlow and Scikit Learn installed, then just run:

pip install git+git://github.com/google/skflow.git

Tutorial

• Introduction to Scikit Flow and why you want to start learning TensorFlow
• More coming soon.

Usage

Below are few simple examples of the API. For more examples, please see examples.

Linear Classifier

Simple linear classification.

import skflow
from sklearn import datasets, metrics

iris = datasets.load_iris()
classifier = skflow.TensorFlowLinearClassifier(n_classes=3)
classifier.fit(iris.data, iris.target)
score = metrics.accuracy_score(classifier.predict(iris.data), iris.target)
print("Accuracy: %f" % score)

Deep Neural Network

Example of 3 layer network with 10, 20 and 10 hidden units respectively:

import skflow
from sklearn import datasets, metrics

iris = datasets.load_iris()
classifier = skflow.TensorFlowDNNClassifier(hidden_units=[10, 20, 10], n_classes=3)
classifier.fit(iris.data, iris.target)
score = metrics.accuracy_score(classifier.predict(iris.data), iris.target)
print("Accuracy: %f" % score)

Custom model

Example of how to pass a custom model to the TensorFlowEstimator:

import skflow
from sklearn import datasets, metrics

iris = datasets.load_iris()

def my_model(X, y):
    """This is DNN with 10, 20, 10 hidden layers, and dropout of 0.5 probability."""
    layers = skflow.ops.dnn(X, [10, 20, 10], keep_prob=0.5)
    return skflow.ops.logistic_classifier(layers, y)

classifier = skflow.TensorFlowEstimator(model_fn=my_model, n_classes=3)
classifier.fit(iris.data, iris.target)
score = metrics.accuracy_score(classifier.predict(iris.data), iris.target)
print("Accuracy: %f" % score)

Coming soon

• Easy way to handle categorical variables
• Text categorization
• Images (CNNs)
• More & deeper