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 applicaitons.
It will continue evolve both in distributed direction and as a general pipelininig machinery.

Why Scikit Flow?

To smooth the transition from Scikit Learn world of one-liner machine learning into more open world of building different shapes of ML models. You can start by using fit/predict and slide into TensorFlow APIs as you getting comfortable.
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

Example usage:

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: ", 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 = accuracy_score(classifier.predict(iris.data), iris.target)
print("Accuracy: ", score)

Custom model

This is example of how to pass 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 = accuracy_score(classifier.predict(iris.data), iris.target)
print("Accuracy: ", score)

Coming soon

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

xubenben/skflow