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

Usage

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

General tips

It's useful to re-scale dataset before passing to estimator to 0 mean and unit standard deviation. Stochastic Gradient Descent doesn't always do the right thing when variable are very different scale.
Categorical variables should be managed before passing input to the estimator. I'll write a tutorial in coming days on how to handle categorical variables Deep Learning-style.

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)

Linear Regressor

Simple linear regression:

import skflow
from sklearn import datasets, metrics, preprocessing

boston = datasets.load_boston()
X = preprocessing.StandardScaler().fit_transform(boston.data)
regressor = skflow.TensorFlowLinearRegressor()
regressor.fit(X, boston.target)
score = metrics.mean_squared_error(regressor.predict(X), boston.target)
print ("MSE: %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.models.logistic_regression(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)

Custom model with multiple GPUs

To use multiple GPUs to build a custom model, everything else is the same as the example above except that in the definition of custom model you'll need to specify the device:

import tensorflow as tf

def my_model(X, y):
    """
    This is DNN with 10, 20, 10 hidden layers, and dropout of 0.5 probability.

    Note: If you want to run this example with multiple GPUs, Cuda Toolkit 7.0 and 
    CUDNN 6.5 V2 from NVIDIA need to be installed beforehand. 
    """
    with tf.device('/gpu:1'):
    	layers = skflow.ops.dnn(X, [10, 20, 10], keep_prob=0.5)
    with tf.device('/gpu:2'):
    	return skflow.models.logistic_regression(layers, y)

Coming soon

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

petrex/skflow