jayinai/skflow

Simplified interface for TensorFlow (mimicking Scikit Learn) for Deep Learning

SkFlow has been moved to Tensorflow.

SkFlow has been moved to http://github.com/tensorflow/tensorflow into contrib folder specifically located here. The development will continue there. Please submit any issues and pull requests to Tensorflow repository instead.

This repository will ramp down, including after next Tensorflow release we will wind down code here. Please see instructions on most recent installation here.

Installation

No installation needed, SkFlow has been moved into Tensorflow 0.8+. Make sure you have Tensorflow 0.8+ version and you can start using SkFlow right away by importing like this:

import tensorflow.contrib.learn as skflow

Tutorial

• Introduction to Scikit Flow and Why You Want to Start Learning TensorFlow
• DNNs, Custom model and Digit Recognition examples
• Categorical Variables: One Hot vs Distributed representation
• Scikit Flow Key Features Illustrated
• More coming soon.

Community

• Twitter #skflow.
• StackOverflow with skflow tag for questions and struggles.
• Github issues for technical discussions and feature requests.
• Gitter channel for non-trivial discussions.

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.

Linear Classifier

Simple linear classification:

import tensorflow.contrib.learn as 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(iris.target, classifier.predict(iris.data))
print("Accuracy: %f" % score)

Linear Regressor

Simple linear regression:

import tensorflow.contrib.learn as 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 tensorflow.contrib.learn as 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(iris.target, classifier.predict(iris.data))
print("Accuracy: %f" % score)

Custom model

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

import tensorflow.contrib.learn as 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(iris.target, classifier.predict(iris.data))
print("Accuracy: %f" % score)

Saving / Restoring models

Each estimator has a save method which takes folder path where all model information will be saved. For restoring you can just call skflow.TensorFlowEstimator.restore(path) and it will return object of your class.

Some example code:

import tensorflow.contrib.learn as skflow

classifier = skflow.TensorFlowLinearRegression()
classifier.fit(...)
classifier.save('/tmp/tf_examples/my_model_1/')

new_classifier = TensorFlowEstimator.restore('/tmp/tf_examples/my_model_2')
new_classifier.predict(...)

Summaries

To get nice visualizations and summaries you can use logdir parameter on fit. It will start writing summaries for loss and histograms for variables in your model. You can also add custom summaries in your custom model function by calling tf.summary and passing Tensors to report.

classifier = skflow.TensorFlowLinearRegression()
classifier.fit(X, y, logdir='/tmp/tf_examples/my_model_1/')

Then run next command in command line:

tensorboard --logdir=/tmp/tf_examples/my_model_1

and follow reported url.

Graph visualization:

Loss visualization:

More examples

See examples folder for:

• Easy way to handle categorical variables - words are just an example of categorical variable.
• Text Classification - see examples for RNN, CNN on word and characters.
• Language modeling and text sequence to sequence.
• Images (CNNs) - see example for digit recognition.
• More & deeper - different examples showing DNNs and CNNs