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.
First, make sure you have TensorFlow and Scikit Learn installed, then just run:
pip install git+git://github.com/google/skflow.git
Example usage:
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)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)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)- Easy way to handle categorical variables
- Text categorization
- Images (CNNs)
- More & deeper
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