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