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.
First, make sure you have TensorFlow and Scikit Learn installed, then just run:
pip install git+git://github.com/google/skflow.git- Introduction to Scikit Flow and why you want to start learning TensorFlow
- DNNs, custom model and Digit recognition examples
- More coming soon.
Below are few simple examples of the API. For more examples, please see examples.
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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.
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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.
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)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)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)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)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)- Easy way to handle categorical variables
- Text categorization
- Images (CNNs)
- More & deeper