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)
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 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(...)
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.
- Images (CNNs) - see example for digit recognition.
- More & deeper - different examples showing DNNs and CNNs