Introduction
Below are few simple examples of the API. For more examples, please see examples <https://github.com/tensorflow/skflow/tree/master/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:
.. code:: python
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(iris.target, classifier.predict(iris.data))
print("Accuracy: %f" % score)
Linear Regressor
Simple linear regression:
.. code:: python
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)
Deep Neural Network
Example of 3 layer network with 10, 20 and 10 hidden units respectively:
.. code:: python
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(iris.target, classifier.predict(iris.data))
print("Accuracy: %f" % score)
Custom model
Example of how to pass a custom model to the TensorFlowEstimator:
.. code:: python
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(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:
.. code:: python
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(...)
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.
.. code:: python
classifier = skflow.TensorFlowLinearRegression()
classifier.fit(X, y, logdir='/tmp/tf_examples/my_model_1/')
Then run next command in command line:
.. code:: bash
tensorboard --logdir=/tmp/tf_examples/my_model_1
and follow reported url.
| Graph visualization: | Text classification RNN Graph |
| Loss visualization: | Text classification RNN Loss |
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