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update tensorflow learn readme (#3140)

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* update tensorflow learn readme

since `TensorFlowDNNClassifier`, `TensorFlowLinearClassifier`, `TensorFlowLinearRegressor` are all deprecated, use `DNNClassifier`, `LinearClassifier`, `LinearRegressor`

* Update README.md

* Update README.md
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Wenjian Huang 2016-07-02 01:57:11 +08:00 committed by Vijay Vasudevan
parent 4e1221f01b
commit bb9943a6be
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tensorflow/contrib/learn/python/learn/README.md
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@ -59,8 +59,8 @@ Simple linear classification:
from sklearn import datasets, metrics
iris = datasets.load_iris()
classifier = learn.TensorFlowLinearClassifier(n_classes=3)
classifier.fit(iris.data, iris.target)
classifier = learn.LinearClassifier(n_classes=3)
classifier.fit(iris.data, iris.target, steps=200, batch_size=32)
score = metrics.accuracy_score(iris.target, classifier.predict(iris.data))
print("Accuracy: %f" % score)
```
@ -74,8 +74,8 @@ from sklearn import datasets, metrics, preprocessing
boston = datasets.load_boston()
x = preprocessing.StandardScaler().fit_transform(boston.data)
regressor = learn.TensorFlowLinearRegressor()
regressor.fit(x, boston.target)
regressor = learn.LinearRegressor()
regressor.fit(x, boston.target, steps=200, batch_size=32)
score = metrics.mean_squared_error(regressor.predict(x), boston.target)
print ("MSE: %f" % score)
```
@ -88,15 +88,15 @@ Example of 3 layer network with 10, 20 and 10 hidden units respectively:
from sklearn import datasets, metrics
iris = datasets.load_iris()
classifier = learn.TensorFlowDNNClassifier(hidden_units=[10, 20, 10], n_classes=3)
classifier.fit(iris.data, iris.target)
classifier = learn.DNNClassifier(hidden_units=[10, 20, 10], n_classes=3)
classifier.fit(iris.data, iris.target, steps=200, batch_size=32)
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:
Example of how to pass a custom model to the Estimator:
```python
from sklearn import datasets, metrics
@ -108,7 +108,7 @@ def my_model(x, y):
layers = learn.ops.dnn(x, [10, 20, 10], dropout=0.5)
return learn.models.logistic_regression(layers, y)
classifier = learn.TensorFlowEstimator(model_fn=my_model, n_classes=3)
classifier = learn.Estimator(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)
@ -116,16 +116,16 @@ 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 ``learn.TensorFlowEstimator.restore(path)`` and it will return object of your class.
Each estimator has a ``save`` method which takes folder path where all model information will be saved. For restoring you can just call ``learn.Estimator.restore(path)`` and it will return object of your class.
Some example code:
```python
classifier = learn.TensorFlowLinearRegression()
classifier = learn.LinearRegressor()
classifier.fit(...)
classifier.save('/tmp/tf_examples/my_model_1/')
new_classifier = TensorFlowEstimator.restore('/tmp/tf_examples/my_model_2')
new_classifier = Estimator.restore('/tmp/tf_examples/my_model_2')
new_classifier.predict(...)
```
@ -134,7 +134,7 @@ new_classifier.predict(...)
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.
```python
classifier = learn.TensorFlowLinearRegression()
classifier = learn.LinearRegressor()
classifier.fit(x, y, logdir='/tmp/tf_examples/my_model_1/')
```