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Diffstat (limited to 'tensorflow/contrib/learn/python/learn/README.md')
| -rw-r--r-- | tensorflow/contrib/learn/python/learn/README.md | 18 |
1 files changed, 9 insertions, 9 deletions
diff --git a/tensorflow/contrib/learn/python/learn/README.md b/tensorflow/contrib/learn/python/learn/README.md index 0aae178e9a..6a7b0ea614 100644 --- a/tensorflow/contrib/learn/python/learn/README.md +++ b/tensorflow/contrib/learn/python/learn/README.md @@ -9,7 +9,7 @@ TF Learn is a simplified interface for TensorFlow, to get people started on pred ### Why *TensorFlow Learn*? -- To smooth the transition from the [scikit-learn](http://scikit-learn.org/stable/) world of one-liner machine learning into the more open world of building different shapes of ML models. You can start by using [fit](../../../../g3doc/api_docs/python/contrib.learn.md#Estimator.fit)/[predict](../../../../g3doc/api_docs/python/contrib.learn.md#Estimator.predict) and slide into TensorFlow APIs as you are getting comfortable. +- To smooth the transition from the [scikit-learn](http://scikit-learn.org/stable/) world of one-liner machine learning into the more open world of building different shapes of ML models. You can start by using [fit](https://www.tensorflow.org/api_docs/python/tf/contrib/learn/Estimator#fit)/[predict](https://www.tensorflow.org/api_docs/python/tf/contrib/learn/Estimator#predict) and slide into TensorFlow APIs as you are getting comfortable. - To provide a set of reference models that will be easy to integrate with existing code. ## Installation @@ -43,17 +43,17 @@ Optionally you can install [scikit-learn](http://scikit-learn.org/stable/) and [ ### Existing Estimator Implementations - [`LinearClassifier`](https://www.tensorflow.org/code/tensorflow/contrib/learn/python/learn/estimators/linear.py) - ([docs](../../../../g3doc/api_docs/python/contrib.learn.md#LinearClassifier)) + ([docs](https://www.tensorflow.org/api_docs/python/tf/contrib/learn/LinearClassifier)) - [`LinearRegressor`](https://www.tensorflow.org/code/tensorflow/contrib/learn/python/learn/estimators/linear.py) - ([docs](../../../../g3doc/api_docs/python/contrib.learn.md#LinearRegressor)) + ([docs](https://www.tensorflow.org/api_docs/python/tf/contrib/learn/LinearRegressor)) - [`DNNClassifier`](https://www.tensorflow.org/code/tensorflow/contrib/learn/python/learn/estimators/dnn.py) - ([docs](../../../../g3doc/api_docs/python/contrib.learn.md#DNNClassifier)) + ([docs](https://www.tensorflow.org/api_docs/python/tf/contrib/learn/DNNClassifier)) - [`DNNRegressor`](https://www.tensorflow.org/code/tensorflow/contrib/learn/python/learn/estimators/dnn.py) - ([docs](../../../../g3doc/api_docs/python/contrib.learn.md#DNNRegressor)) + ([docs](https://www.tensorflow.org/api_docs/python/tf/contrib/learn/DNNRegressor)) - [`DNNLinearCombinedClassifier`](https://www.tensorflow.org/code/tensorflow/contrib/learn/python/learn/estimators/dnn_linear_combined.py) - ([docs](../../../../g3doc/api_docs/python/contrib.learn.md#DNNLinearCombinedClassifier)) + ([docs](https://www.tensorflow.org/api_docs/python/tf/contrib/learn/DNNLinearCombinedClassifier)) - [`DNNLinearCombinedRegressor`](https://www.tensorflow.org/code/tensorflow/contrib/learn/python/learn/estimators/dnn_linear_combined.py) - ([docs](../../../../g3doc/api_docs/python/contrib.learn.md#DNNLinearCombinedRegressor)) + ([docs](https://www.tensorflow.org/api_docs/python/tf/contrib/learn/DNNLinearCombinedRegressor)) - [`SVM`](https://www.tensorflow.org/code/tensorflow/contrib/learn/python/learn/estimators/svm.py) ([docs](https://www.tensorflow.org/code/tensorflow/contrib/learn/python/learn/estimators/g3doc/svm.md)) - [`GMM`](https://www.tensorflow.org/code/tensorflow/contrib/factorization/python/ops/gmm.py) @@ -67,7 +67,7 @@ Below are a few simple examples of the API. For more examples, please see [examp General tips: -- It's useful to rescale a dataset to 0 mean and unit standard deviation before passing it to an [`Estimator`](../../../../g3doc/api_docs/python/contrib.learn.md#estimators). [Stochastic Gradient Descent](https://en.wikipedia.org/wiki/Stochastic_gradient_descent) doesn't always do the right thing when variable are at very different scales. +- It's useful to rescale a dataset to 0 mean and unit standard deviation before passing it to an [`Estimator`](https://www.tensorflow.org/api_docs/python/tf/contrib/learn/Estimator). [Stochastic Gradient Descent](https://en.wikipedia.org/wiki/Stochastic_gradient_descent) doesn't always do the right thing when variable are at very different scales. - Categorical variables should be managed before passing input to the estimator. @@ -219,7 +219,7 @@ INFO:tensorflow:Loss for final step: 0.0162506.</pre> ## Summaries -If you supply a `model_dir` argument to your `Estimator`s, TensorFlow will write summaries for ``loss`` and histograms for variables in this directory. (You can also add custom summaries in your custom model function by calling [Summary](../../../../g3doc/api_docs/python/train.md#summary-operations) operations.) +If you supply a `model_dir` argument to your `Estimator`s, TensorFlow will write summaries for ``loss`` and histograms for variables in this directory. (You can also add custom summaries in your custom model function by calling [Summary](https://www.tensorflow.org/api_guides/python/summary) operations.) To view the summaries in TensorBoard, run the following command, where `logdir` is the `model_dir` for your `Estimator`: |