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# TF Learn
TF Learn is a simplified interface for TensorFlow, to get people started on predictive analytics and data mining. The library covers a variety of needs: from linear models to *Deep Learning* applications like text and image understanding.
### 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 *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](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
[Install TensorFlow](https://www.tensorflow.org/install/), and then simply import `learn` via `from tensorflow.contrib.learn` or use `tf.contrib.learn`.
Optionally you can install [scikit-learn](http://scikit-learn.org/stable/) and [pandas](http://pandas.pydata.org/) for additional functionality.
### Tutorials
- [TF Learn Quickstart](https://www.tensorflow.org/get_started/tflearn). Build,
train, and evaluate a neural network with just a few lines of code.
- [Input Functions](https://www.tensorflow.org/get_started/input_fn). Learn how
to create input functions to feed data into your models.
- [Linear Model](https://www.tensorflow.org/tutorials/wide). Learn the basics
of building linear models.
- [Wide and Deep Learning](https://www.tensorflow.org/tutorials/wide_and_deep).
Jointly train a linear model and a deep neural network.
- [Logging and Monitoring](https://www.tensorflow.org/get_started/monitors).
Use the Monitor API to audit training of a neural network.
- [Custom Estimators](https://www.tensorflow.org/extend/estimators). Learn
how to create a custom estimator.
- More coming soon.
### Community
- Twitter [#tensorflow](https://twitter.com/search?q=tensorflow&src=typd).
- StackOverflow with [tensorflow tag](http://stackoverflow.com/questions/tagged/tensorflow) for questions and struggles.
- GitHub [issues](https://github.com/tensorflow/tensorflow/issues) for technical discussions and feature requests.
### Existing Estimator Implementations
- [`LinearClassifier`](https://www.tensorflow.org/code/tensorflow/contrib/learn/python/learn/estimators/linear.py)
([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](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](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](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](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](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)
([docs](https://www.tensorflow.org/code/tensorflow/contrib/factorization/g3doc/gmm.md))
- [`KMeansClustering`](https://www.tensorflow.org/code/tensorflow/contrib/learn/python/learn/estimators/kmeans.py)
([docs](https://www.tensorflow.org/code/tensorflow/contrib/factorization/g3doc/kmeans.md))
### Usage Examples
Below are a few simple examples of the API. For more examples, please see [examples](https://www.tensorflow.org/code/tensorflow/examples/learn).
General tips:
- 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.
## Linear Classifier
Simple linear classification:
```python
import tensorflow.contrib.learn.python.learn as learn
from sklearn import datasets, metrics
iris = datasets.load_iris()
feature_columns = learn.infer_real_valued_columns_from_input(iris.data)
classifier = learn.LinearClassifier(n_classes=3, feature_columns=feature_columns)
classifier.fit(iris.data, iris.target, steps=200, batch_size=32)
iris_predictions = list(classifier.predict(iris.data, as_iterable=True))
score = metrics.accuracy_score(iris.target, iris_predictions)
print("Accuracy: %f" % score)
```
## Linear Regressor
Simple linear regression:
```python
import tensorflow.contrib.learn.python.learn as learn
from sklearn import datasets, metrics, preprocessing
boston = datasets.load_boston()
x = preprocessing.StandardScaler().fit_transform(boston.data)
feature_columns = learn.infer_real_valued_columns_from_input(x)
regressor = learn.LinearRegressor(feature_columns=feature_columns)
regressor.fit(x, boston.target, steps=200, batch_size=32)
boston_predictions = list(regressor.predict(x, as_iterable=True))
score = metrics.mean_squared_error(boston_predictions, boston.target)
print ("MSE: %f" % score)
```
## Deep Neural Network
Example of 3 layer network with 10, 20 and 10 hidden units respectively:
```python
import tensorflow.contrib.learn.python.learn as learn
from sklearn import datasets, metrics
iris = datasets.load_iris()
feature_columns = learn.infer_real_valued_columns_from_input(iris.data)
classifier = learn.DNNClassifier(hidden_units=[10, 20, 10], n_classes=3, feature_columns=feature_columns)
classifier.fit(iris.data, iris.target, steps=200, batch_size=32)
iris_predictions = list(classifier.predict(iris.data, as_iterable=True))
score = metrics.accuracy_score(iris.target, iris_predictions)
print("Accuracy: %f" % score)
```
## Custom model
Example of how to pass a custom model to the Estimator:
```python
from sklearn import datasets
from sklearn import metrics
import tensorflow as tf
import tensorflow.contrib.layers.python.layers as layers
import tensorflow.contrib.learn.python.learn as learn
iris = datasets.load_iris()
def my_model(features, labels):
"""DNN with three hidden layers."""
# Convert the labels to a one-hot tensor of shape (length of features, 3) and
# with a on-value of 1 for each one-hot vector of length 3.
labels = tf.one_hot(labels, 3, 1, 0)
# Create three fully connected layers respectively of size 10, 20, and 10.
features = layers.stack(features, layers.fully_connected, [10, 20, 10])
# Create two tensors respectively for prediction and loss.
prediction, loss = (
tf.contrib.learn.models.logistic_regression(features, labels)
)
# Create a tensor for training op.
train_op = tf.contrib.layers.optimize_loss(
loss, tf.contrib.framework.get_global_step(), optimizer='Adagrad',
learning_rate=0.1)
return {'class': tf.argmax(prediction, 1), 'prob': prediction}, loss, train_op
classifier = learn.Estimator(model_fn=my_model)
classifier.fit(iris.data, iris.target, steps=1000)
y_predicted = [
p['class'] for p in classifier.predict(iris.data, as_iterable=True)]
score = metrics.accuracy_score(iris.target, y_predicted)
print('Accuracy: {0:f}'.format(score))
```
## Saving / Restoring models
Each estimator supports a `model_dir` argument, which takes a folder path where all model information will be saved:
```python
classifier = learn.DNNClassifier(..., model_dir="/tmp/my_model")
```
If you run multiple `fit` operations on the same `Estimator`, training will resume where the last operation left off, e.g.:
<pre><strong>classifier = learn.DNNClassifier(..., model_dir="/tmp/my_model")
classifier.fit(..., steps=300)</strong>
INFO:tensorflow:Create CheckpointSaverHook
INFO:tensorflow:loss = 2.40115, step = 1
INFO:tensorflow:Saving checkpoints for 1 into /tmp/leftoff/model.ckpt.
INFO:tensorflow:loss = 0.338706, step = 101
INFO:tensorflow:loss = 0.159414, step = 201
INFO:tensorflow:Saving checkpoints for 300 into /tmp/leftoff/model.ckpt.
INFO:tensorflow:Loss for final step: 0.0953846.
<strong>classifier.fit(..., steps=300)</strong>
INFO:tensorflow:Create CheckpointSaverHook
INFO:tensorflow:loss = 0.113173, step = 301
INFO:tensorflow:Saving checkpoints for 301 into /tmp/leftoff/model.ckpt.
INFO:tensorflow:loss = 0.175782, step = 401
INFO:tensorflow:loss = 0.119735, step = 501
INFO:tensorflow:Saving checkpoints for 600 into /tmp/leftoff/model.ckpt.
INFO:tensorflow:Loss for final step: 0.0518137.</pre>
To restore checkpoints to a new `Estimator`, just pass it the same `model_dir` argument, e.g.:
<pre><strong>classifier = learn.DNNClassifier(..., model_dir="/tmp/my_model")
classifier.fit(..., steps=300)</strong>
INFO:tensorflow:Create CheckpointSaverHook
INFO:tensorflow:loss = 1.16335, step = 1
INFO:tensorflow:Saving checkpoints for 1 into /tmp/leftoff/model.ckpt.
INFO:tensorflow:loss = 0.176995, step = 101
INFO:tensorflow:loss = 0.184573, step = 201
INFO:tensorflow:Saving checkpoints for 300 into /tmp/leftoff/model.ckpt.
INFO:tensorflow:Loss for final step: 0.0512496.
<strong>classifier2 = learn.DNNClassifier(..., model_dir="/tmp/my_model")
classifier2.fit(..., steps=300)</strong>
INFO:tensorflow:Create CheckpointSaverHook
INFO:tensorflow:loss = 0.0543797, step = 301
INFO:tensorflow:Saving checkpoints for 301 into /tmp/leftoff/model.ckpt.
INFO:tensorflow:loss = 0.101036, step = 401
INFO:tensorflow:loss = 0.137956, step = 501
INFO:tensorflow:Saving checkpoints for 600 into /tmp/leftoff/model.ckpt.
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](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`:
```shell
tensorboard --logdir=/tmp/tf_examples/my_model_1
```
and then load the reported URL.
**Graph visualization**
**Loss visualization**
## More examples
See the [examples folder](https://www.tensorflow.org/code/tensorflow/examples/learn) for:
- An easy way to handle [categorical variables](https://www.tensorflow.org/code/tensorflow/examples/learn/text_classification.py) (words are just an example of a categorical variable)
- Text Classification: see examples for [RNN](https://www.tensorflow.org/code/tensorflow/examples/learn/text_classification_character_rnn.py) and [CNN](https://www.tensorflow.org/code/tensorflow/examples/learn/text_classification_character_cnn.py) on characters
- [Digit recognition using a CNN](https://www.tensorflow.org/code/tensorflow/examples/learn/mnist.py)
- And much more!
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