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46
README.md
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@ -24,45 +24,34 @@ and discussion.**
# Download and Setup
To install TensorFlow using a binary package, see the instructions below. For
more detailed installation instructions, including installing from source, see
To install the CPU version of TensorFlow using a binary package, see the
instructions below. For more detailed installation instructions, including
installing from source, GPU-enabled support, etc., see
[here](tensorflow/g3doc/get_started/os_setup.md).
## Binary Installation
The TensorFlow Python API requires Python 2.7.
The simplest way to install TensorFlow is using
[pip](https://pypi.python.org/pypi/pip) for both Linux and Mac.
For the GPU-enabled version, or if you encounter installation errors, or for
more detailed installation instructions, see
[here](tensorflow/g3doc/get_started/os_setup.md#detailed_install).
### Ubuntu/Linux
Make sure you have [pip](https://pypi.python.org/pypi/pip) installed:
```sh
$ sudo apt-get install python-pip
```
Install TensorFlow:
```sh
```bash
# For CPU-only version
$ sudo pip install https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.5.0-cp27-none-linux_x86_64.whl
# For GPU-enabled version. See detailed install instructions
# for GPU configuration information.
$ sudo pip install https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.5.0-cp27-none-linux_x86_64.whl
$ pip install https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.5.0-cp27-none-linux_x86_64.whl
```
### Mac OS X
Make sure you have [pip](https://pypi.python.org/pypi/pip) installed:
If using `easy_install`:
```sh
$ sudo easy_install pip
```
Install TensorFlow (only CPU binary version is currently available).
```sh
$ sudo pip install https://storage.googleapis.com/tensorflow/mac/tensorflow-0.5.0-py2-none-any.whl
```bash
# Only CPU-version is available at the moment.
$ pip install https://storage.googleapis.com/tensorflow/mac/tensorflow-0.5.0-py2-none-any.whl
```
### Try your first TensorFlow program
@ -83,7 +72,6 @@ Hello, TensorFlow!
```
##For more information
* [TensorFlow website](http://tensorflow.org)

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tensorflow/g3doc/get_started/os_setup.md
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@ -1,90 +1,33 @@
# Download and Setup <a class="md-anchor" id="AUTOGENERATED-download-and-setup"></a>
You can install TensorFlow using our provided binary packages or from source.
## Binary Installation <a class="md-anchor" id="AUTOGENERATED-binary-installation"></a>
The TensorFlow Python API requires Python 2.7.
The simplest way to install TensorFlow is using
[pip](https://pypi.python.org/pypi/pip) for both Linux and Mac.
If you encounter installation errors, see [common problems](#common_problems)
for some solutions. To simplify installation, please consider using our
virtualenv-based instructions [here](#virtualenv_install).
### Ubuntu/Linux <a class="md-anchor" id="AUTOGENERATED-ubuntu-linux"></a>
**Note**: All the virtualenv-related instructions are optional, but we recommend
using the virtualenv on any multi-user system.
Make sure you have [pip](https://pypi.python.org/pypi/pip), the python headers,
and (optionally) [virtualenv](https://pypi.python.org/pypi/virtualenv) installed:
```bash
$ sudo apt-get install python-pip python-dev python-virtualenv
```
Set up a new virtualenv environment. To set it up in the
directory `~/tensorflow`, run:
```bash
$ virtualenv --system-site-packages ~/tensorflow
$ cd ~/tensorflow
```
Activate the virtualenv:
```bash
$ source bin/activate # If using bash
$ source bin/activate.csh # If using csh
(tensorflow)$ # Your prompt should change
```
Inside the virtualenv, install TensorFlow:
```bash
# For CPU-only version
(tensorflow)$ pip install --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.5.0-cp27-none-linux_x86_64.whl
$ pip install https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.5.0-cp27-none-linux_x86_64.whl
# For GPU-enabled version (only install this version if you have the CUDA sdk installed)
(tensorflow)$ pip install --upgrade https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.5.0-cp27-none-linux_x86_64.whl
# When you are done using TensorFlow:
(tensorflow)$ deactivate # Deactivate the virtualenv
$ # Your prompt should change back
$ pip install https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.5.0-cp27-none-linux_x86_64.whl
```
### Mac OS X <a class="md-anchor" id="AUTOGENERATED-mac-os-x"></a>
**Note**: All the virtualenv-related instructions are optional, but we recommend
using the virtualenv on any multi-user system.
Make sure you have [pip](https://pypi.python.org/pypi/pip) and
(optionally) [virtualenv](https://pypi.python.org/pypi/virtualenv) installed:
If using `easy_install`:
```bash
$ sudo easy_install pip # If pip is not already installed
$ sudo pip install --upgrade virtualenv
```
Set up a new virtualenv environment. Assuming you want to set it up in the
directory `~/tensorflow`, run:
```bash
$ virtualenv --system-site-packages ~/tensorflow
$ cd ~/tensorflow
```
Activate the virtualenv:
```bash
$ source bin/activate # If using bash
$ source bin/activate.csh # If using csh
(tensorflow)$ # Your prompt should change
```
Install TensorFlow (only CPU binary version is currently available).
```bash
(tensorflow)$ pip install --upgrade https://storage.googleapis.com/tensorflow/mac/tensorflow-0.5.0-py2-none-any.whl
# When you are done using TensorFlow:
(tensorflow)$ deactivate # Deactivate the virtualenv
$ # Your prompt should change back
# Only CPU-version is available at the moment.
$ pip install https://storage.googleapis.com/tensorflow/mac/tensorflow-0.5.0-py2-none-any.whl
```
## Docker-based installation <a class="md-anchor" id="AUTOGENERATED-docker-based-installation"></a>
@ -132,11 +75,10 @@ export CUDA_HOME=/usr/local/cuda
### Run TensorFlow <a class="md-anchor" id="AUTOGENERATED-run-tensorflow"></a>
First, activate the TensorFlow virtualenv, then open a python terminal:
Open a python terminal:
```bash
$ source ~/tensorflow/bin/activate # Assuming the tensorflow virtualenv is ~/tensorflow
(tensorflow)$ python
$ python
>>> import tensorflow as tf
>>> hello = tf.constant('Hello, TensorFlow!')
@ -332,3 +274,100 @@ Validation error: 7.0%
...
...
```
## VirtualEnv-based installation <a class="md-anchor" id="virtualenv_install"></a>
We recommend using [virtualenv](https://pypi.python.org/pypi/virtualenv) to
create an isolated container and install TensorFlow in that container -- it is
optional but makes verifying installation issues easier.
First, install all required tools:
```bash
# On Linux:
$ sudo apt-get install python-pip python-dev python-virtualenv
# On Mac:
$ sudo easy_install pip # If pip is not already installed
$ sudo pip install --upgrade virtualenv
```
Next, set up a new virtualenv environment. To set it up in the
directory `~/tensorflow`, run:
```bash
$ virtualenv --system-site-packages ~/tensorflow
$ cd ~/tensorflow
```
Then activate the virtualenv:
```bash
$ source bin/activate # If using bash
$ source bin/activate.csh # If using csh
(tensorflow)$ # Your prompt should change
```
Inside the virtualenv, install TensorFlow:
```bash
(tensorflow)$ pip install --upgrade <$url_to_binary.whl>
```
You can then run your TensorFlow program like:
```bash
(tensorflow)$ python tensorflow/models/image/mnist/convolutional.py
# When you are done using TensorFlow:
(tensorflow)$ deactivate # Deactivate the virtualenv
$ # Your prompt should change back
```
## Common Problems <a class="md-anchor" id="AUTOGENERATED-common-problems"></a>
### GPU-related issues <a class="md-anchor" id="AUTOGENERATED-gpu-related-issues"></a>
If you encounter the following when trying to run a TensorFlow program:
```python
ImportError: libcudart.so.7.0: cannot open shared object file: No such file or directory
```
Make sure you followed the the GPU installation [instructions](#install_cuda).
### On Linux <a class="md-anchor" id="AUTOGENERATED-on-linux"></a>
If you encounter:
```python
...
"__add__", "__radd__",
^
SyntaxError: invalid syntax
```
Solution: make sure you are using Python 2.7.
### On MacOSX <a class="md-anchor" id="AUTOGENERATED-on-macosx"></a>
If you encounter:
```python
import six.moves.copyreg as copyreg
ImportError: No module named copyreg
```
Solution: TensorFlow depends on protobuf which require six-1.10.0. The
installation on some machines may only have an earlier version of six that was
installed using distutils. Unfortunately, upgrading a distutils installed
project via `pip` is deprecated and may fail. If you having difficulty
upgrading six, we recommend playing around with tensorflow using virtualenv.

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@ -1,7 +1,7 @@
# Overview <a class="md-anchor" id="AUTOGENERATED-overview"></a>
# Overview
## Variables: Creation, Initializing, Saving, and Restoring <a class="md-anchor" id="AUTOGENERATED-variables--creation--initializing--saving--and-restoring"></a>
## Variables: Creation, Initializing, Saving, and Restoring
TensorFlow Variables are in-memory buffers containing tensors. Learn how to
use them to hold and update model parameters during training.
@ -9,7 +9,7 @@ use them to hold and update model parameters during training.
[View Tutorial](../how_tos/variables/index.md)
## TensorFlow Mechanics 101 <a class="md-anchor" id="AUTOGENERATED-tensorflow-mechanics-101"></a>
## TensorFlow Mechanics 101
A step-by-step walk through of the details of using TensorFlow infrastructure
to train models at scale, using MNIST handwritten digit recognition as a toy
@ -18,7 +18,7 @@ example.
[View Tutorial](../tutorials/mnist/tf/index.md)
## TensorBoard: Visualizing Learning <a class="md-anchor" id="AUTOGENERATED-tensorboard--visualizing-learning"></a>
## TensorBoard: Visualizing Learning
TensorBoard is a useful tool for visualizing the training and evaluation of
your model(s). This tutorial describes how to build and run TensorBoard as well
@ -28,7 +28,7 @@ TensorBoard uses for display.
[View Tutorial](../how_tos/summaries_and_tensorboard/index.md)
## TensorBoard: Graph Visualization <a class="md-anchor" id="AUTOGENERATED-tensorboard--graph-visualization"></a>
## TensorBoard: Graph Visualization
This tutorial describes how to use the graph visualizer in TensorBoard to help
you understand the dataflow graph and debug it.
@ -36,7 +36,7 @@ you understand the dataflow graph and debug it.
[View Tutorial](../how_tos/graph_viz/index.md)
## Reading Data <a class="md-anchor" id="AUTOGENERATED-reading-data"></a>
## Reading Data
This tutorial describes the three main methods of getting data into your
TensorFlow program: Feeding, Reading and Preloading.
@ -44,7 +44,7 @@ TensorFlow program: Feeding, Reading and Preloading.
[View Tutorial](../how_tos/reading_data/index.md)
## Threading and Queues <a class="md-anchor" id="AUTOGENERATED-threading-and-queues"></a>
## Threading and Queues
This tutorial describes the various constructs implemented by TensorFlow
to facilitate asynchronous and concurrent training.
@ -52,7 +52,7 @@ to facilitate asynchronous and concurrent training.
[View Tutorial](../how_tos/threading_and_queues/index.md)
## Adding a New Op <a class="md-anchor" id="AUTOGENERATED-adding-a-new-op"></a>
## Adding a New Op
TensorFlow already has a large suite of node operations from which you can
compose in your graph, but here are the details of how to add you own custom Op.
@ -60,7 +60,7 @@ compose in your graph, but here are the details of how to add you own custom Op.
[View Tutorial](../how_tos/adding_an_op/index.md)
## Custom Data Readers <a class="md-anchor" id="AUTOGENERATED-custom-data-readers"></a>
## Custom Data Readers
If you have a sizable custom data set, you may want to consider extending
TensorFlow to read your data directly in it's native format. Here's how.
@ -68,14 +68,14 @@ TensorFlow to read your data directly in it's native format. Here's how.
[View Tutorial](../how_tos/new_data_formats/index.md)
## Using GPUs <a class="md-anchor" id="AUTOGENERATED-using-gpus"></a>
## Using GPUs
This tutorial describes how to construct and execute models on GPU(s).
[View Tutorial](../how_tos/using_gpu/index.md)
## Sharing Variables <a class="md-anchor" id="AUTOGENERATED-sharing-variables"></a>
## Sharing Variables
When deploying large models on multiple GPUs, or when unrolling complex LSTMs
or RNNs, it is often necessary to access the same Variable objects from

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@ -0,0 +1,11 @@
# TensorFlow
* [Home][home]
* [Getting Started](/get_started/index.md)
* [Mechanics](/how_tos/index.md)
* [Tutorials](/tutorials/index.md)
* [Python API](/api_docs/python/index.md)
* [C++ API](/api_docs/cc/index.md)
* [Other Resources](/resources/index.md)
[home]: /index.md

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tensorflow/g3doc/resources/dims_types.md
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@ -15,7 +15,7 @@ Python list) has a rank of 2:
t = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
A rank two tensor is what we typically think of as a matrix, a rank on tensor
A rank two tensor is what we typically think of as a matrix, a rank one tensor
is a vector. For a rank two tensor you can acccess any element with the syntax
`t[i, j]`. For a rank three tensor you would need to address an element with
't[i, j, k]'.
@ -65,4 +65,3 @@ Data type | Python type | Description
`DT_QINT32` | `tf.qint32` | 32 bits signed integer used in quantized Ops.
`DT_QINT8` | `tf.qint8` | 8 bits signed integer used in quantized Ops.
`DT_QUINT8` | `tf.quint8` | 8 bits unsigned integer used in quantized Ops.

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@ -6,7 +6,7 @@
Additional details about the TensorFlow programming model and the underlying
implementation can be found in out white paper:
* [TensorFlow: Large-scale machine learning on heterogeneous systems](http://tensorflow.org/tensorflow-whitepaper2015.pdf)
* [TensorFlow: Large-scale machine learning on heterogeneous systems](http://www.tensorflow.org/whitepaper2015.pdf)
### Citation <a class="md-anchor" id="AUTOGENERATED-citation"></a>

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tensorflow/g3doc/tutorials/index.md
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@ -1,7 +1,7 @@
# Overview <a class="md-anchor" id="AUTOGENERATED-overview"></a>
# Overview
## MNIST For ML Beginners <a class="md-anchor" id="AUTOGENERATED-mnist-for-ml-beginners"></a>
## MNIST For ML Beginners
If you're new to machine learning, we recommend starting here. You'll learn
about a classic problem, handwritten digit classification (MNIST), and get a
@ -10,7 +10,7 @@ gentle introduction to multiclass classification.
[View Tutorial](../tutorials/mnist/beginners/index.md)
## Deep MNIST for Experts <a class="md-anchor" id="AUTOGENERATED-deep-mnist-for-experts"></a>
## Deep MNIST for Experts
If you're already familiar with other deep learning software packages, and are
already familiar with MNIST, this tutorial with give you a very brief primer on
@ -19,7 +19,7 @@ TensorFlow.
[View Tutorial](../tutorials/mnist/pros/index.md)
## TensorFlow Mechanics 101 <a class="md-anchor" id="AUTOGENERATED-tensorflow-mechanics-101"></a>
## TensorFlow Mechanics 101
This is a technical tutorial, where we walk you through the details of using
TensorFlow infrastructure to train models at scale. We use again MNIST as the
@ -28,7 +28,7 @@ example.
[View Tutorial](../tutorials/mnist/tf/index.md)
## Convolutional Neural Networks <a class="md-anchor" id="AUTOGENERATED-convolutional-neural-networks"></a>
## Convolutional Neural Networks
An introduction to convolutional neural networks using the CIFAR-10 data set.
Convolutional neural nets are particularly tailored to images, since they
@ -38,7 +38,7 @@ representations of visual content.
[View Tutorial](../tutorials/deep_cnn/index.md)
## Vector Representations of Words <a class="md-anchor" id="AUTOGENERATED-vector-representations-of-words"></a>
## Vector Representations of Words
This tutorial motivates why it is useful to learn to represent words as vectors
(called *word embeddings*). It introduces the word2vec model as an efficient
@ -49,7 +49,7 @@ embeddings).
[View Tutorial](../tutorials/word2vec/index.md)
## Recurrent Neural Networks <a class="md-anchor" id="AUTOGENERATED-recurrent-neural-networks"></a>
## Recurrent Neural Networks
An introduction to RNNs, wherein we train an LSTM network to predict the next
word in an English sentence. (A task sometimes called language modeling.)
@ -57,7 +57,7 @@ word in an English sentence. (A task sometimes called language modeling.)
[View Tutorial](../tutorials/recurrent/index.md)
## Sequence-to-Sequence Models <a class="md-anchor" id="AUTOGENERATED-sequence-to-sequence-models"></a>
## Sequence-to-Sequence Models
A follow on to the RNN tutorial, where we assemble a sequence-to-sequence model
for machine translation. You will learn to build your own English-to-French
@ -66,7 +66,7 @@ translator, entirely machine learned, end-to-end.
[View Tutorial](../tutorials/seq2seq/index.md)
## Mandelbrot Set <a class="md-anchor" id="AUTOGENERATED-mandelbrot-set"></a>
## Mandelbrot Set
TensorFlow can be used for computation that has nothing to do with machine
learning. Here's a naive implementation of Mandelbrot set visualization.
@ -74,7 +74,7 @@ learning. Here's a naive implementation of Mandelbrot set visualization.
[View Tutorial](../tutorials/mandelbrot/index.md)
## Partial Differential Equations <a class="md-anchor" id="AUTOGENERATED-partial-differential-equations"></a>
## Partial Differential Equations
As another example of non-machine learning computation, we offer an example of
a naive PDE simulation of raindrops landing on a pond.
@ -82,7 +82,7 @@ a naive PDE simulation of raindrops landing on a pond.
[View Tutorial](../tutorials/pdes/index.md)
## MNIST Data Download <a class="md-anchor" id="AUTOGENERATED-mnist-data-download"></a>
## MNIST Data Download
Details about downloading the MNIST handwritten digits data set. Exciting
stuff.
@ -90,7 +90,7 @@ stuff.
[View Tutorial](../tutorials/mnist/download/index.md)
## Visual Object Recognition <a class="md-anchor" id="AUTOGENERATED-visual-object-recognition"></a>
## Visual Object Recognition
We will be releasing our state-of-the-art Inception object recognition model,
complete and already trained.
@ -98,7 +98,7 @@ complete and already trained.
COMING SOON
## Deep Dream Visual Hallucinations <a class="md-anchor" id="AUTOGENERATED-deep-dream-visual-hallucinations"></a>
## Deep Dream Visual Hallucinations
Building on the Inception recognition model, we will release a TensorFlow
version of the [Deep Dream](https://github.com/google/deepdream) neural network

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@ -106,7 +106,7 @@ P(w_t | h) &= \text{softmax}(\exp \{ \text{score}(w_t, h) \}) \\
\end{align}
$$
where \\(\text{score}(w_t, h)\\) computes the compatibility of word \\(w_t\\) with
where \\( \text{score}(w_t, h) \\) computes the compatibility of word \\(w_t\\) with
the context \\(h\\) (a dot product is commonly used). We train this model by
maximizing its log-likelihood on the training set, i.e. by maximizing