Set TensorFlow as default backend for new installs
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Francois Chollet
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25
README.md
25
README.md
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# Keras: Deep Learning library for Theano and TensorFlow
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# Keras: Deep Learning library for TensorFlow and Theano
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[](https://travis-ci.org/fchollet/keras)
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[](https://badge.fury.io/py/keras)
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## You have just found Keras.
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Keras is a minimalist, highly modular neural networks library, written in Python and capable of running on top of either [TensorFlow](https://github.com/tensorflow/tensorflow) or [Theano](https://github.com/Theano/Theano). It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.
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Keras is a minimalist, highly modular neural networks library, written in Python and capable of running on top of either [TensorFlow](https://github.com/tensorflow/tensorflow) or [Theano](https://github.com/Theano/Theano). It was developed with a focus on enabling fast experimentation. *Being able to go from idea to result with the least possible delay is key to doing good research.*
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Use Keras if you need a deep learning library that:
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- allows for easy and fast prototyping (through total modularity, minimalism, and extensibility).
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- supports both convolutional networks and recurrent networks, as well as combinations of the two.
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- supports arbitrary connectivity schemes (including multi-input and multi-output training).
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- runs seamlessly on CPU and GPU.
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- Allows for easy and fast prototyping (through total modularity, minimalism, and extensibility).
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- Supports both convolutional networks and recurrent networks, as well as combinations of the two.
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- Supports arbitrary connectivity schemes (including multi-input and multi-output training).
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- Runs seamlessly on CPU and GPU.
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Read the documentation at [Keras.io](http://keras.io).
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@ -114,16 +114,17 @@ Keras uses the following dependencies:
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- HDF5 and h5py (optional, required if you use model saving/loading functions)
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- Optional but recommended if you use CNNs: cuDNN.
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*When using the Theano backend:*
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- Theano
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- [See installation instructions](http://deeplearning.net/software/theano/install.html#install).
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*When using the TensorFlow backend:*
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- TensorFlow
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- [See installation instructions](https://github.com/tensorflow/tensorflow#download-and-setup).
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*When using the Theano backend:*
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- Theano
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- [See installation instructions](http://deeplearning.net/software/theano/install.html#install).
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To install Keras, `cd` to the Keras folder and run the install command:
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```sh
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sudo python setup.py install
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@ -137,9 +138,9 @@ sudo pip install keras
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------------------
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## Switching from Theano to TensorFlow
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## Switching from TensorFlow to Theano
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By default, Keras will use Theano as its tensor manipulation library. [Follow these instructions](http://keras.io/backend/) to configure the Keras backend.
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By default, Keras will use TensorFlow as its tensor manipulation library. [Follow these instructions](http://keras.io/backend/) to configure the Keras backend.
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------------------
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20
docs/templates/backend.md
vendored
20
docs/templates/backend.md
vendored
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Keras is a model-level library, providing high-level building blocks for developing deep learning models. It does not handle itself low-level operations such as tensor products, convolutions and so on. Instead, it relies on a specialized, well-optimized tensor manipulation library to do so, serving as the "backend engine" of Keras. Rather than picking one single tensor library and making the implementation of Keras tied to that library, Keras handles the problem in a modular way, and several different backend engines can be plugged seamlessly into Keras.
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At this time, Keras has two backend implementations available: the **Theano** backend and the **TensorFlow** backend.
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At this time, Keras has two backend implementations available: the **TensorFlow** backend and the **Theano** backend.
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- [Theano](http://deeplearning.net/software/theano/) is an open-source symbolic tensor manipulation framework developed by LISA/MILA Lab at Université de Montréal.
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- [TensorFlow](http://www.tensorflow.org/) is an open-source symbolic tensor manipulation framework developed by Google, Inc.
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- [Theano](http://deeplearning.net/software/theano/) is an open-source symbolic tensor manipulation framework developed by LISA/MILA Lab at Université de Montréal.
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In the future, we are likely to add more backend options. If you are interested in developing a new backend, get in touch!
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----
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@ -19,9 +21,16 @@ If you have run Keras at least once, you will find the Keras configuration file
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If it isn't there, you can create it.
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It probably looks like this:
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The default configuration file looks like this:
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`{"epsilon": 1e-07, "floatx": "float32", "backend": "theano"}`
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```
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{
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"image_dim_ordering": "tf",
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"epsilon": 1e-07,
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"floatx": "float32",
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"backend": "tensorflow"
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}
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```
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Simply change the field `backend` to either `"theano"` or `"tensorflow"`, and Keras will use the new configuration next time you run any Keras code.
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@ -29,9 +38,8 @@ You can also define the environment variable ``KERAS_BACKEND`` and this will
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override what is defined in your config file :
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```bash
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KERAS_BACKEND=tensorflow python -c "from keras import backend; print(backend._BACKEND)"
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KERAS_BACKEND=tensorflow python -c "from keras import backend"
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Using TensorFlow backend.
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tensorflow
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```
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----
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29
docs/templates/index.md
vendored
29
docs/templates/index.md
vendored
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## You have just found Keras.
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Keras is a minimalist, highly modular neural networks library, written in Python and capable of running on top of either [TensorFlow](https://github.com/tensorflow/tensorflow) or [Theano](https://github.com/Theano/Theano). It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.
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Keras is a minimalist, highly modular neural networks library, written in Python and capable of running on top of either [TensorFlow](https://github.com/tensorflow/tensorflow) or [Theano](https://github.com/Theano/Theano). It was developed with a focus on enabling fast experimentation. *Being able to go from idea to result with the least possible delay is key to doing good research.*
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Use Keras if you need a deep learning library that:
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- allows for easy and fast prototyping (through total modularity, minimalism, and extensibility).
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- supports both convolutional networks and recurrent networks, as well as combinations of the two.
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- supports arbitrary connectivity schemes (including multi-input and multi-output training).
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- runs seamlessly on CPU and GPU.
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- Allows for easy and fast prototyping (through total modularity, minimalism, and extensibility).
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- Supports both convolutional networks and recurrent networks, as well as combinations of the two.
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- Supports arbitrary connectivity schemes (including multi-input and multi-output training).
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- Runs seamlessly on CPU and GPU.
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Read the documentation at [Keras.io](http://keras.io).
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@ -33,7 +33,6 @@ Keras is compatible with: __Python 2.7-3.5__.
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------------------
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## Getting started: 30 seconds to Keras
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The core data structure of Keras is a __model__, a way to organize layers. The main type of model is the [`Sequential`](http://keras.io/getting-started/sequential-model-guide) model, a linear stack of layers. For more complex architectures, you should use the [Keras functional API](http://keras.io/getting-started/functional-api-guide).
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@ -98,6 +97,7 @@ For a more in-depth tutorial about Keras, you can check out:
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In the [examples folder](https://github.com/fchollet/keras/tree/master/examples) of the repository, you will find more advanced models: question-answering with memory networks, text generation with stacked LSTMs, etc.
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------------------
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@ -110,32 +110,33 @@ Keras uses the following dependencies:
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- HDF5 and h5py (optional, required if you use model saving/loading functions)
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- Optional but recommended if you use CNNs: cuDNN.
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*When using the Theano backend:*
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- Theano
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- [See installation instructions](http://deeplearning.net/software/theano/install.html#install).
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*When using the TensorFlow backend:*
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- TensorFlow
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- [See installation instructions](https://github.com/tensorflow/tensorflow#download-and-setup).
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*When using the Theano backend:*
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- Theano
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- [See installation instructions](http://deeplearning.net/software/theano/install.html#install).
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To install Keras, `cd` to the Keras folder and run the install command:
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```
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```sh
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sudo python setup.py install
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```
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You can also install Keras from PyPI:
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```
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```sh
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sudo pip install keras
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```
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------------------
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## Switching from Theano to TensorFlow
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## Switching from TensorFlow to Theano
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By default, Keras will use Theano as its tensor manipulation library. [Follow these instructions](http://keras.io/backend/) to configure the Keras backend.
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By default, Keras will use TensorFlow as its tensor manipulation library. [Follow these instructions](http://keras.io/backend/) to configure the Keras backend.
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------------------
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@ -23,7 +23,7 @@ _keras_dir = os.path.join(_keras_base_dir, '.keras')
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if not os.path.exists(_keras_dir):
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os.makedirs(_keras_dir)
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_BACKEND = 'theano'
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_BACKEND = 'tensorflow'
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_config_path = os.path.expanduser(os.path.join(_keras_dir, 'keras.json'))
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if os.path.exists(_config_path):
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_config = json.load(open(_config_path))
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@ -6,7 +6,7 @@ from collections import defaultdict
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_FLOATX = 'float32'
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_EPSILON = 10e-8
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_UID_PREFIXES = defaultdict(int)
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_IMAGE_DIM_ORDERING = 'th'
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_IMAGE_DIM_ORDERING = 'tf'
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_LEGACY_WEIGHT_ORDERING = False
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