Metadata-Version: 1.1
Name: nengo-dl
Version: 0.3.0
Summary: Deep learning integration for Nengo
Home-page: https://github.com/nengo/nengo_dl
Author: Daniel Rasmussen
Author-email: daniel.rasmussen@appliedbrainresearch.com
License: Free for non-commercial use
Description-Content-Type: UNKNOWN
Description: ********************************************
        NengoDL: Deep learning integration for Nengo
        ********************************************
        
        NengoDL is a simulator for `Nengo <https://pythonhosted.org/nengo/>`_ models.
        That means it takes a Nengo network as input, and allows the user to simulate
        that network using some underlying computational framework (in this case,
        TensorFlow).
        
        In practice, what that means is that the code for constructing a Nengo model
        is exactly the same as it would be for the standard Nengo simulator.  All that
        changes is that we use a different Simulator class to execute the
        model.
        
        For example:
        
        .. code-block:: python
        
            import nengo
            import nengo_dl
            import numpy as np
        
            with nengo.Network() as net:
                inp = nengo.Node(output=np.sin)
                ens = nengo.Ensemble(50, 1, neuron_type=nengo.LIF())
                nengo.Connection(inp, ens, synapse=0.1)
                p = nengo.Probe(ens)
        
            with nengo_dl.Simulator(net) as sim: # this is the only line that changes
                sim.run(1.0)
        
            print(sim.data[p])
        
        However, NengoDL is not simply a duplicate of the Nengo simulator.  It also
        adds a number of unique features, such as:
        
        - optimizing the parameters of a model through deep learning
          training methods
        - faster simulation speed, on both CPU and GPU
        - inserting networks defined using TensorFlow (such as
          convolutional neural networks) directly into a Nengo model
        
        More details can be found in the `NengoDL documentation
        <https://nengo.github.io/nengo_dl/>`_.
        
        Installation
        ============
        
        Installation instructions can be found `here
        <https://nengo.github.io/nengo_dl/installation.html>`_.
        
        Release History
        ===============
        
        .. Changelog entries should follow this format:
        
           version (release date)
           ----------------------
        
           **section**
        
           - One-line description of change (link to Github issue/PR)
        
        .. Changes should be organized in one of several sections:
        
           - Added
           - Changed
           - Deprecated
           - Removed
           - Fixed
        
        0.3.0 (April 25, 2017)
        ----------------------
        
        **Added**
        
        - Use logger for debug/builder output
        - Implemented TensorFlow gradients for sparse Variable update Ops, to allow
          models with those elements to be trained
        - Added tutorial/examples on using ``Simulator.train``
        - Added support for training models when ``unroll_simulation=False``
        - Compatibility changes for Nengo 2.4.0
        - Added a new graph planner algorithm, which can improve simulation speed at
          the cost of build time
        
        **Changed**
        
        - Significant improvements to simulation speed
        
          - Use sparse Variable updates for signals.scatter/gather
          - Improved graph optimizer memory organization
          - Implemented sparse matrix multiplication op, to allow more aggressive
            merging of DotInc operators
        
        - Significant improvements to build speed
        
          - Added early termination to graph optimization
          - Algorithmic improvements to graph optimization functions
        
        - Reorganized documentation to more clearly direct new users to relevant
          material
        
        **Fixed**
        
        - Fix bug where passing a built model to the Simulator more than once would
          result in an error
        - Cache result of calls to ``tensor_graph.build_loss/build_optimizer``, so that
          we don't unnecessarily create duplicate elements in the graph on repeated
          calls
        - Fix support for Variables on GPU when ``unroll_simulation=False``
        - SimPyFunc operators will always be assigned to CPU, even when
          ``device="/gpu:0"``, since there is no GPU kernel
        - Fix bug where ``Simulator.loss`` was not being computed correctly for
          models with internal state
        - Data/targets passed to ``Simulator.train`` will be truncated if not evenly
          divisible by the specified minibatch size
        - Fixed bug where in some cases Nodes with side effects would not be run if
          their output was not used in the simulation
        - Fixed bug where strided reads that cover a full array would be interpreted as
          non-strided reads of the full array
        
        
        0.2.0 (March 13, 2017)
        ----------------------
        
        Initial release of TensorFlow-based NengoDL
        
        0.1.0 (June 12, 2016)
        ---------------------
        
        Initial release of Lasagne-based NengoDL
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: License :: Free for non-commercial use
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: Unix
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Topic :: Scientific/Engineering
