48c3a3c8f4
Autograd can automatically differentiate native Python and Numpy code. It can handle a large subset of Python's features, including loops, ifs, recursion and closures, and it can even take derivatives of derivatives of derivatives. It uses reverse-mode differentiation (a.k.a. backpropagation), which means it can efficiently take gradients of scalar-valued functions with respect to array-valued arguments. The main intended application is gradient-based optimization.
81 lines
3.3 KiB
Text
81 lines
3.3 KiB
Text
@comment $NetBSD: PLIST,v 1.1 2016/08/24 23:50:12 markd Exp $
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${PYSITELIB}/${EGG_INFODIR}/PKG-INFO
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${PYSITELIB}/${EGG_INFODIR}/SOURCES.txt
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${PYSITELIB}/${EGG_INFODIR}/dependency_links.txt
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${PYSITELIB}/${EGG_INFODIR}/requires.txt
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${PYSITELIB}/${EGG_INFODIR}/top_level.txt
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${PYSITELIB}/autograd/__init__.py
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${PYSITELIB}/autograd/__init__.pyc
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${PYSITELIB}/autograd/__init__.pyo
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${PYSITELIB}/autograd/container_types.py
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${PYSITELIB}/autograd/container_types.pyc
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${PYSITELIB}/autograd/container_types.pyo
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${PYSITELIB}/autograd/convenience_wrappers.py
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${PYSITELIB}/autograd/convenience_wrappers.pyc
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${PYSITELIB}/autograd/convenience_wrappers.pyo
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${PYSITELIB}/autograd/core.py
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${PYSITELIB}/autograd/core.pyc
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${PYSITELIB}/autograd/core.pyo
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${PYSITELIB}/autograd/numpy/__init__.py
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${PYSITELIB}/autograd/numpy/__init__.pyc
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${PYSITELIB}/autograd/numpy/__init__.pyo
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${PYSITELIB}/autograd/numpy/complex_array_node.py
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${PYSITELIB}/autograd/numpy/complex_array_node.pyc
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${PYSITELIB}/autograd/numpy/complex_array_node.pyo
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${PYSITELIB}/autograd/numpy/fft.py
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${PYSITELIB}/autograd/numpy/fft.pyc
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${PYSITELIB}/autograd/numpy/fft.pyo
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${PYSITELIB}/autograd/numpy/gpu_array_node.py
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${PYSITELIB}/autograd/numpy/gpu_array_node.pyc
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${PYSITELIB}/autograd/numpy/gpu_array_node.pyo
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${PYSITELIB}/autograd/numpy/linalg.py
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${PYSITELIB}/autograd/numpy/linalg.pyc
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${PYSITELIB}/autograd/numpy/linalg.pyo
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${PYSITELIB}/autograd/numpy/numpy_extra.py
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${PYSITELIB}/autograd/numpy/numpy_extra.pyc
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${PYSITELIB}/autograd/numpy/numpy_extra.pyo
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${PYSITELIB}/autograd/numpy/numpy_grads.py
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${PYSITELIB}/autograd/numpy/numpy_grads.pyc
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${PYSITELIB}/autograd/numpy/numpy_grads.pyo
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${PYSITELIB}/autograd/numpy/numpy_wrapper.py
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${PYSITELIB}/autograd/numpy/numpy_wrapper.pyc
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${PYSITELIB}/autograd/numpy/numpy_wrapper.pyo
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${PYSITELIB}/autograd/numpy/random.py
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${PYSITELIB}/autograd/numpy/random.pyc
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${PYSITELIB}/autograd/numpy/random.pyo
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${PYSITELIB}/autograd/numpy/use_gpu_numpy.py
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${PYSITELIB}/autograd/numpy/use_gpu_numpy.pyc
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${PYSITELIB}/autograd/numpy/use_gpu_numpy.pyo
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${PYSITELIB}/autograd/scipy/__init__.py
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${PYSITELIB}/autograd/scipy/__init__.pyc
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${PYSITELIB}/autograd/scipy/__init__.pyo
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${PYSITELIB}/autograd/scipy/linalg.py
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${PYSITELIB}/autograd/scipy/linalg.pyc
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${PYSITELIB}/autograd/scipy/linalg.pyo
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${PYSITELIB}/autograd/scipy/misc.py
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${PYSITELIB}/autograd/scipy/misc.pyc
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${PYSITELIB}/autograd/scipy/misc.pyo
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${PYSITELIB}/autograd/scipy/signal.py
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${PYSITELIB}/autograd/scipy/signal.pyc
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${PYSITELIB}/autograd/scipy/signal.pyo
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${PYSITELIB}/autograd/scipy/special.py
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${PYSITELIB}/autograd/scipy/special.pyc
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${PYSITELIB}/autograd/scipy/special.pyo
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${PYSITELIB}/autograd/scipy/stats/__init__.py
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${PYSITELIB}/autograd/scipy/stats/__init__.pyc
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${PYSITELIB}/autograd/scipy/stats/__init__.pyo
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${PYSITELIB}/autograd/scipy/stats/dirichlet.py
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${PYSITELIB}/autograd/scipy/stats/dirichlet.pyc
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${PYSITELIB}/autograd/scipy/stats/dirichlet.pyo
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${PYSITELIB}/autograd/scipy/stats/multivariate_normal.py
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${PYSITELIB}/autograd/scipy/stats/multivariate_normal.pyc
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${PYSITELIB}/autograd/scipy/stats/multivariate_normal.pyo
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${PYSITELIB}/autograd/scipy/stats/norm.py
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${PYSITELIB}/autograd/scipy/stats/norm.pyc
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${PYSITELIB}/autograd/scipy/stats/norm.pyo
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${PYSITELIB}/autograd/scipy/stats/t.py
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${PYSITELIB}/autograd/scipy/stats/t.pyc
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${PYSITELIB}/autograd/scipy/stats/t.pyo
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${PYSITELIB}/autograd/util.py
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${PYSITELIB}/autograd/util.pyc
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${PYSITELIB}/autograd/util.pyo
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