%global _empty_manifest_terminate_build 0 Name: python-nn Version: 0.1.1 Release: 1 Summary: A neural network library built on top of TensorFlow for quickly building deep learning models. License: MIT URL: https://github.com/marella/nn Source0: https://mirrors.aliyun.com/pypi/web/packages/b3/2a/b00995cba3fda79210c0002355925b45a3abf882c2b3c42b5275dc6708df/nn-0.1.1.tar.gz BuildArch: noarch %description A neural network library built on top of TensorFlow for quickly building deep learning models. [![Build Status](https://travis-ci.org/marella/nn.svg?branch=master)](https://travis-ci.org/marella/nn) ## Usage `nn.Tensor` is the core data structure which is a wrapper for `tf.Tensor` and provides additional functionality. It can be created using the `nn.tensor()` function: ```py import nn a = nn.tensor([1, 2, 3]) assert isinstance(a, nn.Tensor) assert a.shape == (3, ) ``` It supports method chaining: ```py c = a.square().sum() assert c.numpy() == 14 ``` and can be used with `tf.Tensor` objects: ```py import tensorflow as tf b = tf.constant(2) c = (a - b).square().sum() assert c.numpy() == 2 ``` It can also be used with high level APIs such as `tf.keras`: ```py model = nn.Sequential([ nn.Dense(128, activation='relu'), nn.Dropout(0.2), nn.Dense(10) ]) y = model(x) assert isinstance(y, nn.Tensor) ``` and to perform automatic differentiation and optimization: ```py optimizer = nn.Adam() with nn.GradientTape() as tape: outputs = model(inputs) loss = (targets - outputs).square().mean() grads = tape.gradient(loss, model.trainable_variables) optimizer.apply_gradients(zip(grads, model.trainable_variables)) ``` To use it with ops that expect `tf.Tensor` objects as inputs, wrap the ops using `nn.op()`: ```py mean = nn.op(tf.reduce_mean) c = mean(a) assert isinstance(c, nn.Tensor) maximum = nn.op(tf.maximum, binary=True) c = maximum(a, b) assert isinstance(c, nn.Tensor) ``` or convert it to a `tf.Tensor` object using the `tf()` method or `nn.tf()` function: ```py b = a.tf() assert isinstance(b, tf.Tensor) b = nn.tf(a) assert isinstance(b, tf.Tensor) ``` See more examples [here][examples]. ## Installation Requirements: - TensorFlow >= 2.0 - Python >= 3.6 Install from PyPI (recommended): ```sh pip install nn ``` Alternatively, install from source: ```sh git clone https://github.com/marella/nn.git cd nn pip install -e . ``` [TensorFlow] should be installed separately. ## Testing To run tests, install dependencies: ```sh pip install -e .[tests] ``` and run: ```sh pytest tests ``` [tensorflow]: https://www.tensorflow.org/install [examples]: https://github.com/marella/train/tree/master/examples %package -n python3-nn Summary: A neural network library built on top of TensorFlow for quickly building deep learning models. Provides: python-nn BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-nn A neural network library built on top of TensorFlow for quickly building deep learning models. [![Build Status](https://travis-ci.org/marella/nn.svg?branch=master)](https://travis-ci.org/marella/nn) ## Usage `nn.Tensor` is the core data structure which is a wrapper for `tf.Tensor` and provides additional functionality. It can be created using the `nn.tensor()` function: ```py import nn a = nn.tensor([1, 2, 3]) assert isinstance(a, nn.Tensor) assert a.shape == (3, ) ``` It supports method chaining: ```py c = a.square().sum() assert c.numpy() == 14 ``` and can be used with `tf.Tensor` objects: ```py import tensorflow as tf b = tf.constant(2) c = (a - b).square().sum() assert c.numpy() == 2 ``` It can also be used with high level APIs such as `tf.keras`: ```py model = nn.Sequential([ nn.Dense(128, activation='relu'), nn.Dropout(0.2), nn.Dense(10) ]) y = model(x) assert isinstance(y, nn.Tensor) ``` and to perform automatic differentiation and optimization: ```py optimizer = nn.Adam() with nn.GradientTape() as tape: outputs = model(inputs) loss = (targets - outputs).square().mean() grads = tape.gradient(loss, model.trainable_variables) optimizer.apply_gradients(zip(grads, model.trainable_variables)) ``` To use it with ops that expect `tf.Tensor` objects as inputs, wrap the ops using `nn.op()`: ```py mean = nn.op(tf.reduce_mean) c = mean(a) assert isinstance(c, nn.Tensor) maximum = nn.op(tf.maximum, binary=True) c = maximum(a, b) assert isinstance(c, nn.Tensor) ``` or convert it to a `tf.Tensor` object using the `tf()` method or `nn.tf()` function: ```py b = a.tf() assert isinstance(b, tf.Tensor) b = nn.tf(a) assert isinstance(b, tf.Tensor) ``` See more examples [here][examples]. ## Installation Requirements: - TensorFlow >= 2.0 - Python >= 3.6 Install from PyPI (recommended): ```sh pip install nn ``` Alternatively, install from source: ```sh git clone https://github.com/marella/nn.git cd nn pip install -e . ``` [TensorFlow] should be installed separately. ## Testing To run tests, install dependencies: ```sh pip install -e .[tests] ``` and run: ```sh pytest tests ``` [tensorflow]: https://www.tensorflow.org/install [examples]: https://github.com/marella/train/tree/master/examples %package help Summary: Development documents and examples for nn Provides: python3-nn-doc %description help A neural network library built on top of TensorFlow for quickly building deep learning models. [![Build Status](https://travis-ci.org/marella/nn.svg?branch=master)](https://travis-ci.org/marella/nn) ## Usage `nn.Tensor` is the core data structure which is a wrapper for `tf.Tensor` and provides additional functionality. It can be created using the `nn.tensor()` function: ```py import nn a = nn.tensor([1, 2, 3]) assert isinstance(a, nn.Tensor) assert a.shape == (3, ) ``` It supports method chaining: ```py c = a.square().sum() assert c.numpy() == 14 ``` and can be used with `tf.Tensor` objects: ```py import tensorflow as tf b = tf.constant(2) c = (a - b).square().sum() assert c.numpy() == 2 ``` It can also be used with high level APIs such as `tf.keras`: ```py model = nn.Sequential([ nn.Dense(128, activation='relu'), nn.Dropout(0.2), nn.Dense(10) ]) y = model(x) assert isinstance(y, nn.Tensor) ``` and to perform automatic differentiation and optimization: ```py optimizer = nn.Adam() with nn.GradientTape() as tape: outputs = model(inputs) loss = (targets - outputs).square().mean() grads = tape.gradient(loss, model.trainable_variables) optimizer.apply_gradients(zip(grads, model.trainable_variables)) ``` To use it with ops that expect `tf.Tensor` objects as inputs, wrap the ops using `nn.op()`: ```py mean = nn.op(tf.reduce_mean) c = mean(a) assert isinstance(c, nn.Tensor) maximum = nn.op(tf.maximum, binary=True) c = maximum(a, b) assert isinstance(c, nn.Tensor) ``` or convert it to a `tf.Tensor` object using the `tf()` method or `nn.tf()` function: ```py b = a.tf() assert isinstance(b, tf.Tensor) b = nn.tf(a) assert isinstance(b, tf.Tensor) ``` See more examples [here][examples]. ## Installation Requirements: - TensorFlow >= 2.0 - Python >= 3.6 Install from PyPI (recommended): ```sh pip install nn ``` Alternatively, install from source: ```sh git clone https://github.com/marella/nn.git cd nn pip install -e . ``` [TensorFlow] should be installed separately. ## Testing To run tests, install dependencies: ```sh pip install -e .[tests] ``` and run: ```sh pytest tests ``` [tensorflow]: https://www.tensorflow.org/install [examples]: https://github.com/marella/train/tree/master/examples %prep %autosetup -n nn-0.1.1 %build %py3_build %install %py3_install install -d -m755 %{buildroot}/%{_pkgdocdir} if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi pushd %{buildroot} if [ -d usr/lib ]; then find usr/lib -type f -printf "\"/%h/%f\"\n" >> filelist.lst fi if [ -d usr/lib64 ]; then find usr/lib64 -type f -printf "\"/%h/%f\"\n" >> filelist.lst fi if [ -d usr/bin ]; then find usr/bin -type f -printf "\"/%h/%f\"\n" >> filelist.lst fi if [ -d usr/sbin ]; then find usr/sbin -type f -printf "\"/%h/%f\"\n" >> filelist.lst fi touch doclist.lst if [ -d usr/share/man ]; then find usr/share/man -type f -printf "\"/%h/%f.gz\"\n" >> doclist.lst fi popd mv %{buildroot}/filelist.lst . mv %{buildroot}/doclist.lst . %files -n python3-nn -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Mon Jul 10 2023 Python_Bot - 0.1.1-1 - Package Spec generated