%global _empty_manifest_terminate_build 0 Name: python-shinnosuke-gpu Version: 0.7.1 Release: 1 Summary: A keras-like API deep learning framework,realized by cupy License: MIT License URL: https://github.com/eLeVeNnN/shinnosuke-gpu Source0: https://mirrors.aliyun.com/pypi/web/packages/a2/6a/e80b0f74b2a9a7efed526697f7d616984ff93976dcc06609c3e83f8daf23/shinnosuke-gpu-0.7.1.tar.gz BuildArch: noarch %description #Shinnosuke-GPU : Deep learning framework ##Descriptions 1. Based on Cupy(GPU version) 2. Completely realized by Python only 3. Keras-like API 4. For deep learning studying ##Features 1. Native to Python 2. Keras-like API 3. Easy to get start 4. Commonly used models are provided: Dense, Conv2D, MaxPooling2D, LSTM, SimpleRNN, etc 5. Several basic networks Examples 6. Sequential model and Functional model are implemented 7. Autograd is supported ##Installation Using pip: `$ pip install shinnosuke-gpu` ##Supports ### Two model types: 1.**Sequential** ```python from shinnosuke.models import Sequential from shinnosuke.layers.FC import Dense m=Sequential() m.add(Dense(500,activation='relu',n_in=784)) m.add(Dense(10,activation='softmax')) m.compile(optimizer='sgd',loss='sparse_categorical_crossentropy',learning_rate=0.1) m.fit(trainX,trainy,batch_size=512,epochs=1,validation_ratio=0.) ``` 2.**Model** ```python from shinnosuke.models import Model from shinnosuke.layers.FC import Dense from shinnosuke.layers.Base import Input X_input=Input(shape=(None,784)) X=Dense(500,activation='relu')(X_input) X=Dense(10,activation='softmax')(X) model=Model(inputs=X_input,outputs=X) model.compile(optimizer='sgd',loss='sparse_categorical_crossentropy',learning_rate=0.1) model.fit(trainX,trainy,batch_size=512,epochs=1,validation_ratio=0.) ``` ### Two basic class: #### - Layer: - Dense - Conv2D - MaxPooling2D - MeanPooling2D - Activation - Input - Dropout - BatchNormalization - TimeDistributed - SimpleRNN - LSTM - GRU (waiting for implemented) - ZeroPadding2D - Operations( includes Add, Minus, Multiply, Matmul, and so on basic operations for Layer and Node) ####- Node: - Variable - Constant ###Optimizers - StochasticGradientDescent - Momentum - RMSprop - AdaGrad - AdaDelta - Adam Waiting for implemented more ###Objectives - MeanSquaredError - MeanAbsoluteError - BinaryCrossEntropy - SparseCategoricalCrossEntropy - CategoricalCrossEntropy ###Activations - Relu - Linear - Sigmoid - Tanh - Softmax ###Initializations - Zeros - Ones - Uniform - LecunUniform - GlorotUniform - HeUniform - Normal - LecunNormal - GlorotNormal - HeNormal - Orthogonal ###Regularizes waiting for implement. ###Utils - get_batches (generate mini-batch) - to_categorical (convert inputs to one-hot vector/matrix) - concatenate (concatenate Nodes that have the same shape in specify axis) - pad_sequences (pad sequences to the same length) %package -n python3-shinnosuke-gpu Summary: A keras-like API deep learning framework,realized by cupy Provides: python-shinnosuke-gpu BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-shinnosuke-gpu #Shinnosuke-GPU : Deep learning framework ##Descriptions 1. Based on Cupy(GPU version) 2. Completely realized by Python only 3. Keras-like API 4. For deep learning studying ##Features 1. Native to Python 2. Keras-like API 3. Easy to get start 4. Commonly used models are provided: Dense, Conv2D, MaxPooling2D, LSTM, SimpleRNN, etc 5. Several basic networks Examples 6. Sequential model and Functional model are implemented 7. Autograd is supported ##Installation Using pip: `$ pip install shinnosuke-gpu` ##Supports ### Two model types: 1.**Sequential** ```python from shinnosuke.models import Sequential from shinnosuke.layers.FC import Dense m=Sequential() m.add(Dense(500,activation='relu',n_in=784)) m.add(Dense(10,activation='softmax')) m.compile(optimizer='sgd',loss='sparse_categorical_crossentropy',learning_rate=0.1) m.fit(trainX,trainy,batch_size=512,epochs=1,validation_ratio=0.) ``` 2.**Model** ```python from shinnosuke.models import Model from shinnosuke.layers.FC import Dense from shinnosuke.layers.Base import Input X_input=Input(shape=(None,784)) X=Dense(500,activation='relu')(X_input) X=Dense(10,activation='softmax')(X) model=Model(inputs=X_input,outputs=X) model.compile(optimizer='sgd',loss='sparse_categorical_crossentropy',learning_rate=0.1) model.fit(trainX,trainy,batch_size=512,epochs=1,validation_ratio=0.) ``` ### Two basic class: #### - Layer: - Dense - Conv2D - MaxPooling2D - MeanPooling2D - Activation - Input - Dropout - BatchNormalization - TimeDistributed - SimpleRNN - LSTM - GRU (waiting for implemented) - ZeroPadding2D - Operations( includes Add, Minus, Multiply, Matmul, and so on basic operations for Layer and Node) ####- Node: - Variable - Constant ###Optimizers - StochasticGradientDescent - Momentum - RMSprop - AdaGrad - AdaDelta - Adam Waiting for implemented more ###Objectives - MeanSquaredError - MeanAbsoluteError - BinaryCrossEntropy - SparseCategoricalCrossEntropy - CategoricalCrossEntropy ###Activations - Relu - Linear - Sigmoid - Tanh - Softmax ###Initializations - Zeros - Ones - Uniform - LecunUniform - GlorotUniform - HeUniform - Normal - LecunNormal - GlorotNormal - HeNormal - Orthogonal ###Regularizes waiting for implement. ###Utils - get_batches (generate mini-batch) - to_categorical (convert inputs to one-hot vector/matrix) - concatenate (concatenate Nodes that have the same shape in specify axis) - pad_sequences (pad sequences to the same length) %package help Summary: Development documents and examples for shinnosuke-gpu Provides: python3-shinnosuke-gpu-doc %description help #Shinnosuke-GPU : Deep learning framework ##Descriptions 1. Based on Cupy(GPU version) 2. Completely realized by Python only 3. Keras-like API 4. For deep learning studying ##Features 1. Native to Python 2. Keras-like API 3. Easy to get start 4. Commonly used models are provided: Dense, Conv2D, MaxPooling2D, LSTM, SimpleRNN, etc 5. Several basic networks Examples 6. Sequential model and Functional model are implemented 7. Autograd is supported ##Installation Using pip: `$ pip install shinnosuke-gpu` ##Supports ### Two model types: 1.**Sequential** ```python from shinnosuke.models import Sequential from shinnosuke.layers.FC import Dense m=Sequential() m.add(Dense(500,activation='relu',n_in=784)) m.add(Dense(10,activation='softmax')) m.compile(optimizer='sgd',loss='sparse_categorical_crossentropy',learning_rate=0.1) m.fit(trainX,trainy,batch_size=512,epochs=1,validation_ratio=0.) ``` 2.**Model** ```python from shinnosuke.models import Model from shinnosuke.layers.FC import Dense from shinnosuke.layers.Base import Input X_input=Input(shape=(None,784)) X=Dense(500,activation='relu')(X_input) X=Dense(10,activation='softmax')(X) model=Model(inputs=X_input,outputs=X) model.compile(optimizer='sgd',loss='sparse_categorical_crossentropy',learning_rate=0.1) model.fit(trainX,trainy,batch_size=512,epochs=1,validation_ratio=0.) ``` ### Two basic class: #### - Layer: - Dense - Conv2D - MaxPooling2D - MeanPooling2D - Activation - Input - Dropout - BatchNormalization - TimeDistributed - SimpleRNN - LSTM - GRU (waiting for implemented) - ZeroPadding2D - Operations( includes Add, Minus, Multiply, Matmul, and so on basic operations for Layer and Node) ####- Node: - Variable - Constant ###Optimizers - StochasticGradientDescent - Momentum - RMSprop - AdaGrad - AdaDelta - Adam Waiting for implemented more ###Objectives - MeanSquaredError - MeanAbsoluteError - BinaryCrossEntropy - SparseCategoricalCrossEntropy - CategoricalCrossEntropy ###Activations - Relu - Linear - Sigmoid - Tanh - Softmax ###Initializations - Zeros - Ones - Uniform - LecunUniform - GlorotUniform - HeUniform - Normal - LecunNormal - GlorotNormal - HeNormal - Orthogonal ###Regularizes waiting for implement. ###Utils - get_batches (generate mini-batch) - to_categorical (convert inputs to one-hot vector/matrix) - concatenate (concatenate Nodes that have the same shape in specify axis) - pad_sequences (pad sequences to the same length) %prep %autosetup -n shinnosuke-gpu-0.7.1 %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-shinnosuke-gpu -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Tue Jun 20 2023 Python_Bot - 0.7.1-1 - Package Spec generated