automatic import of python-shinnosuke-gpuopeneuler20.03

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diff --git a/.gitignore b/.gitignore
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+/shinnosuke-gpu-0.7.1.tar.gz
diff --git a/python-shinnosuke-gpu.spec b/python-shinnosuke-gpu.spec
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+%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 <Python_Bot@openeuler.org> - 0.7.1-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..b264e1c
--- /dev/null
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@@ -0,0 +1 @@
+28e6d3f9b2797dad566919dbb1fffe96  shinnosuke-gpu-0.7.1.tar.gz