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            "Development Status :: 4 - Beta",
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            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: Implementation",
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        "description": "#Shinnosuke-GPU : Deep learning framework\n##Descriptions\n1. Based on Cupy(GPU version)\n\n2. Completely realized by Python only\n3. Keras-like API\n4. For deep learning studying\n\n##Features\n1. Native to Python\n\n2. Keras-like API\n3. Easy to get start\n4. Commonly used models are provided: Dense, Conv2D, MaxPooling2D, LSTM, SimpleRNN, etc\n5. Several basic networks Examples\n6. Sequential model and Functional model are implemented\n7. Autograd is supported \n\n##Installation\nUsing pip:\n\n`$ pip install shinnosuke-gpu`\n\n##Supports\n\n### Two model types:\n1.**Sequential**\n\n```python\nfrom shinnosuke.models import Sequential\nfrom shinnosuke.layers.FC import Dense\n\nm=Sequential()\n\nm.add(Dense(500,activation='relu',n_in=784))\n\nm.add(Dense(10,activation='softmax'))\n\nm.compile(optimizer='sgd',loss='sparse_categorical_crossentropy',learning_rate=0.1)\n\nm.fit(trainX,trainy,batch_size=512,epochs=1,validation_ratio=0.)\n\n```\n2.**Model**\n```python\nfrom shinnosuke.models import Model\nfrom shinnosuke.layers.FC import Dense\nfrom shinnosuke.layers.Base import Input\n\nX_input=Input(shape=(None,784))\n\nX=Dense(500,activation='relu')(X_input)\n\nX=Dense(10,activation='softmax')(X)\n\nmodel=Model(inputs=X_input,outputs=X)\n\nmodel.compile(optimizer='sgd',loss='sparse_categorical_crossentropy',learning_rate=0.1)\n\nmodel.fit(trainX,trainy,batch_size=512,epochs=1,validation_ratio=0.)\n```\n### Two basic class:\n#### - Layer:\n\n- Dense\n\n- Conv2D\n\n- MaxPooling2D\n- MeanPooling2D\n- Activation\n- Input\n- Dropout\n- BatchNormalization\n- TimeDistributed\n- SimpleRNN\n- LSTM\n- GRU (waiting for implemented)\n- ZeroPadding2D\n- Operations( includes Add, Minus, Multiply, Matmul, and so on basic operations for Layer and Node)\n\n####- Node:\n\n- Variable\n- Constant\n\n###Optimizers\n- StochasticGradientDescent\n\n- Momentum\n\n- RMSprop\n- AdaGrad\n- AdaDelta\n- Adam\n\nWaiting for implemented more\n\n###Objectives\n\n- MeanSquaredError\n\n- MeanAbsoluteError\n\n- BinaryCrossEntropy\n\n- SparseCategoricalCrossEntropy\n\n- CategoricalCrossEntropy\n\n###Activations\n- Relu\n\n- Linear\n\n- Sigmoid\n- Tanh\n- Softmax\n\n###Initializations\n- Zeros\n\n- Ones\n\n- Uniform\n\n- LecunUniform\n- GlorotUniform\n- HeUniform\n- Normal\n- LecunNormal\n- GlorotNormal\n- HeNormal\n- Orthogonal\n\n###Regularizes\nwaiting for implement.\n\n###Utils\n- get_batches (generate mini-batch)\n\n- to_categorical (convert inputs to one-hot vector/matrix)\n- concatenate (concatenate Nodes that have the same shape in specify axis)\n\n- pad_sequences (pad sequences to the same length)\n\n\n\n\n\n\n\n\n",
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