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        "author": "Shoya Ishimaru",
        "author_email": "shoya.ishimaru@gmail.com",
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        "description": "zChainer\n========\n\nscikit-learn like interface and stacked autoencoder for chainer\n\nRequirements\n------------\n\n-  numpy\n-  scikit-learn\n-  chainer >= 1.5\n\nInstallation\n------------\n\n::\n\n    pip install zChainer\n\nUsage\n-----\n\nAutoencoder\n~~~~~~~~~~~\n\n.. code:: python\n\n    import numpy as np\n    import chainer.functions as F\n    import chainer.links as L\n    from chainer import ChainList, optimizers\n    from zChainer import NNAutoEncoder, utility\n\n    data = (..).astype(np.float32)\n\n    encoder = ChainList(\n        L.Linear(784, 200),\n        L.Linear(200, 100))\n    decoder =ChainList(\n        L.Linear(200, 784),\n        L.Linear(100, 200))\n\n    # You can set your own forward function. Default is as below.\n    #def forward(self, x):\n    #    h = F.dropout(F.relu(self.model[0](x)))\n    #    return F.dropout(F.relu(self.model[1](h)))\n    #\n    #NNAutoEncoder.forward = forward\n    ae = NNAutoEncoder(encoder, decoder, optimizers.Adam(), epoch=100, batch_size=100,\n        log_path=\"./ae_log_\"+utility.now()+\".csv\", export_path=\"./ae_\"+utility.now()+\".model\")\n\n    ae.fit(data)\n\nTraining and Testing\n~~~~~~~~~~~~~~~~~~~~\n\n.. code:: python\n\n    import numpy as np\n    import chainer.functions as F\n    import chainer.links as L\n    from chainer import ChainList, optimizers\n    from zChainer import NNManager, utility\n    import pickle\n\n    X_train = (..).astype(np.float32)\n    y_train = (..).astype(np.int32)\n    X_test = (..).astype(np.float32)\n    y_test = (..).astype(np.int32)\n\n    # Create a new network\n    model = ChainList(L.Linear(784, 200), L.Linear(200, 100), L.Linear(100, 10))\n\n    # or load a serialized model\n    #f = open(\"./ae_2015-12-01_11-26-45.model\")\n    #model = pickle.load(f)\n    #f.close()\n    #model.add_link(L.Linear(100,10))\n\n    def forward(self, x):\n        h = F.relu(self.model[0](x))\n        h = F.relu(self.model[1](h))\n        return F.relu(self.model[2](h))\n\n    def output(self, y):\n        y_trimed = y.data.argmax(axis=1)\n        return np.array(y_trimed, dtype=np.int32)\n\n    NNManager.forward = forward\n    NNManager.output = output\n    nn = NNManager(model, optimizers.Adam(), F.softmax_cross_entropy,\n        epoch=100, batch_size=100,\n        log_path=\"./training_log_\"+utility.now()+\".csv\")\n\n    nn.fit(X_train, y_train, is_classification=True)\n    nn.predict(X_test, y_test)",
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