semvec/pypi-packages huggingface dataset:
zChainer 0.3.2
scikit-learn like interface and stacked autoencoder for chainer
License: MIT
Keywords: deep neural network,machine learning

zChainer
========

scikit-learn like interface and stacked autoencoder for chainer

Requirements
------------

-  numpy
-  scikit-learn
-  chainer >= 1.5

Installation
------------

::

    pip install zChainer

Usage
-----

Autoencoder
~~~~~~~~~~~

.. code:: python

    import numpy as np
    import chainer.functions as F
    import chainer.links as L
    from chainer import ChainList, optimizers
    from zChainer import NNAutoEncoder, utility

    data = (..).astype(np.float32)

    encoder = ChainList(
        L.Linear(784, 200),
        L.Linear(200, 100))
    decoder =ChainList(
        L.Linear(200, 784),
        L.Linear(100, 200))

    # You can set your own forward function. Default is as below.
    #def forward(self, x):
    #    h = F.dropout(F.relu(self.model[0](x)))
    #    return F.dropout(F.relu(self.model[1](h)))
    #
    #NNAutoEncoder.forward = forward
    ae = NNAutoEncoder(encoder, decoder, optimizers.Adam(), epoch=100, batch_size=100,
        log_path="./ae_log_"+utility.now()+".csv", export_path="./ae_"+utility.now()+".model")

    ae.fit(data)

Training and Testing
~~~~~~~~~~~~~~~~~~~~

.. code:: python

    import numpy as np
    import chainer.functions as F
    import chainer.links as L
    from chainer import ChainList, optimizers
    from zChainer import NNManager, utility
    import pickle

    X_train = (..).astype(np.float32)
    y_train = (..).astype(np.int32)
    X_test = (..).astype(np.float32)
    y_test = (..).astype(np.int32)

    # Create a new network
    model = ChainList(L.Linear(784, 200), L.Linear(200, 100), L.Linear(100, 10))

    # or load a serialized model
    #f = open("./ae_2015-12-01_11-26-45.model")
    #model = pickle.load(f)
    #f.close()
    #model.add_link(L.Linear(100,10))

    def forward(self, x):
        h = F.relu(self.model[0](x))
        h = F.relu(self.model[1](h))
        return F.relu(self.model[2](h))

    def output(self, y):
        y_trimed = y.data.argmax(axis=1)
        return np.array(y_trimed, dtype=np.int32)

    NNManager.forward = forward
    NNManager.output = output
    nn = NNManager(model, optimizers.Adam(), F.softmax_cross_entropy,
        epoch=100, batch_size=100,
        log_path="./training_log_"+utility.now()+".csv")

    nn.fit(X_train, y_train, is_classification=True)
    nn.predict(X_test, y_test)