.. _reseauneuronesrst:

===================
Réseaux de neurones
===================


.. only:: html

    **Links:** :download:`notebook <reseau_neurones.ipynb>`, :downloadlink:`html <reseau_neurones2html.html>`, :download:`PDF <reseau_neurones.pdf>`, :download:`python <reseau_neurones.py>`, :downloadlink:`slides <reseau_neurones.slides.html>`, :githublink:`GitHub|_doc/notebooks/ml/reseau_neurones.ipynb|*`


Réseaux de neurones avec scikit-learn.

.. code:: ipython3

    %matplotlib inline

.. code:: ipython3

    from sklearn.linear_model import Perceptron
    X = [[0., 0.], [1., 1.]]
    y = [0, 1]
    clf = Perceptron()
    clf.fit(X, y) 




.. parsed-literal::
    Perceptron(alpha=0.0001, class_weight=None, eta0=1.0, fit_intercept=True,
          n_iter=5, n_jobs=1, penalty=None, random_state=0, shuffle=True,
          verbose=0, warm_start=False)



.. code:: ipython3

    import matplotlib.pyplot as plt
    import numpy
    def softmax(x):
        return 1.0 / (1 + numpy.exp(-x))
    def dsoftmax(x):
        t = numpy.exp(-x)
        return t / (1 + t)**2
    x = numpy.arange(-10,10, 0.1)
    y = softmax(x)
    dy = dsoftmax(x)
    fig, ax = plt.subplots(1,1)
    ax.plot(x,y, label="softmax")
    ax.plot(x,dy, label="dérivée")
    ax.set_ylim([-0.1, 1.1])
    ax.plot([-5, -5], [-0.1, 1.1], "r")
    ax.plot([5, 5], [-0.1, 1.1], "r")
    ax.legend(loc=2)




.. parsed-literal::
    <matplotlib.legend.Legend at 0x1b651aeacf8>




.. image:: reseau_neurones_3_1.png


.. code:: ipython3

    x





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