Réseaux de neurones

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Réseaux de neurones avec scikit-learn.

%matplotlib inline
from sklearn.linear_model import Perceptron
X = [[0., 0.], [1., 1.]]
y = [0, 1]
clf = Perceptron()
clf.fit(X, y)
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)
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)
<matplotlib.legend.Legend at 0x1b651aeacf8>
../_images/reseau_neurones_3_1.png
x
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