In [420]:

    

import theano
import numpy as np
import pickle
from theano import tensor as T
from matplotlib import pyplot as plt


    

In [435]:

    

with open('theano_test_data.pl','r') as datafile:
    Xdata,ydata = pickle.load(datafile)


    

In [436]:

    

p = 2
H = 4
x = T.vector('x')
W1 = theano.shared(value = np.random.randn(p*H).reshape((H,p)), name= 'W1')
w2 = theano.shared(value = np.random.randn(H), name= 'w2')


    

In [437]:

    

u1 = T.dot(W1,x)


    

In [438]:

    

h = T.nnet.relu(u1)
u2 = T.dot(h,w2)
y = T.scalar('y')
prob = T.nnet.sigmoid(u2)
R = - y * T.log(prob) - (1 - y) * T.log(1 - prob)


    

In [439]:

    

w2g = T.grad(R,w2)
W1g = T.grad(R,W1)


    

In [440]:

    

learn_rate = .05


    

In [441]:

    

W_updates = [(W1, W1 - learn_rate * W1g),
            (w2, w2 - learn_rate * w2g)]


    

In [442]:

    

grad_step = theano.function([x,y],R,updates=W_updates)


    

In [443]:

    

Xdata.shape, ydata[0]


    

    
Out[443]:





((1000L, 2L), 0.0)

In [444]:

    

n = Xdata.shape[0]
for i in range(n):
    grad_step(Xdata[i,:],ydata[i])


    

In [445]:

    

W1.get_value(), w2.get_value()


    

    
Out[445]:





(array([[-1.54568534,  0.64928966],
        [ 0.17317346, -1.67084628],
        [-1.35370085,  0.8111976 ],
        [ 1.58425059, -0.3717367 ]]),
 array([-1.53440713,  0.89179048,  0.78306905, -1.52970686]))

In [446]:

    

yhat = T.gt(prob,.5)


    

In [447]:

    

ypred = np.array([prob.eval({x: X}) > .5 for X in Xdata])


    

In [448]:

    

plt.scatter(Xdata[ypred==1.,0],Xdata[ypred==1.,1])
plt.scatter(Xdata[ypred==0.,0],Xdata[ypred==0.,1],c='r')
plt.show()


    

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