#f.close()
W = param_dict['W'].param
pylab.ioff()
net.set_input('input',Xtest)
net.set_target('squared loss',Ytest)
net.forward_prop(test=True)
pylab.plot(Xtest,net.target_layers['squared loss'].outputs)
pylab.plot(X,Y)
pylab.show()
return param_dict
if __name__ == '__main__':
#D = loadmat('mnist_small.mat')
#D = loadmat('patches_16x16.mat')
#X = D['patches']
D = ld.load_mnist(range(10))
X = D[0]
Y = D[1]
Xtest = D[2]
Ytest = D[3]
num_examples = X.shape[0]
num_hid = 500
num_iters = 100
eta = 0.1
mo = 0.9
np.random.seed(1)
ind = np.random.permutation(X.shape[0])[0:num_examples]
X = X[ind]
Y = Y[ind]
dW = mo * dW - eta * gW
dbh = mo * dbh - eta * gbh
dO = mo * dO - eta * gO
dbo = mo * dbo - eta * gbo
W = W + dW
bh = bh + dbh
O = O + dO
bo = bo + dbo
print 'Iteration %d Complete. Objective value: %s' % (i + 1,
iteration_obj)
return W, bh, O, bo
if __name__ == '__main__':
np.random.seed(1)
X, y, Xtest, ytest = ld.load_mnist(range(10))[:4]
kmin = 10
kmax = 10
W, bh, O, bo = train_carbm_nnet(X, y, mo=0.5, kmin=kmin, kmax=kmax)
yhat = carbm_nnet_classify(W, bh, O, bo, Xtest, 10, 10)
err = (yhat != ytest.argmax(1)).mean()
print 'Classification error: %s' % err
pylab.ioff()
d.print_aligned(W)
pylab.show(block=True)
print 'File deleted: %s' % ~os.path.isfile(save_file)
return W, b, c
print 'Iteration %d complete. Objective value: %s' % (i + 1, obj)
times.append(time.time())
errs.append(obj)
#Save out some results about timing/reconstruction error per epoch.
if save_file is not None:
np.savez(save_file, W=W, b=b, c=c, times=times, errs=errs)
#Plot some diagnostics for this epoch.
if plotting:
pylab.ion()
pylab.subplot(1, 2, 1)
d.print_aligned(W[:, 0:np.minimum(100, W.shape[1])])
pylab.subplot(1, 2, 2)
d.print_aligned((v_neg + m).T)
pylab.draw()
return W, b, c
if __name__ == '__main__':
np.random.seed(1)
import load_datasets as ld
X = ld.load_mnist(range(10))[0]
W, b, c = carbm_cd(X, plotting=True, num_iters=10)
pylab.ioff()
d.print_aligned(W)
pylab.show(block=True)
gO = G['gO']
gbo = G['gbo']
dW = mo*dW - eta*gW
dbh = mo*dbh - eta*gbh
dO = mo*dO - eta*gO
dbo = mo*dbo - eta*gbo
W = W + dW
bh = bh + dbh
O = O + dO
bo = bo + dbo
print 'Iteration %d Complete. Objective value: %s' % (i+1,iteration_obj)
return W,bh,O,bo
if __name__ == '__main__':
np.random.seed(1)
X,y,Xtest,ytest = ld.load_mnist(range(10))[:4]
kmin = 10
kmax = 10
W,bh,O,bo = train_carbm_nnet(X,y,mo=0.5,kmin=kmin,kmax=kmax)
yhat = carbm_nnet_classify(W,bh,O,bo,Xtest,10,10)
err = (yhat != ytest.argmax(1)).mean()
print 'Classification error: %s' % err
pylab.ioff()
d.print_aligned(W)
pylab.show(block=True)