def _callback(pi):
Y_hat = TeaSpoon.exe(pred, [pi, X])
out = {}
out['MSE'] = np.mean(Y_hat==y)
out['Loss'] = TeaSpoon.exe(loss, [pi, X,y])
opt = {'freq':10}
return out, opt
def callback(pi):
Y_hat = TS.exe(pred_T, [pi, X])
out = {}
out['ACC'] = np.mean(Y_hat==y)
out['Loss'] = TS.exe(loss_T, [pi, X,y])
out['pi'] = 3.14
opt = {'freq':1}
return out, opt
def test_default(self):
p1, res = TeaSpoon.optimize(
fun=loss,
p0=p0,
jac=grad,
callback='default',
args=(X, y),
method='BFGS',
options = {'maxiter': 20, 'disp': 0},
)
loss_0 = TeaSpoon.exe(loss, [p0, X, y])
loss_1 = TeaSpoon.exe(loss, [p1, X, y])
assert loss_0 > loss_1
P = TT.nnet.softmax( T.dot(X, w) + b )
return TT.argmax(P, 1)
# debug your theano code (optional)
TS.debug(pred, [p0, X])
TS.debug(loss, [p0, X, y])
# compile your functions
pred_T = TS.compile(pred, [p0, X], jac=False)
loss_T, grad_T = TS.compile(loss, [p0, X, y], jac=True)
# # exection of comiled funtion (optional)
TS.exe(pred_T, [p0, X])
TS.exe(loss_T, [p0, X, y])
# define your own callback function (optional)
def callback(pi):
Y_hat = TS.exe(pred_T, [pi, X])
out = {}
out['ACC'] = np.mean(Y_hat==y)
out['Loss'] = TS.exe(loss_T, [pi, X,y])
out['pi'] = 3.14
opt = {'freq':1}
return out, opt
# use here the features and options from scipy.optimize
