return -TT.mean(TT.log(P[idx, idy]))
# define prediction function
def pred(pi, X):
'''
compute predictions
'''
w = pi['w'].value
b = pi['b'].value
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):
