def cross_entropy_logits(self, yhat, M): if self.smoothing: n_out = M + self.n_sample return T.cast( T.mean((1.0 - (n_out / (n_out - 1)) * self.smoothing) * gpu_diag_wide(yhat) + (self.smoothing / (n_out - 1)) * T.sum(yhat, axis=1)), theano.config.floatX) else: return T.cast(T.mean(gpu_diag_wide(yhat)), theano.config.floatX)
def top1(self, yhat, M): ydiag = gpu_diag_wide(yhat).dimshuffle((0, 'x')) return T.cast( T.mean( T.mean(T.nnet.sigmoid(-ydiag + yhat) + T.nnet.sigmoid(yhat**2), axis=1) - T.nnet.sigmoid(ydiag**2) / (M + self.n_sample)), theano.config.floatX)
def bpr_max(self, yhat, M): softmax_scores = self.softmax_neg(yhat) return T.cast( T.mean(-T.log( T.sum(T.nnet.sigmoid( gpu_diag_wide(yhat).dimshuffle( (0, 'x')) - yhat) * softmax_scores, axis=1) + 1e-24) + self.bpreg * T.sum((yhat**2) * softmax_scores, axis=1)), theano.config.floatX)
def top1_max(self, yhat, M): softmax_scores = self.softmax_neg(yhat) y = softmax_scores * (T.nnet.sigmoid(-gpu_diag_wide(yhat).dimshuffle( (0, 'x')) + yhat) + T.nnet.sigmoid(yhat**2)) return T.cast(T.mean(T.sum(y, axis=1)), theano.config.floatX)
def bpr(self, yhat, M): return T.cast( T.mean(-T.log( T.nnet.sigmoid( gpu_diag_wide(yhat).dimshuffle((0, 'x')) - yhat))), theano.config.floatX)