def forward(self, x, t):
N = x.shape[0]
log_z = utils.logsumexp(x, axis=1)
log_p = x - log_z
log_p = log_p[np.arange(N), t.ravel()]
y = -log_p.sum() / np.float32(N)
return y
def forward(self, x, t):
xp = cuda.get_array_module(t.data)
N = x.shape[0]
log_z = utils.logsumexp(x, axis=1)
log_p = x - log_z
log_p = log_p[xp.arange(N), t.ravel()]
y = -log_p.sum() / xp.float32(N)
return y
def forward(self, x, t):
N = x.shape[0]
log_z = utils.logsumexp(x, axis=1)
log_p = x - log_z
# ravel flatten multi dim data to one dim data
log_p = log_p[np.arange(N), t.ravel()]
y = -log_p.sum() / np.float32(N)
return y
def forward(self, x):
log_z = utils.logsumexp(x, self.axis)
y = x - log_z
return y
