def check_ref(self):
t_x = chainer.Variable(self.context(self.t_x))
t_e = chainer.Variable(self.context(self.t_e))
x = chainer.Variable(self.context(self.x))
y, e = gravesnet.split_args(self.ngauss, x, t_x, t_e)
p = gaussian_mixture_2d(*y)
q = gaussian_mixture_2d_ref(*y)
gradient_check.assert_allclose(p.data, q.data)
def forward_one_step(self, hidden_state, lstm_cells, x_data, t_x_data, t_e_data, train=True):
x = chainer.Variable(x_data, volatile=not train)
t_x = chainer.Variable(t_x_data, volatile=not train)
t_e = chainer.Variable(t_e_data, volatile=not train)
gps, y_e, hidden_state, lstm_cells = self.bottle_neck(hidden_state, lstm_cells, x_data, train)
t_x = split_axis_by_widths(t_x, [1, 1])
gi, e = (gps + tuple(t_x)), (y_e, t_e)
p = gaussian_mixture_2d_ref(*gi)
loss = concat_losses(p, e)
return hidden_state, lstm_cells, loss
def loss_func(m, y, t_x, t_e):
x, e = split_args(m, y, t_x, t_e)
p = gaussian_mixture_2d_ref(*x)
return concat_losses(p, e)
