def create_convolution(size, depth, winlen):
conv_actfun = activation.tanh
return layers.Serial(
[layers.Convolution(3, size, winlen, stride=1, fun=conv_actfun)] +
[layers.Residual(layers.Convolution(size, size, winlen, stride=1, fun=conv_actfun)) for _ in range(depth)] +
[layers.Convolution(size, 3, winlen, stride=1, fun=activation.linear)]
)
def test_003_simple_serial(self):
W2 = np.random.normal(size=(self._SIZE, self._SIZE)).astype(taiyaki_dtype)
res = self.res.dot(W2.transpose())
l1 = nn.FeedForward(self._NFEATURES, self._SIZE, has_bias=True,
fun=activation.linear)
nn.init_(l1.linear.weight, self.W)
nn.init_(l1.linear.bias, self.b)
l2 = nn.FeedForward(self._SIZE, self._SIZE, fun=activation.linear, has_bias=False)
nn.init_(l2.linear.weight, W2)
network = nn.Serial([l1, l2])
with torch.no_grad():
y = network(torch.tensor(self.x)).numpy()
np.testing.assert_almost_equal(y, res, decimal=4)