def _residual(self, h, channels, strides, keep_prob):
h0 = h
h1 = F.dropout(
F.conv(F.activation(F.batch_normalization(h0)), channels, strides),
keep_prob)
h2 = F.conv(F.activation(F.batch_normalization(h1)), channels)
# c.f. http://gitxiv.com/comments/7rffyqcPLirEEsmpX
if F.volume(h0) == F.volume(h2):
h = h2 + h0
else:
h4 = F.conv(h0, channels, strides)
h = h2 + h4
return h
def _residual(self, h, channels, strides):
h0 = h
h1 = F.activation(
F.batch_normalization(
F.conv(h0, channels, strides, bias_term=False)))
h2 = F.batch_normalization(F.conv(h1, channels, bias_term=False))
if F.volume(h0) == F.volume(h2):
h = h2 + h0
else:
h3 = F.avg_pool(h0)
h4 = tf.pad(h3,
[[0, 0], [0, 0], [0, 0], [channels / 4, channels / 4]])
h = h2 + h4
return h
def _residual(self, h, channels, strides):
h0 = h
h1 = F.activation(
F.batch_normalization(
F.conv(h0, channels, strides, bias_term=False)))
h2 = F.batch_normalization(F.conv(h1, channels, bias_term=False))
# c.f. http://gitxiv.com/comments/7rffyqcPLirEEsmpX
if F.volume(h0) == F.volume(h2):
h = h2 + h0
else:
h3 = F.avg_pool(h0)
h4 = tf.pad(h3,
[[0, 0], [0, 0], [0, 0], [channels / 4, channels / 4]])
h = h2 + h4
return F.activation(h)
def _inference(self, X, keep_prob):
h = X
h = F.activation(F.batch_normalization(F.conv(h, 16, bias_term=False)))
for i in range(self._layers):
h = self._residual(h, channels=16, strides=1)
for channels in [32, 64]:
for i in range(self._layers):
strides = 2 if i == 0 else 1
h = self._residual(h, channels, strides)
h = tf.reduce_mean(h, reduction_indices=[1,
2]) # Global Average Pooling
h = F.dense(h, self._num_classes)
return h