resume resnet impl.
@@ -106,7 +106,7 @@ class ResNet(BaseNet):
out = layers.conv1(images, is_training)
for block in layers.blocks:
out = block(out, is_training)
- out = tf.nn.relu(layers.bn(out, is_training))
+ out = tf.reduce_mean(tf.nn.relu(layers.bn(out, is_training)),axis=[1,2,3])
return layers.fc(out)
@@ -170,6 +170,7 @@ class BottleneckBlock(TrainableLayer):
out=layers.conv[1](out, is_training)
out=layers.conv[2](out, is_training)
out = layers.conv_shortcut(tmp, is_training) + out
+ print(out.shape)
return out
DownResBlockDesc = namedtuple('DownResBlockDesc', ['blocks'])
