resnet hp fix

Author: vpj

labml_nn/resnet/__init__.py

@@ -273,7 +273,7 @@ def __init__(self, n_blocks: List[int], n_channels: List[int],
         # Initial convolution layer maps from `img_channels` to number of channels in the first
         # residual block (`n_channels[0]`)
         self.conv = nn.Conv2d(img_channels, n_channels[0],
-                              kernel_size=first_kernel_size, stride=1, padding=first_kernel_size // 2)
+                              kernel_size=first_kernel_size, stride=2, padding=first_kernel_size // 2)
         # Batch norm after initial convolution
         self.bn = nn.BatchNorm2d(n_channels[0])
 

labml_nn/resnet/experiment.py

@@ -34,7 +34,7 @@ class Configs(CIFAR10Configs):
     # Bottleneck sizes
     bottlenecks: Optional[List[int]] = None
     # Kernel size of the initial convolution layer
-    first_kernel_size: int = 7
+    first_kernel_size: int = 3
 
 
 @option(Configs.model)