Merge pull request aladdinpersson#33 from ankandrew/patch-1

Added bias=False

Author: aladdinpersson

ML/Pytorch/CNN_architectures/pytorch_resnet.py

@@ -23,7 +23,7 @@ def __init__(
  super(block, self).__init__()
  self.expansion = 4
  self.conv1 = nn.Conv2d(
- in_channels, intermediate_channels, kernel_size=1, stride=1, padding=0
+ in_channels, intermediate_channels, kernel_size=1, stride=1, padding=0, bias=False
  )
  self.bn1 = nn.BatchNorm2d(intermediate_channels)
  self.conv2 = nn.Conv2d(
@@ -32,6 +32,7 @@ def __init__(
  kernel_size=3,
  stride=stride,
  padding=1,
+ bias=False
  )
  self.bn2 = nn.BatchNorm2d(intermediate_channels)
  self.conv3 = nn.Conv2d(
@@ -40,6 +41,7 @@ def __init__(
  kernel_size=1,
  stride=1,
  padding=0,
+ bias=False
  )
  self.bn3 = nn.BatchNorm2d(intermediate_channels * self.expansion)
  self.relu = nn.ReLU()
@@ -70,7 +72,7 @@ class ResNet(nn.Module):
  def __init__(self, block, layers, image_channels, num_classes):
  super(ResNet, self).__init__()
  self.in_channels = 64
- self.conv1 = nn.Conv2d(image_channels, 64, kernel_size=7, stride=2, padding=3)
+ self.conv1 = nn.Conv2d(image_channels, 64, kernel_size=7, stride=2, padding=3, bias=False)
  self.bn1 = nn.BatchNorm2d(64)
  self.relu = nn.ReLU()
  self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
@@ -122,6 +124,7 @@ def _make_layer(self, block, num_residual_blocks, intermediate_channels, stride)
  intermediate_channels * 4,
  kernel_size=1,
  stride=stride,
+ bias=False
  ),
  nn.BatchNorm2d(intermediate_channels * 4),
  )