权重和偏置的简单理解
y = ax + b # a就是权重,b就是偏置
模型参数:偏差和权重 Weights 和 biases
for layer in model.modules():
if isinstance(layer, nn.Linear):
print(layer.weight)
print(layer.bias)
Element in in state_dict:
"model.conv1.weight",
"model.bn1.weight", "model.bn1.bias",
"model.bn1.running_mean",
"model.bn1.running_var",
"model.layer1.0.conv1.weight",
"model.layer1.0.bn1.weight", "model.layer1.0.bn1.bias",
"model.layer1.0.bn1.running_mean",
"model.layer1.0.bn1.running_var",
"model.layer1.0.conv2.weight",
"model.layer1.0.bn2.weight", "model.layer1.0.bn2.bias",
"model.layer1.0.bn2.running_mean",
"model.layer1.0.bn2.running_var",
"model.layer1.1.conv1.weight",
"model.layer1.1.bn1.weight", "model.layer1.1.bn1.bias",
"model.layer1.1.bn1.running_mean",
"model.layer1.1.bn1.running_var",
"model.layer1.1.conv2.weight",
"model.layer1.1.bn2.weight", "model.layer1.1.bn2.bias",
"model.layer1.1.bn2.running_mean",
"model.layer1.1.bn2.running_var",
"model.layer2.0.conv1.weight",
"model.layer2.0.bn1.weight", "model.layer2.0.bn1.bias",
"model.layer2.0.bn1.running_mean",
"model.layer2.0.bn1.running_var",
"model.layer2.0.conv2.weight",
"model.layer2.0.bn2.weight", "model.layer2.0.bn2.bias",
......
conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
bn1 = nn.BatchNorm2d(64)
relu = nn.ReLU(inplace=True)
maxpool = nn.MaxPool2()
...... nn.softmax() ......