understanding_pytorch___03___named_parameters.py

for name, param in model.named_parameters():
    print('name: ', name)
    print(type(param))
    print('param.shape: ', param.shape)
    print('param.requires_grad: ', param.requires_grad)
    print('=====')

understanding_pytorch___03___named_parameters_output.py

name:  sequential.0.weight
<class 'torch.nn.parameter.Parameter'>
param.shape:  torch.Size([32, 1, 5, 5])
param.requires_grad:  True
=====
name:  sequential.0.bias
<class 'torch.nn.parameter.Parameter'>
param.shape:  torch.Size([32])
param.requires_grad:  True
=====
name:  sequential.1.weight
<class 'torch.nn.parameter.Parameter'>
param.shape:  torch.Size([64, 32, 5, 5])
param.requires_grad:  True
=====
name:  sequential.1.bias
<class 'torch.nn.parameter.Parameter'>
param.shape:  torch.Size([64])
param.requires_grad:  True
=====
name:  layer1.weight
<class 'torch.nn.parameter.Parameter'>
param.shape:  torch.Size([128, 64, 5, 5])
param.requires_grad:  True
=====
name:  layer1.bias
<class 'torch.nn.parameter.Parameter'>
param.shape:  torch.Size([128])
param.requires_grad:  True
=====
name:  layer2.weight
<class 'torch.nn.parameter.Parameter'>
param.shape:  torch.Size([256, 128, 5, 5])
param.requires_grad:  True
=====
name:  layer2.bias
<class 'torch.nn.parameter.Parameter'>
param.shape:  torch.Size([256])
param.requires_grad:  True
=====
name:  fc.weight
<class 'torch.nn.parameter.Parameter'>
param.shape:  torch.Size([128, 295936])
param.requires_grad:  True
=====
name:  fc.bias
<class 'torch.nn.parameter.Parameter'>
param.shape:  torch.Size([128])
param.requires_grad:  True
=====