[torch]how to read weights in nn model - neural-network

Question

I constructed the nn model using itorch notebook.
model = nn.Sequential()
model:add(nn.Reshape(ninputs))
model:add(nn.Linear(ninputs,noutputs))
Input data to the model
output = model:forward(input)
Then, I print the model and got this.
print(model)
nn.Sequential {
[input -> (1) -> (2) -> output]
(1): nn.Reshape(3072)
(2): nn.Linear(3072 -> 10)
}
{
gradInput : DoubleTensor - empty
modules :
{
1 :
nn.Reshape(3072)
{
_input : DoubleTensor - empty
nelement : 3072
train : true
output : DoubleTensor - size: 3072
gradInput : DoubleTensor - empty
size : LongStorage - size: 1
_gradOutput : DoubleTensor - empty
batchsize : LongStorage - size: 2
}
2 :
nn.Linear(3072 -> 10)
{
gradBias : DoubleTensor - size: 10
weight : DoubleTensor - size: 10x3072
train : true
bias : DoubleTensor - size: 10
gradInput : DoubleTensor - empty
gradWeight : DoubleTensor - size: 10x3072
output : DoubleTensor - size: 10
}
}
train : true
output : DoubleTensor - size: 10
}
how to read the weight in nn.linear ?
Thanks in advance.

Answer 1

Oh, it is similar to php
model.modules[2].weight

Answer 2

I find that model.modules[1].weight is similar to model:get(1).weight, but both can't get the parameters from the table layer like residual block. In this way the residual block as a layer.
however, we can use params, gradParams = model:parameters() to get the parameters for each layer even in the table layer.
It is worth noting that, in the second way, each layer of the network parameters are divided into two layers and arranged in layers