Torch-nn学习: Simpley Layer

    xiaoxiao2021-12-12  46

    1.Linear:y = Ax + b

    module = nn.Linear(inputDimension, outputDimension, [bias = true]) module = nn.Linear(10, 5) -- 10 inputs, 5 outputs print(module.weight) //W print(module.bias) //b print(module.gradWeight) print(module.gradBias) 2.SparseLinear: y = Ax + b module = nn.SparseLinear(10000, 2) -- 10000 inputs, 2 outputs x = torch.Tensor({ {1, 0.1}, {2, 0.3}, {10, 0.3}, {31, 0.2} }) //dim not larger than 10000 3.Bilinear: forall k: y_k = x_1 A_k x_2 + b module = nn.Bilinear(inputDimension1, inputDimension2, outputDimension, [bias = true]) 4.Dropout:前向跟后向作用于相同位置,缩放了1/(1 - P) module = nn.Dropout(p) > module:forward(x) 0 4 0 0 10 12 0 16 [torch.DoubleTensor of dimension 2x4] > module:backward(x, x:clone():fill(1)) 0 2 0 0 2 2 0 2 [torch.DoubleTensor of dimension 2x4] 5.SpatialDropout:

    module = nn.SpatialDropout(p)

    对于比较靠前的卷积层,同一个feature map内的相邻像素关联较大,iid dropout没有好的正则化作用,因此用这个方法可以使一整个子区域同时失效或激活。这个操作假设最右边的两个维度是空间。 6.abs,Add,CAdd,Mul,CMul,Max,Min,Mean module = nn.Mean(dimension, nInputDim) module = nn.Sum(dimension, nInputDim, sizeAverage) 7. Euclidean,Cosine module = nn.Euclidean(inputSize,outputSize) module = nn.WeightedEuclidean(inputSize,outputSize) module = nn.Cosine(inputSize,outputSize) 8. module = nn.Identity() combine with  ParallelTable; pred_mlp = nn.Sequential() -- A network that makes predictions given x. pred_mlp:add(nn.Linear(5, 4)) pred_mlp:add(nn.Linear(4, 3)) xy_mlp = nn.ParallelTable() -- A network for predictions and for keeping the xy_mlp:add(pred_mlp) -- true label for comparison with a criterion xy_mlp:add(nn.Identity()) -- by forwarding both x and y through the network.//just like short-cut mlp = nn.Sequential() -- The main network that takes both x and y. mlp:add(xy_mlp) -- It feeds x and y to parallel networks; cr = nn.MSECriterion() cr_wrap = nn.CriterionTable(cr) mlp:add(cr_wrap) -- and then applies the criterion. for i = 1, 100 do -- Do a few training iterations x = torch.ones(5) -- Make input features. y = torch.Tensor(3) y:copy(x:narrow(1,1,3)) -- Make output label. err = mlp:forward{x,y} -- Forward both input and output. print(err) -- Print error from criterion. mlp:zeroGradParameters() -- Do backprop... mlp:backward({x, y}) mlp:updateParameters(0.05) end 9. module = nn.Copy(inputType, outputType, [forceCopy, dontCast]) module = nn.Narrow(dimension, offset, length) > x = torch.rand(4, 5) > x 0.3695 0.2017 0.4485 0.4638 0.0513 0.9222 0.1877 0.3388 0.6265 0.5659 0.8785 0.7394 0.8265 0.9212 0.0129 0.2290 0.7971 0.2113 0.1097 0.3166 [torch.DoubleTensor of size 4x5] > nn.Narrow(1, 2, 3):forward(x) 0.9222 0.1877 0.3388 0.6265 0.5659 0.8785 0.7394 0.8265 0.9212 0.0129 0.2290 0.7971 0.2113 0.1097 0.3166 [torch.DoubleTensor of size 3x5] 10. > x = torch.linspace(1, 5, 5) 1 2 3 4 5 [torch.DoubleTensor of dimension 5] > m = nn.Replicate(3) > o = m:forward(x) 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 11. > print(x) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 [torch.Tensor of dimension 4x4] > print(nn.Reshape(2,8):forward(x)) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 [torch.Tensor of dimension 2x8] 12.View,size = -1 for using mini-batch module = nn.View(sizes) > input = torch.Tensor(2, 3) > minibatch = torch.Tensor(5, 2, 3) > m = nn.View(-1):setNumInputDims(2) > print(#m:forward(input)) 6 [torch.LongStorage of size 1] > print(#m:forward(minibatch)) 5 6 [torch.LongStorage of size 2] 13.Select:选择第几个dim的第几个index module = nn.Select(dim, index) 14.Squeeze:若指定维度,则把对应维度压缩,否则压缩所有维度为1的维度。 module = nn.Squeeze([dim, numInputDims]) x=torch.rand(2,1,2,1,2) > x (1,1,1,.,.) = 0.6020 0.8897 (2,1,1,.,.) = 0.4713 0.2645 (1,1,2,.,.) = 0.4441 0.9792 (2,1,2,.,.) = 0.5467 0.8648 > torch.squeeze(x,2) (1,1,.,.) = 0.6020 0.8897 (2,1,.,.) = 0.4713 0.2645 (1,2,.,.) = 0.4441 0.9792 (2,2,.,.) = 0.5467 0.8648 [torch.DoubleTensor of dimension 2x2x1x2] 还有: module = nn.Unsqueeze(pos [, numInputDims]) 15. module = nn.Transpose({dim1, dim2} [, {dim3, dim4}, ...]) module = nn.Clamp(min_value, max_value) module = nn.Normalize(p, [eps]) module = nn.MM(transA, transB) module = nn.BatchNormalization(N [, eps] [, momentum] [,affine])

    module = nn.PixelShuffle(r)//used for super-resolution

    module = nn.Padding(dim, pad [, nInputDim, value, index]) penalty = nn.L1Penalty(L1weight, sizeAverage) Autoencoder会用: encoder = nn.Sequential() encoder:add(nn.Linear(3, 128)) encoder:add(nn.Threshold()) decoder = nn.Linear(128, 3) autoencoder = nn.Sequential() autoencoder:add(encoder) autoencoder:add(nn.L1Penalty(l1weight)) autoencoder:add(decoder) criterion = nn.MSECriterion() -- To measure reconstruction error module = nn.GradientReversal([lambda = 1]) 16. gpu = nn.GPU(module, device, [outdevice]) require 'cunn' gpu:cuda() eg:单块卡内存不够,可以这么用 mlp = nn.Sequential() :add(nn.GPU(nn.Linear(10000,10000), 1)) :add(nn.GPU(nn.Linear(10000,10000), 2)) :add(nn.GPU(nn.Linear(10000,10000), 3)) :add(nn.GPU(nn.Linear(10000,10000), 4, cutorch.getDevice()))

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