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82 | 82 | xPMg = gdev(xPM) |
83 | 83 | yg = fy(θvecg, xPMg) |
84 | 84 | @test cdev(yg) == y_exp' |
85 | | - ygradg = Zygote.gradient(θv -> sum(fy(θv, xPMg)), θvecg)[1] # erros without ";" |
| 85 | + ygradg = Zygote.gradient(θv -> sum(fy(θv, xPMg)), θvecg)[1] # errors without ";" |
86 | 86 | @test ygradg isa CA.ComponentArray |
87 | 87 | @test CA.getdata(ygradg) isa GPUArraysCore.AbstractGPUArray |
88 | 88 | ygradgc = HVI.apply_preserve_axes(cdev, ygradg) # can print the cpu version |
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117 | 117 | y_uncg = gdev(y_unc) |
118 | 118 | costg = fcost(θvecg, xPMg, y_og, y_uncg) |
119 | 119 | @test costg ≈ cost |
120 | | - ygradg = Zygote.gradient(θv -> fcost(θv, xPMg, y_og, y_uncg), θvecg)[1] # erros without ";" |
| 120 | + ygradg = Zygote.gradient(θv -> fcost(θv, xPMg, y_og, y_uncg), θvecg)[1] # errors without ";" |
121 | 121 | @test ygradg isa CA.ComponentArray |
122 | 122 | @test CA.getdata(ygradg) isa GPUArraysCore.AbstractGPUArray |
123 | 123 | ygradgc = HVI.apply_preserve_axes(cdev, ygradg) # can print the cpu version |
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186 | 186 | # (xM, xP, y_o, y_unc, i_sites), batchsize = n_site_batch) |
187 | 187 | train_loader = get_hybridproblem_train_dataloader(prob; scenario) |
188 | 188 | @assert train_loader.data == (xM, xP, y_o, y_unc, i_sites) |
189 | | - train_lodaer2 = MLUtils.DataLoader(train_loader.data, batchsize = n_site) |
190 | 189 | pbm_covars = get_hybridproblem_pbmpar_covars(prob; scenario) |
191 | 190 |
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192 | 191 | #loss_gf = get_loss_gf(g, transM, f, y_global_o, intϕ; gdev = identity) |
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