如何使用Torch7獲得預測

Question

我仍然熟悉Torch，並且迄今爲止都非常棒。然而，我卻遇到了一個死路，我不知道如何解決：我如何獲得Torch7（或更具體的dp庫）來評估單個輸入並返回預測的輸出？

這裏是我的設置（基本DP演示）：

require 'dp' 
--[[hyperparameters]]-- 
opt = { 
    nHidden = 100, --number of hidden units 
    learningRate = 0.1, --training learning rate 
    momentum = 0.9, --momentum factor to use for training 
    maxOutNorm = 1, --maximum norm allowed for output neuron weights 
    batchSize = 128, --number of examples per mini-batch 
    maxTries = 100, --maximum number of epochs without reduction in validation error. 
    maxEpoch = 1000 --maximum number of epochs of training 
} 
--[[data]]-- 
datasource = dp.Mnist{input_preprocess = dp.Standardize()} 
print("feature size: ", datasource:featureSize()) 

--[[Model]]-- 
model = dp.Sequential{ 
    models = { 
     dp.Neural{ 
      input_size = datasource:featureSize(), 
      output_size = opt.nHidden, 
      transfer = nn.Tanh(), 
      sparse_init = true 
     }, 
     dp.Neural{ 
      input_size = opt.nHidden, 
      output_size = #(datasource:classes()), 
      transfer = nn.LogSoftMax(), 
      sparse_init = true 
     } 
    } 
} 

--[[Propagators]]-- 
train = dp.Optimizer{ 
    loss = dp.NLL(), 
    visitor = { -- the ordering here is important: 
     dp.Momentum{momentum_factor = opt.momentum}, 
     dp.Learn{learning_rate = opt.learningRate}, 
     dp.MaxNorm{max_out_norm = opt.maxOutNorm} 
    }, 
    feedback = dp.Confusion(), 
    sampler = dp.ShuffleSampler{batch_size = opt.batchSize}, 
    progress = true 
} 
valid = dp.Evaluator{ 
    loss = dp.NLL(), 
    feedback = dp.Confusion(), 
    sampler = dp.Sampler{} 
} 
test = dp.Evaluator{ 
    loss = dp.NLL(), 
    feedback = dp.Confusion(), 
    sampler = dp.Sampler{} 
} 

--[[Experiment]]-- 
xp = dp.Experiment{ 
    model = model, 
    optimizer = train, 
    validator = valid, 
    tester = test, 
    observer = { 
     dp.FileLogger(), 
     dp.EarlyStopper{ 
      error_report = {'validator','feedback','confusion','accuracy'}, 
      maximize = true, 
      max_epochs = opt.maxTries 
     } 
    }, 
    random_seed = os.time(), 
    max_epoch = opt.maxEpoch 
} 

xp:run(datasource) 

Answer 1

你有兩個選擇。

一。使用封裝nn.Module轉發您的torch.Tensor：

mlp = model:toModule(datasource:trainSet():sub(1,2)) 
mlp:float() 
input = torch.FloatTensor(1, 1, 32, 32) -- replace this with your input 
output = mlp:forward(input) 

兩個。將您的torch.Tensor封裝成dp.ImageView，並通過您的dp.Model轉發：

input = torch.FloatTensor(1, 1, 32, 32) -- replace with your input 
inputView = dp.ImageView('bchw', input) 
outputView = mlp:forward(inputView, dp.Carry{nSample=1}) 
output = outputView:forward('b')