train_dataset = datasets.MNIST('./data', train=True, download=True, transform=transform) local_train_datasets = dataset_split(train_dataset, n_workers)print(f"{datetime.now().strftime('%H:%M:%S')}Start training") ps = ParameterServer() ps_rref = rpc.RRef(ps) futs = []foridx, train...
This PR adds basic functionality to the Trainer class. Specifically, distributed PyTorch training can now be run. Example usage: def train_func(config): # training code trainer = Trainer("torch") trainer.start() results = trainer.run(train_func) trainer.shutdown() A few runnable examples are...
trainer.train(train_dataset)defrun_ps(trainers): transform=transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,)) ]) train_dataset = datasets.MNIST('./data', train=True, download=True, transform=transform) local_train_datasets = dataset_split(train_dataset, n_w...
NumberOfIterations = 30, // Give the instances of the positive class slightly more weight. PositiveInstanceWeight = 1.2f, }; // Define the trainer. var pipeline = mlContext.BinaryClassification.Trainers .SgdCalibrated(options); // Train the model. var model = pipeline.Fit(trainingData); //...
NumberOfIterations = 30, // Give the instances of the positive class slightly more weight. PositiveInstanceWeight = 1.2f, }; // Define the trainer. var pipeline = mlContext.BinaryClassification.Trainers .SgdCalibrated(options); // Train the model. var model = pipeline.Fit(trainingData...
优化器目前优化器主要分为两个方向:1. The acceleratedSGD:SGDmomemtum2. The adaptive learning rate methods: Adam SGDM:收敛慢,更好的精度,比较稳定,train和val的差距比较小Adam:收敛快,可能不收敛,不那么稳定,generalization performance比较差。 深度学习中sgd优化器的原理 ...
优化器目前优化器主要分为两个方向:1. The acceleratedSGD:SGDmomemtum2. The adaptive learning rate methods: Adam SGDM:收敛慢,更好的精度,比较稳定,train和val的差距比较小Adam:收敛快,可能不收敛,不那么稳定,generalization performance比较差。 深度学习中sgd优化器的原理 ...
# sgd trainer source("logistic.R") lr.sgd.train <- function(env) { M <- env$data.summary$n.users N <- env$data.summary$n.items D <- env$config$l.dim train.data <- env$fil.data[env$train.samples,] n.samples <- dim(train.data)[1] # model parameters user.mat <- matrix(da...
// Define the trainer.var pipeline = mlContext.BinaryClassification.Trainers .SgdNonCalibrated(); // Train the model.var model = pipeline.Fit(trainingData); // Create testing data. Use different random seed to make it different // from training data.var...
var options = new SymbolicSgdLogisticRegressionBinaryTrainer.Options() { LearningRate = 0.2f, NumberOfIterations = 10, NumberOfThreads = 1, }; // Define the trainer. var pipeline = mlContext.BinaryClassification.Trainers .SymbolicSgdLogisticRegression(options); // Train the model. var model = ...