def train_simple_perceptron():
with Timer("Loading dataset"):
trainset, validset, testset = load_mnist()
with Timer("Creating model"):
# TODO: We should the number of different targets in the dataset,
# but I'm not sure how to do it right (keep in mind the regression?).
output_size = 10
model = Perceptron(trainset.input_size, output_size)
model.initialize() # By default, uniform initialization.
with Timer("Building optimizer"):
optimizer = SGD(loss=NLL(model, trainset))
optimizer.append_update_rule(ConstantLearningRate(0.0001))
with Timer("Building trainer"):
# Train for 10 epochs
batch_scheduler = MiniBatchScheduler(trainset, 100)
stopping_criterion = tasks.MaxEpochStopping(10)
trainer = Trainer(optimizer, batch_scheduler, stopping_criterion=stopping_criterion)
# Print time for one epoch
trainer.append_task(tasks.PrintEpochDuration())
trainer.append_task(tasks.PrintTrainingDuration())
# Print mean/stderror of classification errors.
classif_error = tasks.ClassificationError(model.use, validset)
trainer.append_task(tasks.Print("Validset - Classif error: {0:.1%} ± {1:.1%}", classif_error.mean, classif_error.stderror))
with Timer("Training"):
trainer.train()
