tarfile_extract_1fprn6vu/v634/RBatchGenerator__PyTorch_8py.html

import torch

import ROOT


tree_name = "sig_tree"

file_name = "http://root.cern/files/Higgs_data.root"


batch_size = 128

chunk_size = 5_000


rdataframe = ROOT.RDataFrame(tree_name, file_name)


target = "Type"


# Returns two generators that return training and validation batches

# as PyTorch tensors.

gen_train, gen_validation = ROOT.TMVA.Experimental.CreatePyTorchGenerators(

    rdataframe,

    batch_size,

    chunk_size,

    target=target,

    validation_split=0.3,

)


# Get a list of the columns used for training

input_columns = gen_train.train_columns

num_features = len(input_columns)


def calc_accuracy(targets, pred):

    return torch.sum(targets == pred.round()) / pred.size(0)


# Initialize PyTorch model

model = torch.nn.Sequential(

    torch.nn.Linear(num_features, 300),

    torch.nn.Tanh(),

    torch.nn.Linear(300, 300),

    torch.nn.Tanh(),

    torch.nn.Linear(300, 300),

    torch.nn.Tanh(),

    torch.nn.Linear(300, 1),

    torch.nn.Sigmoid(),

)

loss_fn = torch.nn.MSELoss(reduction="mean")

optimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.9)


number_of_epochs = 2


for _ in range(number_of_epochs):

    # Loop through the training set and train model

    for i, (x_train, y_train) in enumerate(gen_train):

        # Make prediction and calculate loss

        pred = model(x_train)

        loss = loss_fn(pred, y_train)


        # improve model

        model.zero_grad()

        loss.backward()

        optimizer.step()


        # Calculate accuracy

        accuracy = calc_accuracy(y_train, pred)


        print(f"Training => accuracy: {accuracy}")


    #################################################################

    # Validation

    #################################################################


    # Evaluate the model on the validation set

    for i, (x_train, y_train) in enumerate(gen_validation):

        # Make prediction and calculate accuracy

        pred = model(x_train)

        accuracy = calc_accuracy(y_train, pred)


        print(f"Validation => accuracy: {accuracy}")

len
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Definition TGWin32VirtualXProxy.cxx:249

ROOT::RDataFrame
ROOT's RDataFrame offers a modern, high-level interface for analysis of data stored in TTree ,...
Definition RDataFrame.hxx:41