ce0325cb72000af404c2c0b4c3ef1644a66bc0e4,examples/mnist.py,,get_new_model,#Any#Any#,76

Before Change

    new_model.tau = model.tau
    train_data = train_data + train_data
    random.shuffle(train_data)
    acc = new_model.train(train_data, batch_size=100)
    return acc, new_model


def sequential_sgd(model, train_data, check_data, n_iter=5):
    print(model.widths)

After Change

    new_model.tau = model.tau
    train_data = train_data + train_data
    random.shuffle(train_data)
    acc = 0.0
    for X, y in train_data:
        acc += new_model.update(Example.dense(model.nr_class, X, y))
    return acc / len(train_data), new_model


def sequential_sgd(model, train_data, check_data, n_iter=5):
    print(model.widths)

Instances