81933a874f108043a25f6980334f7242147ddf59,examples/example_mnist_tap_machine.py,,example_mnist_tap_machine,#,13

Before Change

    num_hidden_units = 256
    batch_size = 100
    learning_rate = schedules.power_law_decay(initial=0.1, coefficient=0.1)
    num_terms = 2

    (_, _, shuffled_filepath) = \
            util.default_paths(paysage_path)

    // set up the reader to get minibatches
    data = batch.HDFBatch(shuffled_filepath,
                         "train/images",
                          batch_size,
                          transform=batch.binarize_color,
                          train_fraction=0.95)

    // set up the model and initialize the parameters
    vis_layer = layers.BernoulliLayer(data.ncols)
    hid_layer = layers.BernoulliLayer(num_hidden_units)

    rbm = model.Model([vis_layer, hid_layer])
    rbm.initialize(data, "glorot_normal")

    perf  = fit.ProgressMonitor(data,
                                metrics=["ReconstructionError",
                                         "EnergyDistance",
                                         "HeatCapacity"])

    opt = optimizers.Gradient(stepsize=learning_rate,
                              tolerance=1e-4,
                              ascent=True)

    sampler = fit.DrivenSequentialMC.from_batch(rbm, data)

    sgd = fit.SGD(rbm, data, opt, num_epochs, sampler=sampler, method=fit.tap, monitor=perf)

    // fit the model
    print("Training with stochastic gradient ascent using TAP expansion to " + str(num_terms) + " terms.")
    sgd.train()

    util.show_metrics(rbm, perf)
    valid = data.get("validate")

After Change

    sgd = fit.SGD(rbm, data, opt, num_epochs, sampler=sampler, method=fit.tap, monitor=perf)

    // fit the model
    print("Training with stochastic gradient ascent using TAP expansion")
    sgd.train()

    util.show_metrics(rbm, perf)
    valid = data.get("validate")

Instances