Example #1
0
def main():
    # First, let's create a simple feedforward MLP with one hidden layer as a Prototype.
    mlp = Prototype()
    mlp.add(
        BasicLayer(input_size=28 * 28,
                   output_size=1000,
                   activation='rectifier',
                   noise='dropout'))
    mlp.add(SoftmaxLayer(output_size=10))

    # Now, we get to choose what values we want to monitor, and what datasets we would like to monitor on!
    # Each Model (in our case, the Prototype), has a get_monitors method that will return a useful
    # dictionary of {string_name: monitor_theano_expression} for various computations of the model we might
    # care about. By default, this method returns an empty dictionary - it was the model creator's job to
    # include potential monitor values.
    mlp_monitors = mlp.get_monitors()
    mlp_channel = MonitorsChannel(name="error")
    for name, expression in mlp_monitors.items():
        mlp_channel.add(
            Monitor(name=name,
                    expression=expression,
                    train=True,
                    valid=True,
                    test=True))

    # create some monitors for statistics about the hidden and output weights!
    # let's look at the mean, variance, and standard deviation of the weights matrices.
    weights_channel = MonitorsChannel(name="weights")
    hiddens_1 = mlp[0].get_params()[0]
    hiddens1_mean = T.mean(hiddens_1)
    weights_channel.add(
        Monitor(name="hiddens_mean", expression=hiddens1_mean, train=True))

    hiddens_2 = mlp[1].get_params()[0]
    hiddens2_mean = T.mean(hiddens_2)
    weights_channel.add(
        Monitor(name="out_mean", expression=hiddens2_mean, train=True))

    # create our plot object to do live plotting!
    plot = Plot(bokeh_doc_name="Monitor Tutorial",
                monitor_channels=[mlp_channel, weights_channel],
                open_browser=True)

    # use SGD optimizer
    optimizer = SGD(model=mlp,
                    dataset=MNIST(concat_train_valid=False),
                    n_epoch=500,
                    save_frequency=100,
                    batch_size=600,
                    learning_rate=.01,
                    lr_decay=False,
                    momentum=.9,
                    nesterov_momentum=True)

    # train, with the plot!
    optimizer.train(plot=plot)
def main():
    # First, let's create a simple feedforward MLP with one hidden layer as a Prototype.
    mlp = Prototype()
    mlp.add(BasicLayer(input_size=28*28, output_size=1000, activation='rectifier', noise='dropout'))
    mlp.add(SoftmaxLayer(output_size=10))

    # Now, we get to choose what values we want to monitor, and what datasets we would like to monitor on!
    # Each Model (in our case, the Prototype), has a get_monitors method that will return a useful
    # dictionary of {string_name: monitor_theano_expression} for various computations of the model we might
    # care about. By default, this method returns an empty dictionary - it was the model creator's job to
    # include potential monitor values.
    mlp_monitors = mlp.get_monitors()
    mlp_channel = MonitorsChannel(name="error")
    for name, expression in mlp_monitors.items():
        mlp_channel.add(Monitor(name=name, expression=expression, train=True, valid=True, test=True))

    # create some monitors for statistics about the hidden and output weights!
    # let's look at the mean, variance, and standard deviation of the weights matrices.
    weights_channel = MonitorsChannel(name="weights")
    hiddens_1 = mlp[0].get_params()[0]
    hiddens1_mean = T.mean(hiddens_1)
    weights_channel.add(Monitor(name="hiddens_mean", expression=hiddens1_mean, train=True))

    hiddens_2 = mlp[1].get_params()[0]
    hiddens2_mean = T.mean(hiddens_2)
    weights_channel.add(Monitor(name="out_mean", expression=hiddens2_mean, train=True))

    # create our plot object to do live plotting!
    plot = Plot(bokeh_doc_name="Monitor Tutorial", monitor_channels=[mlp_channel, weights_channel], open_browser=True)

    # use SGD optimizer
    optimizer = SGD(model=mlp,
                    dataset=MNIST(concat_train_valid=False),
                    n_epoch=500,
                    save_frequency=100,
                    batch_size=600,
                    learning_rate=.01,
                    lr_decay=False,
                    momentum=.9,
                    nesterov_momentum=True)

    # train, with the plot!
    optimizer.train(plot=plot)
def setup_optimization(model, n_epoch, mnist_dataset):
    # setup optimizer stochastic gradient descent 
    optimizer = SGD(model=model,
                    dataset=mnist_dataset,
                    n_epoch=n_epoch,
                    batch_size=600,
                    learning_rate=.01,
                    momentum=.9,
                    nesterov_momentum=True,
                    save_frequency=500,
                    early_stop_threshold=0.997)

    # create a Monitor to view progress on a metric other than training cost
    error = Monitor('error', model.get_monitors()['softmax_error'], train=True, valid=True, test=True)

    return optimizer, error
Example #4
0
def main():
    ########################################
    # Initialization things with arguments #
    ########################################
    # use these arguments to get results from paper referenced above
    _train_args = {"epochs": 1000,  # maximum number of times to run through the dataset
                   "batch_size": 100,  # number of examples to process in parallel (minibatch)
                   "min_batch_size": 1,  # the minimum number of examples for a batch to be considered
                   "save_freq": 1,  # how many epochs between saving parameters
                   "stop_threshold": .9995,  # multiplier for how much the train cost to improve to not stop early
                   "stop_patience": 500,  # how many epochs to wait to see if the threshold has been reached
                   "learning_rate": .25,  # initial learning rate for SGD
                   "lr_decay": 'exponential',  # the decay function to use for the learning rate parameter
                   "lr_decay_factor": .995,  # by how much to decay the learning rate each epoch
                   "momentum": 0.5,  # the parameter momentum amount
                   'momentum_decay': False,  # how to decay the momentum each epoch (if applicable)
                   'momentum_factor': 0,  # by how much to decay the momentum (in this case not at all)
                   'nesterov_momentum': False,  # whether to use nesterov momentum update (accelerated momentum)
    }

    config_root_logger()
    log.info("Creating a new GSN")

    mnist = MNIST(concat_train_valid=True)
    gsn = GSN(layers=2,
              walkbacks=4,
              hidden_size=1500,
              visible_activation='sigmoid',
              hidden_activation='tanh',
              input_size=28*28,
              tied_weights=True,
              hidden_add_noise_sigma=2,
              input_salt_and_pepper=0.4,
              outdir='outputs/test_gsn/',
              vis_init=False,
              noiseless_h1=True,
              input_sampling=True,
              weights_init='uniform',
              weights_interval='montreal',
              bias_init=0,
              cost_function='binary_crossentropy')

    recon_cost_channel = MonitorsChannel(name='cost')
    recon_cost_channel.add(Monitor('recon_cost', gsn.get_monitors()['recon_cost'], test=True))
    recon_cost_channel.add(Monitor('noisy_recon_cost', gsn.get_monitors()['noisy_recon_cost'], test=True))

    # Load initial weights and biases from file
    # params_to_load = '../../../outputs/gsn/mnist/trained_epoch_395.pkl'
    # gsn.load_params(params_to_load)

    optimizer = SGD(model=gsn, dataset=mnist, **_train_args)
    # optimizer = AdaDelta(model=gsn, dataset=mnist, epochs=200, batch_size=100, learning_rate=1e-6)
    optimizer.train(monitor_channels=recon_cost_channel)

    # Save some reconstruction output images
    n_examples = 100
    xs_test = mnist.test_inputs[:n_examples]
    noisy_xs_test = gsn.f_noise(xs_test)
    reconstructed = gsn.run(noisy_xs_test)
    # Concatenate stuff
    stacked = numpy.vstack(
        [numpy.vstack([xs_test[i * 10: (i + 1) * 10],
                       noisy_xs_test[i * 10: (i + 1) * 10],
                       reconstructed[i * 10: (i + 1) * 10]])
         for i in range(10)])
    number_reconstruction = PIL.Image.fromarray(
        tile_raster_images(stacked, (gsn.image_height, gsn.image_width), (10, 30))
    )

    number_reconstruction.save(gsn.outdir + 'reconstruction.png')
    log.info("saved output image!")

    # Construct image from the weight matrix
    image = PIL.Image.fromarray(
        tile_raster_images(
            X=gsn.weights_list[0].get_value(borrow=True).T,
            img_shape=(28, 28),
            tile_shape=closest_to_square_factors(gsn.hidden_size),
            tile_spacing=(1, 1)
        )
    )
    image.save(gsn.outdir + "gsn_mnist_weights.png")
Example #5
0
def add_list_layers():
    # You can also add lists of layers at a time (or as initialization) to a Prototype! This lets you specify
    # more complex interactions between layers!
    hidden1 = BasicLayer(input_size=28 * 28,
                         output_size=512,
                         activation='rectifier',
                         noise='dropout')

    hidden2 = BasicLayer(inputs_hook=(512, hidden1.get_outputs()),
                         output_size=512,
                         activation='rectifier',
                         noise='dropout')

    class_layer = SoftmaxLayer(inputs_hook=(512, hidden2.get_outputs()),
                               output_size=10)

    mlp = Prototype([hidden1, hidden2, class_layer])
    return mlp


if __name__ == '__main__':
    mlp = sequential_add_layers()
    optimizer = SGD(model=mlp,
                    dataset=MNIST(concat_train_valid=True),
                    n_epoch=500,
                    batch_size=600,
                    learning_rate=.01,
                    momentum=.9,
                    nesterov_momentum=True)
    optimizer.train()
Example #6
0
def run_midi(dataset):
    log.info("Creating RNN-RBM for dataset %s!", dataset)

    outdir = "outputs/rnnrbm/%s/" % dataset

    # grab the MIDI dataset
    if dataset == 'nottingham':
        midi = Nottingham()
    elif dataset == 'jsb':
        midi = JSBChorales()
    elif dataset == 'muse':
        midi = MuseData()
    elif dataset == 'piano_de':
        midi = PianoMidiDe()
    else:
        raise AssertionError("dataset %s not recognized." % dataset)

    # create the RNN-RBM
    # rng = numpy.random
    # rng.seed(0xbeef)
    # mrg = RandomStreams(seed=rng.randint(1 << 30))
    rng = numpy.random.RandomState(1234)
    mrg = RandomStreams(rng.randint(2 ** 30))
    # rnnrbm = RNN_RBM(input_size=88,
    #                  hidden_size=150,
    #                  rnn_hidden_size=100,
    #                  k=15,
    #                  weights_init='gaussian',
    #                  weights_std=0.01,
    #                  rnn_weights_init='gaussian',
    #                  rnn_weights_std=0.0001,
    #                  rng=rng,
    #                  outdir=outdir)
    rnnrbm = RNN_RBM(input_size=88,
                     hidden_size=150,
                     rnn_hidden_size=100,
                     k=15,
                     weights_init='gaussian',
                     weights_std=0.01,
                     rnn_weights_init='identity',
                     rnn_hidden_activation='relu',
                     # rnn_weights_init='gaussian',
                     # rnn_hidden_activation='tanh',
                     rnn_weights_std=0.0001,
                     mrg=mrg,
                     outdir=outdir)

    # make an optimizer to train it
    optimizer = SGD(model=rnnrbm,
                    dataset=midi,
                    n_epoch=200,
                    batch_size=100,
                    minimum_batch_size=2,
                    learning_rate=.001,
                    save_frequency=10,
                    early_stop_length=200,
                    momentum=False,
                    momentum_decay=False,
                    nesterov_momentum=False)

    optimizer = AdaDelta(model=rnnrbm,
                         dataset=midi,
                         n_epoch=200,
                         batch_size=100,
                         minimum_batch_size=2,
                         # learning_rate=1e-4,
                         learning_rate=1e-6,
                         save_frequency=10,
                         early_stop_length=200)

    ll = Monitor('pseudo-log', rnnrbm.get_monitors()['pseudo-log'], test=True)
    mse = Monitor('frame-error', rnnrbm.get_monitors()['mse'], valid=True, test=True)

    plot = Plot(bokeh_doc_name='rnnrbm_midi_%s' % dataset, monitor_channels=[ll, mse], open_browser=True)

    # perform training!
    optimizer.train(plot=plot)
    # use the generate function!
    generated, _ = rnnrbm.generate(initial=None, n_steps=200)

    dt = 0.3
    r = (21, 109)
    midiwrite(outdir + 'rnnrbm_generated_midi.mid', generated, r=r, dt=dt)

    if has_pylab:
        extent = (0, dt * len(generated)) + r
        pylab.figure()
        pylab.imshow(generated.T, origin='lower', aspect='auto',
                     interpolation='nearest', cmap=pylab.cm.gray_r,
                     extent=extent)
        pylab.xlabel('time (s)')
        pylab.ylabel('MIDI note number')
        pylab.title('generated piano-roll')

    # Construct image from the weight matrix
    image = Image.fromarray(
        tile_raster_images(
            X=rnnrbm.W.get_value(borrow=True).T,
            img_shape=closest_to_square_factors(rnnrbm.input_size),
            tile_shape=closest_to_square_factors(rnnrbm.hidden_size),
            tile_spacing=(1, 1)
        )
    )
    image.save(outdir + 'rnnrbm_midi_weights.png')

    log.debug("done!")
    del midi
    del rnnrbm
    del optimizer
Example #7
0
    # although this is recommended over print statements everywhere
    import logging
    import opendeep.log.logger as logger
    logger.config_root_logger()
    log = logging.getLogger(__name__)
    log.info("Creating RBM!")

    # grab the MNIST dataset
    mnist = MNIST(concat_train_valid=False)
    # create the RBM
    rng = numpy.random.RandomState(1234)
    mrg = theano.tensor.shared_randomstreams.RandomStreams(rng.randint(2**30))
    rbm = RBM(input_size=28*28, hidden_size=500, k=15, weights_init='uniform', weights_interval=4*numpy.sqrt(6./(28*28+500)), rng=rng)
    # rbm.load_params('rbm_trained.pkl')
    # make an optimizer to train it (AdaDelta is a good default)
    optimizer = SGD(model=rbm, dataset=mnist, n_epoch=15, batch_size=20, learning_rate=0.1, lr_decay=False, nesterov_momentum=False)
    # optimizer = AdaDelta(model=rbm, dataset=mnist, n_epoch=200, batch_size=100, learning_rate=1e-6)
    # perform training!
    optimizer.train()
    # test it on some images!
    test_data = mnist.getSubset(TEST)[0]
    test_data = test_data[:25].eval()
    # use the run function!
    preds = rbm.run(test_data)

    # Construct image from the test matrix
    image = Image.fromarray(
        tile_raster_images(
            X=test_data,
            img_shape=(28, 28),
            tile_shape=(5, 5),
Example #8
0
def run_midi(dataset):
    log.info("Creating RNN-RBM for dataset %s!", dataset)

    outdir = "outputs/rnnrbm/%s/" % dataset

    # grab the MIDI dataset
    if dataset == 'nottingham':
        midi = Nottingham()
    elif dataset == 'jsb':
        midi = JSBChorales()
    elif dataset == 'muse':
        midi = MuseData()
    elif dataset == 'piano_de':
        midi = PianoMidiDe()
    else:
        raise AssertionError("dataset %s not recognized." % dataset)

    # create the RNN-RBM
    # rng = numpy.random
    # rng.seed(0xbeef)
    # mrg = RandomStreams(seed=rng.randint(1 << 30))
    rng = numpy.random.RandomState(1234)
    mrg = RandomStreams(rng.randint(2**30))
    # rnnrbm = RNN_RBM(input_size=88,
    #                  hidden_size=150,
    #                  rnn_hidden_size=100,
    #                  k=15,
    #                  weights_init='gaussian',
    #                  weights_std=0.01,
    #                  rnn_weights_init='gaussian',
    #                  rnn_weights_std=0.0001,
    #                  rng=rng,
    #                  outdir=outdir)
    rnnrbm = RNN_RBM(
        input_size=88,
        hidden_size=150,
        rnn_hidden_size=100,
        k=15,
        weights_init='gaussian',
        weights_std=0.01,
        rnn_weights_init='identity',
        rnn_hidden_activation='relu',
        # rnn_weights_init='gaussian',
        # rnn_hidden_activation='tanh',
        rnn_weights_std=0.0001,
        mrg=mrg,
        outdir=outdir)

    # make an optimizer to train it
    optimizer = SGD(model=rnnrbm,
                    dataset=midi,
                    epochs=200,
                    batch_size=100,
                    min_batch_size=2,
                    learning_rate=.001,
                    save_freq=10,
                    stop_patience=200,
                    momentum=False,
                    momentum_decay=False,
                    nesterov_momentum=False)

    optimizer = AdaDelta(
        model=rnnrbm,
        dataset=midi,
        epochs=200,
        batch_size=100,
        min_batch_size=2,
        # learning_rate=1e-4,
        learning_rate=1e-6,
        save_freq=10,
        stop_patience=200)

    ll = Monitor('pseudo-log', rnnrbm.get_monitors()['pseudo-log'], test=True)
    mse = Monitor('frame-error',
                  rnnrbm.get_monitors()['mse'],
                  valid=True,
                  test=True)

    plot = Plot(bokeh_doc_name='rnnrbm_midi_%s' % dataset,
                monitor_channels=[ll, mse],
                open_browser=True)

    # perform training!
    optimizer.train(plot=plot)
    # use the generate function!
    generated, _ = rnnrbm.generate(initial=None, n_steps=200)

    dt = 0.3
    r = (21, 109)
    midiwrite(outdir + 'rnnrbm_generated_midi.mid', generated, r=r, dt=dt)

    if has_pylab:
        extent = (0, dt * len(generated)) + r
        pylab.figure()
        pylab.imshow(generated.T,
                     origin='lower',
                     aspect='auto',
                     interpolation='nearest',
                     cmap=pylab.cm.gray_r,
                     extent=extent)
        pylab.xlabel('time (s)')
        pylab.ylabel('MIDI note number')
        pylab.title('generated piano-roll')

    # Construct image from the weight matrix
    image = Image.fromarray(
        tile_raster_images(
            X=rnnrbm.W.get_value(borrow=True).T,
            img_shape=closest_to_square_factors(rnnrbm.input_size),
            tile_shape=closest_to_square_factors(rnnrbm.hidden_size),
            tile_spacing=(1, 1)))
    image.save(outdir + 'rnnrbm_midi_weights.png')

    log.debug("done!")
    del midi
    del rnnrbm
    del optimizer
Example #9
0
def main():
    ########################################
    # Initialization things with arguments #
    ########################################
    # use these arguments to get results from paper referenced above
    _train_args = {"n_epoch": 1000,  # maximum number of times to run through the dataset
                   "batch_size": 100,  # number of examples to process in parallel (minibatch)
                   "minimum_batch_size": 1,  # the minimum number of examples for a batch to be considered
                   "save_frequency": 1,  # how many epochs between saving parameters
                   "early_stop_threshold": .9995,  # multiplier for how much the train cost to improve to not stop early
                   "early_stop_length": 500,  # how many epochs to wait to see if the threshold has been reached
                   "learning_rate": .25,  # initial learning rate for SGD
                   "lr_decay": 'exponential',  # the decay function to use for the learning rate parameter
                   "lr_factor": .995,  # by how much to decay the learning rate each epoch
                   "momentum": 0.5,  # the parameter momentum amount
                   'momentum_decay': False,  # how to decay the momentum each epoch (if applicable)
                   'momentum_factor': 0,  # by how much to decay the momentum (in this case not at all)
                   'nesterov_momentum': False,  # whether to use nesterov momentum update (accelerated momentum)
    }

    config_root_logger()
    log.info("Creating a new GSN")

    mnist = MNIST(concat_train_valid=True)
    gsn = GSN(layers=2,
              walkbacks=4,
              hidden_size=1500,
              visible_activation='sigmoid',
              hidden_activation='tanh',
              input_size=28*28,
              tied_weights=True,
              hidden_add_noise_sigma=2,
              input_salt_and_pepper=0.4,
              outdir='outputs/test_gsn/',
              vis_init=False,
              noiseless_h1=True,
              input_sampling=True,
              weights_init='uniform',
              weights_interval='montreal',
              bias_init=0,
              cost_function='binary_crossentropy')

    recon_cost_channel = MonitorsChannel(name='cost')
    recon_cost_channel.add(Monitor('recon_cost', gsn.get_monitors()['recon_cost'], test=True))
    recon_cost_channel.add(Monitor('noisy_recon_cost', gsn.get_monitors()['noisy_recon_cost'], test=True))

    # Load initial weights and biases from file
    # params_to_load = '../../../outputs/gsn/mnist/trained_epoch_395.pkl'
    # gsn.load_params(params_to_load)

    optimizer = SGD(model=gsn, dataset=mnist, **_train_args)
    # optimizer = AdaDelta(model=gsn, dataset=mnist, n_epoch=200, batch_size=100, learning_rate=1e-6)
    optimizer.train(monitor_channels=recon_cost_channel)

    # Save some reconstruction output images
    import opendeep.data.dataset as datasets
    n_examples = 100
    xs_test, _ = mnist.getSubset(datasets.TEST)
    xs_test = xs_test[:n_examples].eval()
    noisy_xs_test = gsn.f_noise(xs_test)
    reconstructed = gsn.run(noisy_xs_test)
    # Concatenate stuff
    stacked = numpy.vstack(
        [numpy.vstack([xs_test[i * 10: (i + 1) * 10],
                       noisy_xs_test[i * 10: (i + 1) * 10],
                       reconstructed[i * 10: (i + 1) * 10]])
         for i in range(10)])
    number_reconstruction = PIL.Image.fromarray(
        tile_raster_images(stacked, (gsn.image_height, gsn.image_width), (10, 30))
    )

    number_reconstruction.save(gsn.outdir + 'reconstruction.png')
    log.info("saved output image!")

    # Construct image from the weight matrix
    image = PIL.Image.fromarray(
        tile_raster_images(
            X=gsn.weights_list[0].get_value(borrow=True).T,
            img_shape=(28, 28),
            tile_shape=closest_to_square_factors(gsn.hidden_size),
            tile_spacing=(1, 1)
        )
    )
    image.save(gsn.outdir + "gsn_mnist_weights.png")