Пример #1
0
def test_mlp(learning_rate=0.01, L1_reg=0.00, L2_reg=0.0001, n_epochs=1000,
             dataset='mnist.pkl.gz', batch_size=20, n_hidden=500):
    """
    Demonstrate stochastic gradient descent optimization for a multilayer
    perceptron

    This is demonstrated on MNIST.

    :type learning_rate: float
    :param learning_rate: learning rate used (factor for the stochastic
    gradient

    :type L1_reg: float
    :param L1_reg: L1-norm's weight when added to the cost (see
    regularization)

    :type L2_reg: float
    :param L2_reg: L2-norm's weight when added to the cost (see
    regularization)

    :type n_epochs: int
    :param n_epochs: maximal number of epochs to run the optimizer

    :type dataset: string
    :param dataset: the path of the MNIST dataset file from
                 http://www.iro.umontreal.ca/~lisa/deep/data/mnist/mnist.pkl.gz


   """
    datasets = load_data(dataset)

    train_set_x, train_set_y = datasets[0]
    valid_set_x, valid_set_y = datasets[1]
    test_set_x, test_set_y = datasets[2]

    # compute number of minibatches for training, validation and testing
    n_train_batches = train_set_x.get_value(borrow=True).shape[0] / batch_size
    n_valid_batches = valid_set_x.get_value(borrow=True).shape[0] / batch_size
    n_test_batches = test_set_x.get_value(borrow=True).shape[0] / batch_size

    ######################
    # BUILD ACTUAL MODEL #
    ######################
    print('... building the model')

    # allocate symbolic variables for the data
    index = T.lscalar()  # index to a [mini]batch
    x = T.matrix('x')  # the data is presented as rasterized images
    y = T.ivector('y')  # the labels are presented as 1D vector of
                        # [int] labels

    rng = numpy.random.RandomState(1234)

    # construct the MLP class
    classifier = MLP(
        rng=rng,
        input=x,
        n_in=28 * 28,
        n_hidden=n_hidden,
        n_out=10
    )

    # start-snippet-4
    # the cost we minimize during training is the negative log likelihood of
    # the model plus the regularization terms (L1 and L2); cost is expressed
    # here symbolically
    cost = (
        classifier.negative_log_likelihood(y)
        + L1_reg * classifier.L1
        + L2_reg * classifier.L2_sqr
    )
    # end-snippet-4

    # compiling a Theano function that computes the mistakes that are made
    # by the model on a minibatch
    test_model = theano.function(
        inputs=[index],
        outputs=classifier.errors(y),
        givens={
            x: test_set_x[index * batch_size:(index + 1) * batch_size],
            y: test_set_y[index * batch_size:(index + 1) * batch_size]
        }
    )

    validate_model = theano.function(
        inputs=[index],
        outputs=classifier.errors(y),
        givens={
            x: valid_set_x[index * batch_size:(index + 1) * batch_size],
            y: valid_set_y[index * batch_size:(index + 1) * batch_size]
        }
    )

    # start-snippet-5
    # compute the gradient of cost with respect to theta (sotred in params)
    # the resulting gradients will be stored in a list gparams
    gparams = [T.grad(cost, param) for param in classifier.params]

    # specify how to update the parameters of the model as a list of
    # (variable, update expression) pairs

    # given two lists of the same length, A = [a1, a2, a3, a4] and
    # B = [b1, b2, b3, b4], zip generates a list C of same size, where each
    # element is a pair formed from the two lists :
    #    C = [(a1, b1), (a2, b2), (a3, b3), (a4, b4)]
    updates = [
        (param, param - learning_rate * gparam)
        for param, gparam in zip(classifier.params, gparams)
    ]

    # compiling a Theano function `train_model` that returns the cost, but
    # in the same time updates the parameter of the model based on the rules
    # defined in `updates`
    train_model = theano.function(
        inputs=[index],
        outputs=cost,
        updates=updates,
        givens={
            x: train_set_x[index * batch_size: (index + 1) * batch_size],
            y: train_set_y[index * batch_size: (index + 1) * batch_size]
        }
    )
    # end-snippet-5

    ###############
    # TRAIN MODEL #
    ###############
    print('... training')

    # early-stopping parameters
    patience = 10000  # look as this many examples regardless
    patience_increase = 2  # wait this much longer when a new best is
                           # found
    improvement_threshold = 0.995  # a relative improvement of this much is
                                   # considered significant
    validation_frequency = min(n_train_batches, patience / 2)
                                  # go through this many
                                  # minibatche before checking the network
                                  # on the validation set; in this case we
                                  # check every epoch

    best_validation_loss = numpy.inf
    best_iter = 0
    test_score = 0.
    start_time = timeit.default_timer()

    epoch = 0
    done_looping = False

    while (epoch < n_epochs) and (not done_looping):
        epoch = epoch + 1
        for minibatch_index in range(n_train_batches):

            minibatch_avg_cost = train_model(minibatch_index)
            # iteration number
            iter = (epoch - 1) * n_train_batches + minibatch_index

            if (iter + 1) % validation_frequency == 0:
                # compute zero-one loss on validation set
                validation_losses = [validate_model(i) for i
                                     in range(n_valid_batches)]
                this_validation_loss = numpy.mean(validation_losses)

                print(
                    'epoch %i, minibatch %i/%i, validation error %f %%' %
                    (
                        epoch,
                        minibatch_index + 1,
                        n_train_batches,
                        this_validation_loss * 100.
                    )
                )

                # if we got the best validation score until now
                if this_validation_loss < best_validation_loss:
                    #improve patience if loss improvement is good enough
                    if (
                        this_validation_loss < best_validation_loss *
                        improvement_threshold
                    ):
                        patience = max(patience, iter * patience_increase)

                    best_validation_loss = this_validation_loss
                    best_iter = iter

                    # test it on the test set
                    test_losses = [test_model(i) for i
                                   in range(n_test_batches)]
                    test_score = numpy.mean(test_losses)

                    print(('     epoch %i, minibatch %i/%i, test error of '
                           'best model %f %%') %
                          (epoch, minibatch_index + 1, n_train_batches,
                           test_score * 100.))

            if patience <= iter:
                done_looping = True
                break

    end_time = timeit.default_timer()
    print(('Optimization complete. Best validation score of %f %% '
           'obtained at iteration %i, with test performance %f %%') %
          (best_validation_loss * 100., best_iter + 1, test_score * 100.))
    print('The code for file ' +
          os.path.split(__file__)[1] +
          ' ran for %.2fm' % ((end_time - start_time) / 60.),
          File=sys.stderr)
Пример #2
0
def test_DBN(finetune_lr=0.1, pretraining_epochs=100,
             pretrain_lr=0.01, k=1, training_epochs=1000,
             dataset='mnist.pkl.gz', batch_size=10):
    """
    Demonstrates how to train and test a Deep Belief Network.

    This is demonstrated on MNIST.

    :type finetune_lr: float
    :param finetune_lr: learning rate used in the finetune stage
    :type pretraining_epochs: int
    :param pretraining_epochs: number of epoch to do pretraining
    :type pretrain_lr: float
    :param pretrain_lr: learning rate to be used during pre-training
    :type k: int
    :param k: number of Gibbs steps in CD/PCD
    :type training_epochs: int
    :param training_epochs: maximal number of iterations ot run the optimizer
    :type dataset: string
    :param dataset: path the the pickled dataset
    :type batch_size: int
    :param batch_size: the size of a minibatch
    """

    datasets = load_data(dataset)

    train_set_x, train_set_y = datasets[0]
    valid_set_x, valid_set_y = datasets[1]
    test_set_x, test_set_y = datasets[2]

    # compute number of minibatches for training, validation and testing
    n_train_batches = train_set_x.get_value(borrow=True).shape[0] / batch_size

    # numpy random generator
    numpy_rng = numpy.random.RandomState(123)
    print('... building the model')
    # construct the Deep Belief Network
    dbn = DBN(numpy_rng=numpy_rng, n_ins=28 * 28,
              hidden_layers_sizes=[1000, 1000, 1000],
              n_outs=10)

    # start-snippet-2
    #########################
    # PRETRAINING THE MODEL #
    #########################
    print('... getting the pretraining functions')
    pretraining_fns = dbn.pretraining_functions(train_set_x=train_set_x,
                                                batch_size=batch_size,
                                                k=k)

    print('... pre-training the model')
    start_time = timeit.default_timer()
    ## Pre-train layer-wise
    for i in range(dbn.n_layers):
        # go through pretraining epochs
        for epoch in range(pretraining_epochs):
            # go through the training set
            c = []
            for batch_index in range(n_train_batches):
                c.append(pretraining_fns[i](index=batch_index,
                                            lr=pretrain_lr))
            print('Pre-training layer %i, epoch %d, cost ' % (i, epoch),)
            print(numpy.mean(c))

    end_time = timeit.default_timer()
    # end-snippet-2
    print(('The pretraining code for file ' +
           os.path.split(__file__)[1] +
           ' ran for %.2fm' % ((end_time - start_time) / 60.)),
          File=sys.stderr)
    ########################
    # FINETUNING THE MODEL #
    ########################

    # get the training, validation and testing function for the model
    print('... getting the finetuning functions')
    train_fn, validate_model, test_model = dbn.build_finetune_functions(
        datasets=datasets,
        batch_size=batch_size,
        learning_rate=finetune_lr
    )

    print('... finetuning the model')
    # early-stopping parameters
    patience = 4 * n_train_batches  # look as this many examples regardless
    patience_increase = 2.    # wait this much longer when a new best is
                              # found
    improvement_threshold = 0.995  # a relative improvement of this much is
                                   # considered significant
    validation_frequency = min(n_train_batches, patience / 2)
                                  # go through this many
                                  # minibatches before checking the network
                                  # on the validation set; in this case we
                                  # check every epoch

    best_validation_loss = numpy.inf
    test_score = 0.
    start_time = timeit.default_timer()

    done_looping = False
    epoch = 0

    while (epoch < training_epochs) and (not done_looping):
        epoch = epoch + 1
        for minibatch_index in range(n_train_batches):

            minibatch_avg_cost = train_fn(minibatch_index)
            iter = (epoch - 1) * n_train_batches + minibatch_index

            if (iter + 1) % validation_frequency == 0:

                validation_losses = validate_model()
                this_validation_loss = numpy.mean(validation_losses)
                print(
                    'epoch %i, minibatch %i/%i, validation error %f %%'
                    % (
                        epoch,
                        minibatch_index + 1,
                        n_train_batches,
                        this_validation_loss * 100.
                    )
                )

                # if we got the best validation score until now
                if this_validation_loss < best_validation_loss:

                    #improve patience if loss improvement is good enough
                    if (
                        this_validation_loss < best_validation_loss *
                        improvement_threshold
                    ):
                        patience = max(patience, iter * patience_increase)

                    # save best validation score and iteration number
                    best_validation_loss = this_validation_loss
                    best_iter = iter

                    # test it on the test set
                    test_losses = test_model()
                    test_score = numpy.mean(test_losses)
                    print(('     epoch %i, minibatch %i/%i, test error of '
                           'best model %f %%') %
                          (epoch, minibatch_index + 1, n_train_batches,
                           test_score * 100.))

            if patience <= iter:
                done_looping = True
                break

    end_time = timeit.default_timer()
    print(
        (
            'Optimization complete with best validation score of %f %%, '
            'obtained at iteration %i, '
            'with test performance %f %%'
        ) % (best_validation_loss * 100., best_iter + 1, test_score * 100.)
    )
    print(('The fine tuning code for file ' +
           os.path.split(__file__)[1] +
           ' ran for %.2fm' % ((end_time - start_time)/ 60.)),
          File=sys.stderr)
Пример #3
0
def test_rbm(
    learning_rate=0.1,
    training_epochs=15,
    dataset="mnist.pkl.gz",
    batch_size=20,
    n_chains=20,
    n_samples=10,
    output_folder="rbm_plots",
    n_hidden=500,
):
    """
    Demonstrate how to train and afterwards sample from it using Theano.

    This is demonstrated on MNIST.

    :param learning_rate: learning rate used for training the RBM

    :param training_epochs: number of epochs used for training

    :param dataset: path the the pickled dataset

    :param batch_size: size of a batch used to train the RBM

    :param n_chains: number of parallel Gibbs chains to be used for sampling

    :param n_samples: number of samples to plot for each chain

    """
    datasets = load_data(dataset)

    train_set_x, train_set_y = datasets[0]
    test_set_x, test_set_y = datasets[2]

    # compute number of minibatches for training, validation and testing
    n_train_batches = train_set_x.get_value(borrow=True).shape[0] / batch_size

    # allocate symbolic variables for the data
    index = T.lscalar()  # index to a [mini]batch
    x = T.matrix("x")  # the data is presented as rasterized images

    rng = numpy.random.RandomState(123)
    theano_rng = RandomStreams(rng.randint(2 ** 30))

    # initialize storage for the persistent chain (state = hidden
    # layer of chain)
    persistent_chain = theano.shared(numpy.zeros((batch_size, n_hidden), dtype=theano.config.floatX), borrow=True)

    # construct the RBM class
    rbm = RBM(input=x, n_visible=28 * 28, n_hidden=n_hidden, numpy_rng=rng, theano_rng=theano_rng)

    # get the cost and the gradient corresponding to one step of CD-15
    cost, updates = rbm.get_cost_updates(lr=learning_rate, persistent=persistent_chain, k=15)

    #################################
    #     Training the RBM          #
    #################################
    if not os.path.isdir(output_folder):
        os.makedirs(output_folder)
    os.chdir(output_folder)

    # start-snippet-5
    # it is ok for a theano function to have no output
    # the purpose of train_rbm is solely to update the RBM parameters
    train_rbm = theano.function(
        [index],
        cost,
        updates=updates,
        givens={x: train_set_x[index * batch_size : (index + 1) * batch_size]},
        name="train_rbm",
    )

    plotting_time = 0.0
    start_time = timeit.default_timer()

    # go through training epochs
    for epoch in range(training_epochs):

        # go through the training set
        mean_cost = []
        for batch_index in range(n_train_batches):
            mean_cost += [train_rbm(batch_index)]

        print("Training epoch %d, cost is " % epoch, numpy.mean(mean_cost))

        # Plot filters after each training epoch
        # plotting_start = timeit.default_timer()
        # Construct image from the weight matrix
        # image = Image.fromarray(
        #    tile_raster_images(
        #        X=rbm.W.get_value(borrow=True).T,
        #        img_shape=(28, 28),
        #        tile_shape=(10, 10),
        #        tile_spacing=(1, 1)
        #    )
        # )
        # image.save('filters_at_epoch_%i.png' % epoch)
        # plotting_stop = timeit.default_timer()
        # plotting_time += (plotting_stop - plotting_start)

    end_time = timeit.default_timer()

    pretraining_time = (end_time - start_time) - plotting_time

    print("Training took %f minutes" % (pretraining_time / 60.0))
    # end-snippet-5 start-snippet-6
    #################################
    #     Sampling from the RBM     #
    #################################
    # find out the number of test samples
    number_of_test_samples = test_set_x.get_value(borrow=True).shape[0]

    # pick random test examples, with which to initialize the persistent chain
    test_idx = rng.randint(number_of_test_samples - n_chains)
    persistent_vis_chain = theano.shared(
        numpy.asarray(test_set_x.get_value(borrow=True)[test_idx : test_idx + n_chains], dtype=theano.config.floatX)
    )
    # end-snippet-6 start-snippet-7
    plot_every = 1000
    # define one step of Gibbs sampling (mf = mean-field) define a
    # function that does `plot_every` steps before returning the
    # sample for plotting
    ([presig_hids, hid_mfs, hid_samples, presig_vis, vis_mfs, vis_samples], updates) = theano.scan(
        rbm.gibbs_vhv, outputs_info=[None, None, None, None, None, persistent_vis_chain], n_steps=plot_every
    )

    # add to updates the shared variable that takes care of our persistent
    # chain :.
    updates.update({persistent_vis_chain: vis_samples[-1]})
    # construct the function that implements our persistent chain.
    # we generate the "mean field" activations for plotting and the actual
    # samples for reinitializing the state of our persistent chain
    sample_fn = theano.function([], [vis_mfs[-1], vis_samples[-1]], updates=updates, name="sample_fn")

    # create a space to store the image for plotting ( we need to leave
    # room for the tile_spacing as well)
    # image_data = numpy.zeros(
    #    (29 * n_samples + 1, 29 * n_chains - 1),
    #    dtype='uint8'
    # )
    # for idx in range(n_samples):
    # generate `plot_every` intermediate samples that we discard,
    # because successive samples in the chain are too correlated
    #    vis_mf, vis_sample = sample_fn()
    #    print(' ... plotting sample ', idx)
    #    image_data[29 * idx:29 * idx + 28, :] = tile_raster_images(
    #        X=vis_mf,
    #        img_shape=(28, 28),
    #        tile_shape=(1, n_chains),
    #        tile_spacing=(1, 1)
    #    )

    # construct image
    # image = Image.fromarray(image_data)
    # image.save('samples.png')
    # end-snippet-7
    os.chdir("../")