예제 #1
0
파일: mlp.py 프로젝트: asterix135/CKME136
def mlp(learning_rate=0.01, L1_reg=0.00, L2_reg=0.0001, n_epochs=1000,
        batch_size=20, n_hidden=500):
    """
    Stochastic gradient descent optimization for a multilayer perceptron

    :type learning_rate: float
    :param learning_rate: learning rate used (factor for stochatic gradient)

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

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

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

    :param batch_size: size of bach processed
    :param n_hidden: number of hidden outputs
    """
    datasets = load_data(5000)

    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')  # labels as 1D vector of [int] labels

    rng = np.random.RandomState(13032016)

    # construct the MLP class
    classifier = MLP(
        rng=rng,
        input=x,
        n_in=prep.SIZE[0] * prep.SIZE[1] * 3,
        n_hidden=n_hidden,
        n_out=3
    )

    # start-snippet-4
    # the cost we minimize during training is the negative log liklihood of
    # the model plus the regularization terms (L1 and L2)
    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 (stored 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

    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
    # at 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
    patience_increase = 2  # wait this long when a new best is found
    improvement_threshold = 0.995  # relative improvement considered significant
    validation_frequency = min(n_train_batches, patience // 2)
                                # go thorugh this many minibatches before
                                # checking the network on the validation set;
                                # in this case we check every epoch
    best_validation_loss = np.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 += 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:
                # computer zero-one loss on validation set
                validation_losses = [validate_model(i) for i
                                     in range(n_valid_batches)]
                this_validation_loss = np.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 get 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 on the test set
                    test_losses = [test_model(i) for i
                                   in range(n_test_batches)]
                    test_score = np.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.))

                    # save the best model
                    with open('best_mlp_model.pkl', 'wb') as f:
                        pickle.dump(classifier, f)

            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)
예제 #2
0
def evaluate_lenet5(learning_rate=0.1, n_epochs=200,
                    nkerns=[20, 50], batch_size=500):
    """

    :type learning_rate: float
    :param learning_rate: factor for stochastic gradient

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

    :type nkerns: list of ints
    :param nkerns:  number of kernels on each layer

    :param batch_size: size of batch
    """
    rng = np.random.RandomState(20160313)

    datasets = load_data(3500)

    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]
    n_valid_batches = valid_set_x.get_value(borrow=True).shape[0]
    n_test_batches = test_set_x.get_value(borrow=True).shape[0]
    n_train_batches //= batch_size
    n_valid_batches //= batch_size
    n_test_batches //= batch_size

    # allocate symbolic variables for the data
    index = T.lscalar()  # index to a [mini]batch

    # start-snippet-1
    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

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

    # Reshape matrix of rasterized images of shape (batch_size, image w*h)
    # to a 4D tensor, compatible with our LeNetConvPoolLayer
    layer0_input = x.reshape((batch_size, 1, prep.SIZE[0], prep.SIZE[1]))

    # Construct the first convolutional pooling layer:
    # filtering reduces the image size to (size-5+1 , size-5+1) = (24, 24)
    # maxpooling reduces this by factor of 1/2
    # 4D output tensor is thus of shape (batch_size, nkerns[0], 12, 12)
    layer0 = LeNetConvPoolLayer(
        rng,
        input=layer0_input,
        image_shape=(batch_size, 1, prep.SIZE[0], prep.SIZE[1]),
        filter_shape=(nkerns[0], 1, 5, 5),
        poolsize=(2, 2)
    )
    new_size = ((prep.SIZE[0] - 5 + 1) // 2, (prep.SIZE[1] - 5 + 1 // 2))

    # Construct the second convolutional pooling layer
    # filtering reduces the image size as previous
    layer1 = LeNetConvPoolLayer(
        rng,
        input=layer0.output,
        image_shape=(batch_size, nkerns[0], new_size[0], new_size[1]),
        filter_shape=(nkerns[1], nkerns[0], 5, 5),
        poolsize=(2, 2)
    )
    new_size = ((new_size[0] -5 + 1) // 2, (new_size[1] -5 + 1 // 2))

    # the HiddenLayer being fully-connected, it operates on 2D matrices of
    # shape (batch_size, num_pixels) (i.e matrix of rasterized images).
    # This will generate a matrix of shape (batch_size, nkerns[1] * new_size),
    # or (500, 50 * new_size[0] * new_size[1]) = (500, 800)
    # with the default values.
    layer2_input = layer1.output.flatten(2)

    # construct a fully-connected sigmoidal layer
    layer2 = HiddenLayer(
        rng,
        input=layer2_input,
        n_in=nkerns[1] * new_size[0] * new_size[1],
        n_out=500,
        activation=T.tanh
    )


    # classify the values of the fully-connected sigmoidal layer
    layer3 = LogisticRegression(input=layer2.output, n_in=500, n_out=3)

    # the cost we minimize during training is the NLL of the model
    cost = layer3.negative_log_likelihood(y)

    # create a function to compute the mistakes that are made by the model
    test_model = theano.function(
        [index],
        layer3.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(
        [index],
        layer3.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]
        }
    )

    # create a list of all model parameters to be fit by gradient descent
    params = layer3.params + layer2.params + layer1.params + layer0.params

    # create a list of gradients for all model parameters
    grads = T.grad(cost, params)

    # train_model is a function that updates the model parameters by
    # SGD Since this model has many parameters, it would be tedious to
    # manually create an update rule for each model parameter. We thus
    # create the updates list by automatically looping over all
    # (params[i], grads[i]) pairs.
    updates = [
        (param_i, param_i - learning_rate * grad_i)
        for param_i, grad_i in zip(params, grads)
    ]

    train_model = theano.function(
        [index],
        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-1

    ###############
    # 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 = np.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):

            iter = (epoch - 1) * n_train_batches + minibatch_index

            if iter % 100 == 0:
                print('training @ iter = ', iter)
            cost_ij = train_model(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 = np.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(i)
                        for i in range(n_test_batches)
                    ]
                    test_score = np.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.))

                    # save the best model
                    # with open('best_mlp_model.pkl', 'wb') as f:
                    #     pickle.dump(classifier, f)

            if patience <= iter:
                done_looping = True
                break

    end_time = timeit.default_timer()
    print('Optimization complete.')
    print('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)