コード例 #1
0
ファイル: convnet3d.py プロジェクト: rodion-zheludkov/kaggle
    def __init__(self, x, y, batch_size, videos, kernels, pools, n_input, n_output, hidden_input, params=None):
        learning_rate = 0.1
        rng = numpy.random.RandomState(1234)

        print '... building the model'
        sys.stdout.flush()

        if not params:
            # Construct the first convolutional pooling layer:
            # filtering reduces the image size to (28-5+1,28-5+1)=(24,24)
            # maxpooling reduces this further to (24/2,24/2) = (12,12)
            # 4D output tensor is thus of shape (batch_size,nkerns[0],12,12)
            layer0 = ConvLayer(x, n_input[0], n_output[0], kernels[0], videos[0], pools[0],
                               batch_size, 'L0', rng)

            layer1 = ConvLayer(layer0.output, n_input[1], n_output[1], kernels[1], videos[1], pools[1],
                               batch_size, 'L1', rng)

            layer2_input = layer1.output.flatten(2)

            # construct a fully-connected sigmoidal layer
            layer2 = HiddenLayer(rng, input=layer2_input, n_in=hidden_input,
                                 n_out=batch_size, activation=T.tanh)

            # classify the values of the fully-connected sigmoidal layer
            layer3 = LogisticRegression(input=layer2.output, n_in=batch_size, n_out=2)
        else:

            layer0 = ConvLayer(x, n_input[0], n_output[0], kernels[0], videos[0], pools[0],
                               batch_size, 'L0', rng, True, params[6], params[7])

            layer1 = ConvLayer(layer0.output, n_input[1], n_output[1], kernels[1], videos[1], pools[1],
                               batch_size, 'L1', rng, True, params[4], params[5])

            layer2_input = layer1.output.flatten(2)

            # construct a fully-connected sigmoidal layer
            layer2 = HiddenLayer(rng, input=layer2_input, n_in=hidden_input,
                                 n_out=batch_size, activation=T.tanh, W=params[2], b=params[3])

            # classify the values of the fully-connected sigmoidal layer
            layer3 = LogisticRegression(input=layer2.output, n_in=batch_size, n_out=2, W=params[0], b=params[1])

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

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

        # create a list of gradients for all model parameters
        grads = T.grad(cost, self.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 = []
        for param_i, grad_i in zip(self.params, grads):
            updates.append((param_i, param_i - learning_rate * grad_i))

        self.train_model = theano.function([x, y], cost, updates=updates)
        self.validate_model = theano.function(inputs=[x, y], outputs=layer3.errors(y))
        self.predict = theano.function(inputs=[x], outputs=layer3.y_pred)

        print '... building done'
        sys.stdout.flush()
コード例 #2
0
def evaluate_lenet5(learning_rate=0.1, n_epochs=200,
                    dataset='mnist.pkl.gz',
                    nkerns=[20, 50], batch_size=500):
    """ Demonstrates lenet on MNIST dataset

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

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

    :type dataset: string
    :param dataset: path to the dataset used for training /testing (MNIST here)

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

    rng = numpy.random.RandomState(23455)

    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]
    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, 28 * 28)
    # to a 4D tensor, compatible with our LeNetConvPoolLayer
    # (28, 28) is the size of MNIST images.
    layer0_input = x.reshape((batch_size, 1, 28, 28))

    # Construct the first convolutional pooling layer:
    # filtering reduces the image size to (28-5+1 , 28-5+1) = (24, 24)
    # maxpooling reduces this further to (24/2, 24/2) = (12, 12)
    # 4D output tensor is thus of shape (batch_size, nkerns[0], 12, 12)
    layer0 = LeNetConvPoolLayer(
        rng,
        input=layer0_input,
        image_shape=(batch_size, 1, 28, 28),
        filter_shape=(nkerns[0], 1, 5, 5),
        poolsize=(2, 2)
    )

    # Construct the second convolutional pooling layer
    # filtering reduces the image size to (12-5+1, 12-5+1) = (8, 8)
    # maxpooling reduces this further to (8/2, 8/2) = (4, 4)
    # 4D output tensor is thus of shape (batch_size, nkerns[1], 4, 4)
    layer1 = LeNetConvPoolLayer(
        rng,
        input=layer0.output,
        image_shape=(batch_size, nkerns[0], 12, 12),
        filter_shape=(nkerns[1], nkerns[0], 5, 5),
        poolsize=(2, 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] * 4 * 4),
    # or (500, 50 * 4 * 4) = (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] * 4 * 4,
        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=10)

    # 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 = 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 xrange(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 xrange(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)

                    # 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 xrange(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.')
    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 >> sys.stderr, ('The code for file ' +
                          os.path.split(__file__)[1] +
                          ' ran for %.2fm' % ((end_time - start_time) / 60.))
コード例 #3
0
ファイル: lenet5.py プロジェクト: hohogpb/pypgd
def train_lenet5(train_set_x, train_set_y, params, batch_size, 
                 learning_rate=0.01, nkerns=[20,50], test=False):
    """ 
    Trains LeNet-5 on MNIST dataset, and returns trained parameters on
    completion.

    :type train_set: list of floats
    :param train_set: training samples (x- and y-values) for training

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

    :type params: list of tuples of floats
    :param params: list of tuple of parameters from Supervisor. Takes the form
                   [(W_layer0, b_layer0), (W_layer1, b_layer1), 
                    (W_layer2, b_layer2), (W_layer3, b_layer3)]

    :type batch_size: int
    :param batch_size: size of training batch

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

    Output: tuple of LeNet-5 parameters by layer, in this format:

    ( (W_layer0, b_layer0), ..., (W_layer3, b_layer3) )
    
    """

    rng = numpy.random.RandomState(23455)

    # compute number of minibatches for training, validation and testing
    n_train_batches = 100
    #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
    y     = T.ivector('y') # the labels are presented as 1D vector of 
                           # [int] labels
    ishape = (28,28)       # this is the size of MNIST images

    print '    ... building the model'

    # Reshape matrix of rasterized images of shape (batch_size,28*28)
    # to a 4D tensor, compatible with our LeNetConvPoolLayer
    layer0_input = x.reshape((batch_size,1,28,28))

    # Construct the first convolutional pooling layer:
    # filtering reduces the image size to (28-5+1,28-5+1)=(24,24)
    # maxpooling reduces this further to (24/2,24/2) = (12,12)
    # 4D output tensor is thus of shape (batch_size,nkerns[0],12,12)
    layer0 = LeNetConvPoolLayer(rng, input=layer0_input,
                                image_shape=(batch_size,1,28,28), 
                                W_values = params[0][0],
                                b_values = params[0][1],
                                filter_shape=(nkerns[0],1,5,5), poolsize=(2,2))

    # Construct the second convolutional pooling layer
    # filtering reduces the image size to (12-5+1,12-5+1)=(8,8)
    # maxpooling reduces this further to (8/2,8/2) = (4,4)
    # 4D output tensor is thus of shape (nkerns[0],nkerns[1],4,4)
    layer1 = LeNetConvPoolLayer(rng, input=layer0.output,
                                image_shape=(batch_size,nkerns[0],12,12), 
                                W_values = params[1][0],
                                b_values = params[1][1],
                                filter_shape=(nkerns[1],nkerns[0],5,5), 
                                poolsize=(2,2))

    # the TanhLayer 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 (20,32*4*4) = (20,512)
    layer2_input = layer1.output.flatten(2)

    # construct a fully-connected sigmoidal layer
    layer2 = HiddenLayer(rng, input=layer2_input, n_in=nkerns[1]*4*4, 
                         n_out=120, activation = T.tanh,
                         W_values = params[2][0],
                         b_values = params[2][1])

    # classify the values of the fully-connected sigmoidal layer
    layer3 = LogisticRegression(input=layer2.output, n_in=120, n_out=10,
                                W_values=params[3][0],
                                b_values=params[3][1])

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

    # 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
    # dictionary by automatically looping over all (params[i],grads[i])  pairs.
    updates = {}
    for param_i, grad_i in zip(params, grads):
        updates[param_i] = param_i - learning_rate * grad_i
    
    train_model = theano.function([index], cost, updates=updates,
                                  givens = {x: train_set_x, y: train_set_y},
                                  mode='FAST_RUN')

    print "    training lenet-5..."

    start_time = time.clock()
    epoch = 0 
    done_looping = False

    while (epoch < batch_size/100) and (not done_looping):
        epoch = epoch + 1
        for minibatch_index in xrange(n_train_batches):
            iter = epoch * n_train_batches + minibatch_index
            cost_ij = train_model(minibatch_index)

    end_time = time.clock()
    print "    worker training complete."
    print "    %i samples analyzed in %.2fm" % (batch_size, 
                                                (end_time-start_time)/60.)

    return ((layer0.params[0].get_value(), layer0.params[1].get_value()),
            (layer1.params[0].get_value(), layer1.params[1].get_value()),
            (layer2.params[0].get_value(), layer2.params[1].get_value()),
            (layer3.params[0].get_value(), layer3.params[1].get_value()))