LeNet.py

import dill
import os
import sys
import timeit
import numpy
import theano
import theano.tensor as T
from theano.tensor.signal import downsample
from theano.tensor.nnet import conv
from logistic_sgd import LogisticRegression
from mlp import HiddenLayer
from Tools import load_data, accuracy, saveimage


"""This tutorial introduces the LeNet5 neural network architecture
using Theano.  LeNet5 is a convolutional neural network, good for
classifying images. This tutorial shows how to build the architecture,
and comes with all the hyper-parameters you need to reproduce the
paper's MNIST results.


This implementation simplifies the model in the following ways:

 - LeNetConvPool doesn't implement location-specific gain and bias parameters
 - LeNetConvPool doesn't implement pooling by average, it implements pooling
   by max.
 - Digit classification is implemented with a logistic regression rather than
   an RBF network
 - LeNet5 was not fully-connected convolutions at second layer

References:
 - Y. LeCun, L. Bottou, Y. Bengio and P. Haffner:
   Gradient-Based Learning Applied to Document
   Recognition, Proceedings of the IEEE, 86(11):2278-2324, November 1998.
   http://yann.lecun.com/exdb/publis/pdf/lecun-98.pdf

"""


class LeNetConvPoolLayer(object):
    """
    Pool Layer of a convolutional network .In fact,this contain two layer which are
    convolution layer and sub-sampling layer
    """

    def __init__(self, rng, input, filter_shape, image_shape, poolsize=(2, 2)):
        """
        Allocate a LeNetConvPoolLayer with shared variable internal parameters.

        :type rng: numpy.random.RandomState
        :param rng: a random number generator used to initialize weights

        :type input: theano.tensor.dtensor4
        :param input: symbolic image tensor, of shape image_shape

        :type filter_shape: tuple or list of length 4
        :param filter_shape: (number of filters, num input feature maps,
                              filter height, filter width)

        :type image_shape: tuple or list of length 4
        :param image_shape: (batch size, num input feature maps,
                             image height, image width)

        :type poolsize: tuple or list of length 2
        :param poolsize: the downsampling (pooling) factor (#rows, #cols)
        """

        assert image_shape[1] == filter_shape[1]
        self.input = input

        # there are "num input feature maps * filter height * filter width"
        # inputs to each hidden unit
        fan_in = numpy.prod(filter_shape[1:])
        # each unit in the lower layer receives a gradient from:
        # "num output feature maps * filter height * filter width" /
        #   pooling size
        fan_out = (filter_shape[0] * numpy.prod(filter_shape[2:]) /
                   numpy.prod(poolsize))
        # initialize weights with random weights
        W_bound = numpy.sqrt(6. / (fan_in + fan_out))
        self.W = theano.shared(
            numpy.asarray(
                rng.uniform(low=-W_bound, high=W_bound, size=filter_shape),
                dtype=theano.config.floatX
            ),
            borrow=True
        )

        # the bias is a 1D tensor -- one bias per output feature map
        b_values = numpy.zeros((filter_shape[0],), dtype=theano.config.floatX)
        self.b = theano.shared(value=b_values, borrow=True)

        # convolve input feature maps with filters
        conv_out = conv.conv2d(
            input=input,
            filters=self.W,
            filter_shape=filter_shape,
            image_shape=image_shape
        )

        # downsample each feature map individually, using maxpooling
        pooled_out = downsample.max_pool_2d(
            input=conv_out,
            ds=poolsize,
            ignore_border=True
        )

        # add the bias term. Since the bias is a vector (1D array), we first
        # reshape it to a tensor of shape (1, n_filters, 1, 1). Each bias will
        # thus be broadcasted across mini-batches and feature map
        # width & height
        self.output = T.tanh(pooled_out + self.b.dimshuffle('x', 0, 'x', 'x'))

        # store parameters of this layer
        self.params = [self.W, self.b]

        # keep track of model input
        self.input = input


class LeNet(object):
    """Convolutional Neural Networks (LeNet) Class

    This LeNet is a special kind of multi-layer neural
    networks that has two LeNetConvPoolLayer(the details you can find in
    LeNetConvPoolLayer class of this file.the first convolution layer has 20 kerns,
    the second has 50 kerns. ),one hidden layer and one LogisticRegression layer.
    In order to show more image,it can be simplified "LeNet5=input+LeNetConvPoolLayer_1+
    LeNetConvPoolLayer_2+HiddenLayer+LogisticRegression+output"
    """

    def __init__(self, rng, input, nkerns, batch_size, n_hidden, n_out):
        """Initialize the parameters for the LeNet
        :type rng: numpy.random.RandomState
        :param rng: a random number generator used to initialize weights

        :type input: theano.tensor.TensorType
        :param input: symbolic variable that describes the input of the
        architecture (one minibatch)

        :type nkerns: list of ints
        :param nkerns: number of kernels on each convolution layer,eg:nkerns[0]
        is on behalf of the number of kernels in the first convolution layer.

        :type batch_size: int
        :param batch_size: size of every bath

        :type n_hidden: int
        :param n_hidden: number of hidden units

        :type n_out: int
        :param n_out: number of output units, the dimension of the space in
        which the labels lie

        """

        layer0_input = input

        # 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)
        self.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)
        self.layer1 = LeNetConvPoolLayer(
            rng,
            input=self.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.
        self.layer2_input = self.layer1.output.flatten(2)

        # construct a fully-connected sigmoidal layer
        self.layer2 = HiddenLayer(
            rng,
            input=self.layer2_input,
            n_in=nkerns[1] * 4 * 4,
            n_out=n_hidden,
            activation=T.tanh
        )

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

        # negative log likelihood of the MLP is given by the negative
        # log likelihood of the output of the model, computed in the
        # logistic regression layer
        self.negative_log_likelihood = (
            self.layer3.negative_log_likelihood
        )

        # same holds for the function computing the number of errors
        self.errors = self.layer3.errors

        # the parameters of the model are the parameters of the all layer it is
        # made out of
        self.params = self.layer3.params + self.layer2.params + \
            self.layer1.params + self.layer0.params
        # end-snippet-3
        # keep track of model input
        self.input = input


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

    :type batch_size: int
    :param batch_size: size of every bath
    """

    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.
    input = x.reshape((batch_size, 1, 28, 28))

    # construct the LeNet class
    classifier = LeNet(
        rng=rng,
        input=input,
        nkerns=nkerns,
        batch_size=batch_size,
        n_hidden=500,
        n_out=10
    )

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

    # create a function to compute the mistakes that are made by the model
    test_model = theano.function(
        [index],
        classifier.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],
        classifier.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 gradients for all model parameters
    grads = T.grad(cost, classifier.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(classifier.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 += 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.))

                    # save the best model
                    with open('best_model-2.pkl', 'w') as f:
                        dill.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 >> sys.stderr, ('The code for file ' +
                          os.path.split(__file__)[1] +
                          ' ran for %.2fm' % ((end_time - start_time) / 60.))
def predict():
    """
    An example of how to load a trained model and use it
    to predict labels.
    """

    # load the saved model
    classifier = dill.load(open('best_model-2.pkl'))
    # compile a predictor function
    predict_model = theano.function(
        inputs=[classifier.input],
        outputs=classifier.layer3.y_pred)

    # We can test it on some examples from test test
    dataset = 'mnist.pkl.gz'
    datasets = load_data(dataset)
    test_set_x, test_set_y = datasets[2]

    test_set_x = test_set_x.get_value()
    test_set_x=test_set_x[0:500, ]

    test_set_y = test_set_y.eval()
    test_set_y = test_set_y[0:500]
    predicted_values = predict_model(test_set_x)

    accuracy(test_set_y, predicted_values)
    saveimage(test_set_x, test_set_y, predicted_values)
if __name__ == '__main__':
    # evaluate_lenet5()
    predict()
def experiment(state, channel):
    evaluate_lenet5(state.learning_rate, dataset=state.dataset)