add data change for kaggle

Author: chaosconst

code/convolutional_mlp_kaggle.py

@@ -0,0 +1,321 @@
+"""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
+
+"""
+import cPickle
+import gzip
+import os
+import sys
+import time
+
+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, load_data
+from mlp import HiddenLayer
+
+
+class LeNetConvPoolLayer(object):
+    """Pool Layer of a convolutional network """
+
+    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]
+
+
+def evaluate_lenet5(learning_rate=0.1, n_epochs=200,
+                    dataset='../data/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]
+    predict_set_x, predict_set_y = datasets[3]
+
+    # 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_predict_batches = predict_set_x.get_value(borrow=True).shape[0]
+    
+    n_train_batches /= batch_size
+    n_valid_batches /= batch_size
+    n_test_batches /= batch_size
+    n_predict_batches /= 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
+
+    ######################
+    # 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
+    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 (nkerns[0],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 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=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]})
+
+    predict_model = theano.function([index], layer3.predict(),
+             givens={
+                x: predict_set_x[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 = []
+    for param_i, grad_i in zip(params, grads):
+        updates.append((param_i, param_i - learning_rate * grad_i))
+
+    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]})
+
+    ###############
+    # 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_params = None
+    best_validation_loss = numpy.inf
+    best_iter = 0
+    test_score = 0.
+    start_time = time.clock()
+
+    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)
+                    
+                    predict_res_array = [predict_model(i) for i in xrange(n_predict_batches)]
+                    print predict_res_array;
+                    f = open("predict_res","w+");
+                    for y_pred_item_array in predict_res_array:
+                      for y_pred_item in y_pred_item_array:
+                        f.write(str(y_pred_item)+'\n');
+                    f.close();
+
+ 
+                    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 = time.clock()
+    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.))
+
+if __name__ == '__main__':
+    evaluate_lenet5()
+
+
+def experiment(state, channel):
+    evaluate_lenet5(state.learning_rate, dataset=state.dataset)