--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/code_tutoriel/convolutional_mlp.py	Fri Feb 26 13:55:27 2010 -0500
@@ -0,0 +1,292 @@
+"""
+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 numpy, time, cPickle, gzip
+
+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
+  
+        # initialize weights to temporary values until we know the shape of the output feature
+        # maps
+        W_values = numpy.zeros(filter_shape, dtype=theano.config.floatX)
+        self.W = theano.shared(value = W_values)
+
+        # 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)
+
+        # convolve input feature maps with filters
+        conv_out = conv.conv2d(input = input, filters = self.W, 
+                filter_shape=filter_shape, image_shape=image_shape)
+
+        # 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)
+        # replace weight values with random weights
+        W_bound = numpy.sqrt(6./(fan_in + fan_out))
+        self.W.value = numpy.asarray( 
+                rng.uniform(low=-W_bound, high=W_bound, size=filter_shape),
+                dtype = theano.config.floatX)
+  
+        # downsample each feature map individually, using maxpooling
+        pooled_out = downsample.max_pool2D( 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='mnist.pkl.gz', nkerns=[20,50]):
+    """ 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]
+
+
+    batch_size = 500    # size of the minibatch
+
+    # compute number of minibatches for training, validation and testing
+    n_train_batches = train_set_x.value.shape[0] / batch_size
+    n_valid_batches = valid_set_x.value.shape[0] / batch_size
+    n_test_batches  = test_set_x.value.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
+
+    ######################
+    # 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]})
+
+    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
+    # 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[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 = float('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 * 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 = False
+            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, test_score*100.))
+    print('The code ran for %f minutes' % ((end_time-start_time)/60.))
+
+if __name__ == '__main__':
+    evaluate_lenet5()
+
+def experiment(state, channel):
+    evaluate_lenet5(state.learning_rate, dataset=state.dataset)