# HG changeset patch
# User XavierMuller
# Date 1266358355 18000
# Node ID 93b4b84d86cf14917e85d6595507b72aa7082d29
# Parent  9c45e0071b5288b8e41a87340f5920567d21463c
 added simple mlp file

diff -r 9c45e0071b52 -r 93b4b84d86cf baseline_algorithms/mlp/mlp_nist.py
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/baseline_algorithms/mlp/mlp_nist.py	Tue Feb 16 17:12:35 2010 -0500
@@ -0,0 +1,411 @@
+"""
+This tutorial introduces the multilayer perceptron using Theano.  
+
+ A multilayer perceptron is a logistic regressor where
+instead of feeding the input to the logistic regression you insert a
+intermidiate layer, called the hidden layer, that has a nonlinear 
+activation function (usually tanh or sigmoid) . One can use many such 
+hidden layers making the architecture deep. The tutorial will also tackle 
+the problem of MNIST digit classification.
+
+.. math::
+
+    f(x) = G( b^{(2)} + W^{(2)}( s( b^{(1)} + W^{(1)} x))),
+
+References:
+
+    - textbooks: "Pattern Recognition and Machine Learning" - 
+                 Christopher M. Bishop, section 5
+
+TODO: recommended preprocessing, lr ranges, regularization ranges (explain 
+      to do lr first, then add regularization)
+
+"""
+__docformat__ = 'restructedtext en'
+
+import pdb
+import numpy
+import pylab
+import theano
+import theano.tensor as T
+import time 
+import theano.tensor.nnet
+from pylearn.io import filetensor as ft
+
+data_path = '/data/lisa/data/nist/by_class/'
+
+class MLP(object):
+    """Multi-Layer Perceptron Class
+
+    A multilayer perceptron is a feedforward artificial neural network model 
+    that has one layer or more of hidden units and nonlinear activations. 
+    Intermidiate layers usually have as activation function thanh or the 
+    sigmoid function  while the top layer is a softamx layer. 
+    """
+
+
+
+    def __init__(self, input, n_in, n_hidden, n_out):
+        """Initialize the parameters for the multilayer perceptron
+
+        :param input: symbolic variable that describes the input of the 
+        architecture (one minibatch)
+
+        :param n_in: number of input units, the dimension of the space in 
+        which the datapoints lie
+
+        :param n_hidden: number of hidden units 
+
+        :param n_out: number of output units, the dimension of the space in 
+        which the labels lie
+
+        """
+
+        # initialize the parameters theta = (W1,b1,W2,b2) ; note that this 
+        # example contains only one hidden layer, but one can have as many 
+        # layers as he/she wishes, making the network deeper. The only 
+        # problem making the network deep this way is during learning, 
+        # backpropagation being unable to move the network from the starting
+        # point towards; this is where pre-training helps, giving a good 
+        # starting point for backpropagation, but more about this in the 
+        # other tutorials
+        
+        # `W1` is initialized with `W1_values` which is uniformely sampled
+        # from -6./sqrt(n_in+n_hidden) and 6./sqrt(n_in+n_hidden)
+        # the output of uniform if converted using asarray to dtype 
+        # theano.config.floatX so that the code is runable on GPU
+        W1_values = numpy.asarray( numpy.random.uniform( \
+              low = -numpy.sqrt(6./(n_in+n_hidden)), \
+              high = numpy.sqrt(6./(n_in+n_hidden)), \
+              size = (n_in, n_hidden)), dtype = theano.config.floatX)
+        # `W2` is initialized with `W2_values` which is uniformely sampled 
+        # from -6./sqrt(n_hidden+n_out) and 6./sqrt(n_hidden+n_out)
+        # the output of uniform if converted using asarray to dtype 
+        # theano.config.floatX so that the code is runable on GPU
+        W2_values = numpy.asarray( numpy.random.uniform( 
+              low = -numpy.sqrt(6./(n_hidden+n_out)), \
+              high= numpy.sqrt(6./(n_hidden+n_out)),\
+              size= (n_hidden, n_out)), dtype = theano.config.floatX)
+
+        self.W1 = theano.shared( value = W1_values )
+        self.b1 = theano.shared( value = numpy.zeros((n_hidden,), 
+                                                dtype= theano.config.floatX))
+        self.W2 = theano.shared( value = W2_values )
+        self.b2 = theano.shared( value = numpy.zeros((n_out,), 
+                                                dtype= theano.config.floatX))
+
+        # symbolic expression computing the values of the hidden layer
+        self.hidden = T.tanh(T.dot(input, self.W1)+ self.b1)
+
+        # symbolic expression computing the values of the top layer 
+        self.p_y_given_x= T.nnet.softmax(T.dot(self.hidden, self.W2)+self.b2)
+
+        # compute prediction as class whose probability is maximal in 
+        # symbolic form
+        self.y_pred = T.argmax( self.p_y_given_x, axis =1)
+        
+        # L1 norm ; one regularization option is to enforce L1 norm to 
+        # be small 
+        self.L1     = abs(self.W1).sum() + abs(self.W2).sum()
+
+        # square of L2 norm ; one regularization option is to enforce 
+        # square of L2 norm to be small
+        self.L2_sqr = (self.W1**2).sum() + (self.W2**2).sum()
+
+
+
+    def negative_log_likelihood(self, y):
+        """Return the mean of the negative log-likelihood of the prediction
+        of this model under a given target distribution.
+
+        .. math::
+
+            \frac{1}{|\mathcal{D}|}\mathcal{L} (\theta=\{W,b\}, \mathcal{D}) = 
+            \frac{1}{|\mathcal{D}|}\sum_{i=0}^{|\mathcal{D}|} \log(P(Y=y^{(i)}|x^{(i)}, W,b)) \\
+                \ell (\theta=\{W,b\}, \mathcal{D}) 
+
+
+        :param y: corresponds to a vector that gives for each example the
+        :correct label
+        """
+        return -T.mean(T.log(self.p_y_given_x)[T.arange(y.shape[0]),y])
+
+
+
+
+    def errors(self, y):
+        """Return a float representing the number of errors in the minibatch 
+        over the total number of examples of the minibatch 
+        """
+
+        # check if y has same dimension of y_pred 
+        if y.ndim != self.y_pred.ndim:
+            raise TypeError('y should have the same shape as self.y_pred', 
+                ('y', target.type, 'y_pred', self.y_pred.type))
+        # check if y is of the correct datatype        
+        if y.dtype.startswith('int'):
+            # the T.neq operator returns a vector of 0s and 1s, where 1
+            # represents a mistake in prediction
+            return T.mean(T.neq(self.y_pred, y))
+        else:
+            raise NotImplementedError()
+
+#def jobman_mlp(state,channel):
+#    (validation_error,test_error,nb_exemples,time)=mlp_full_nist(state.learning_rate,\
+ #                                                                state.n_iter,\
+ #                                                                state.batch_size,\
+ #                                                                state.nb_hidden_units)
+ #   state.validation_error = validation_error
+ #   state.test_error = test_error
+ #   state.nb_exemples = nb_exemples
+  #  state.time=time
+   # return channel.COMPLETE
+
+
+                                                                 
+
+def mlp_full_nist(      verbose = False,\
+                        train_data = 'all/all_train_data.ft',\
+                        train_labels = 'all/all_train_labels.ft',\
+                        test_data = 'all/all_test_data.ft',\
+                        test_labels = 'all/all_test_labels.ft',\
+                        learning_rate=0.01,\
+                        L1_reg = 0.00,\
+                        L2_reg = 0.0001,\
+                        nb_max_exemples=1000000,\
+                        batch_size=20,\
+                        nb_hidden = 500,\
+                        nb_targets = 62):
+   
+    
+   
+    f = open(data_path+train_data)
+    g= open(data_path+train_labels)
+    h = open(data_path+test_data)
+    i= open(data_path+test_labels)
+    
+    raw_train_data = ft.read(f)
+    raw_train_labels = ft.read(g)
+    raw_test_data = ft.read(h)
+    raw_test_labels = ft.read(i)
+    
+    f.close()
+    g.close()
+    i.close()
+    h.close()
+    #create a validation set the same size as the test size
+    #use the end of the training array for this purpose
+    #discard the last remaining so we get a %batch_size number
+    test_size=len(raw_test_labels)
+    test_size = int(test_size/batch_size)
+    test_size*=batch_size
+    train_size = len(raw_train_data)
+    train_size = int(train_size/batch_size)
+    train_size*=batch_size
+    validation_size =test_size 
+    offset = train_size-test_size
+    if verbose == True:
+        print 'train size = %d' %train_size
+        print 'test size = %d' %test_size
+        print 'valid size = %d' %validation_size
+        print 'offset = %d' %offset
+    
+    
+    train_set = (raw_train_data,raw_train_labels)
+    train_batches = []
+    for i in xrange(0, train_size-test_size, batch_size):
+        train_batches = train_batches + \
+            [(raw_train_data[i:i+batch_size], raw_train_labels[i:i+batch_size])]
+            
+    test_batches = []
+    for i in xrange(0, test_size, batch_size):
+        test_batches = test_batches + \
+            [(raw_test_data[i:i+batch_size], raw_test_labels[i:i+batch_size])]
+    
+    validation_batches = []
+    for i in xrange(0, test_size, batch_size):
+        validation_batches = validation_batches + \
+            [(raw_train_data[offset+i:offset+i+batch_size], raw_train_labels[offset+i:offset+i+batch_size])]
+
+
+    ishape     = (32,32) # this is the size of NIST images
+
+    # allocate symbolic variables for the data
+    x = T.fmatrix()  # the data is presented as rasterized images
+    y = T.lvector()  # the labels are presented as 1D vector of 
+                          # [long int] labels
+
+    # construct the logistic regression class
+    classifier = MLP( input=x.reshape((batch_size,32*32)),\
+                        n_in=32*32,\
+                        n_hidden=nb_hidden,\
+                        n_out=nb_targets)
+
+    # the cost we minimize during training is the negative log likelihood of 
+    # the model plus the regularization terms (L1 and L2); cost is expressed
+    # here symbolically
+    cost = classifier.negative_log_likelihood(y) \
+         + L1_reg * classifier.L1 \
+         + L2_reg * classifier.L2_sqr 
+
+    # compiling a theano function that computes the mistakes that are made by 
+    # the model on a minibatch
+    test_model = theano.function([x,y], classifier.errors(y))
+
+    # compute the gradient of cost with respect to theta = (W1, b1, W2, b2) 
+    g_W1 = T.grad(cost, classifier.W1)
+    g_b1 = T.grad(cost, classifier.b1)
+    g_W2 = T.grad(cost, classifier.W2)
+    g_b2 = T.grad(cost, classifier.b2)
+
+    # specify how to update the parameters of the model as a dictionary
+    updates = \
+        { classifier.W1: classifier.W1 - learning_rate*g_W1 \
+        , classifier.b1: classifier.b1 - learning_rate*g_b1 \
+        , classifier.W2: classifier.W2 - learning_rate*g_W2 \
+        , classifier.b2: classifier.b2 - learning_rate*g_b2 }
+
+    # compiling a theano function `train_model` that returns the cost, but in 
+    # the same time updates the parameter of the model based on the rules 
+    # defined in `updates`
+    train_model = theano.function([x, y], cost, updates = updates )
+    n_minibatches        = len(train_batches)
+
+   
+   
+   
+   #conditions for stopping the adaptation:
+   #1) we have reached  nb_max_exemples (this is rounded up to be a multiple of the train size)
+   #2) validation error is going up (probable overfitting)
+   
+   # This means we no longer stop on slow convergence as low learning rates stopped
+   # too fast. 
+    patience              =nb_max_exemples/batch_size
+    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 = n_minibatches/4
+   
+     
+
+   
+    best_params          = None
+    best_validation_loss = float('inf')
+    best_iter            = 0
+    test_score           = 0.
+    start_time = time.clock()
+    n_iter = nb_max_exemples/batch_size  # nb of max times we are allowed to run through all exemples
+    n_iter = n_iter/n_minibatches + 1
+    n_iter=max(1,n_iter) # run at least once on short debug call
+    # have a maximum of `n_iter` iterations through the entire dataset
+   
+    if verbose == True:
+        print 'looping at most %d times through the data set' %n_iter
+    for iter in xrange(n_iter* n_minibatches):
+
+        # get epoch and minibatch index
+        epoch           = iter / n_minibatches
+        minibatch_index =  iter % n_minibatches
+
+        # get the minibatches corresponding to `iter` modulo
+        # `len(train_batches)`
+        x,y = train_batches[ minibatch_index ]
+        # convert to float
+        x_float = x/255.0
+        cost_ij = train_model(x_float,y)
+
+        if (iter+1) % validation_frequency == 0: 
+            # compute zero-one loss on validation set 
+           
+            this_validation_loss = 0.
+            for x,y in validation_batches:
+                # sum up the errors for each minibatch
+                x_float = x/255.0
+                this_validation_loss += test_model(x_float,y)
+            # get the average by dividing with the number of minibatches
+            this_validation_loss /= len(validation_batches)
+            if verbose == True:
+                print('epoch %i, minibatch %i/%i, validation error %f %%' % \
+                    (epoch, minibatch_index+1, n_minibatches, \
+                        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)
+                elif verbose == True:
+                    print 'slow convergence stop'
+
+                # save best validation score and iteration number
+                best_validation_loss = this_validation_loss
+                best_iter = iter
+
+                # test it on the test set
+                test_score = 0.
+                for x,y in test_batches:
+                    x_float=x/255.0
+                    test_score += test_model(x_float,y)
+                test_score /= len(test_batches)
+                if verbose == True:
+                    print(('     epoch %i, minibatch %i/%i, test error of best '
+                        'model %f %%') % 
+                                (epoch, minibatch_index+1, n_minibatches,
+                                test_score*100.))
+                                
+            #if the validation error is going up, we are overfitting
+            #stop converging
+            elif this_validation_loss > best_validation_loss:
+                #calculate the test error at this point and exit
+                # test it on the test set
+                if verbose==True:
+                    print ' We are diverging'
+                best_iter = iter
+                test_score = 0.
+                for x,y in test_batches:
+                    x_float=x/255.0
+                    test_score += test_model(x_float,y)
+                test_score /= len(test_batches)
+                if verbose == True:
+                    print ' validation error is going up, stopping now'
+                    print(('     epoch %i, minibatch %i/%i, test error of best '
+                        'model %f %%') % 
+                                (epoch, minibatch_index+1, n_minibatches,
+                                test_score*100.))
+                                
+                break
+
+
+            
+            if patience <= iter :
+               break 
+        
+
+    end_time = time.clock()
+    if verbose == True:
+        print(('Optimization complete. 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.))
+    print iter
+    return (best_validation_loss * 100.,test_score*100.,best_iter*batch_size,(end_time-start_time)/60)
+
+
+if __name__ == '__main__':
+    mlp_full_mnist()
+
+def jobman_mlp_full_nist(state,channel):
+    (validation_error,test_error,nb_exemples,time)=mlp_full_nist(learning_rate=state.learning_rate,\
+                                                                nb_max_exemples=state.nb_max_exemples,\
+                                                                nb_hidden=state.nb_hidden)
+    state.validation_error=validation_error
+    state.test_error=test_error
+    state.nb_exemples=nb_exemples
+    state.time=time
+    return channel.COMPLETE
+                                                                
+                                                                
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