# HG changeset patch
# User youssouf
# Date 1272574263 14400
# Node ID f2dd752484839b7eb4d3f1121cfd70893a2a2392
# Parent  6478eef4f8aa006032eebaba17eb48629adeddcd
initial commit of mlp with options for detection and 36 classes

diff -r 6478eef4f8aa -r f2dd75248483 baseline/mlp/v_youssouf/mlp_nist.py
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/baseline/mlp/v_youssouf/mlp_nist.py	Thu Apr 29 16:51:03 2010 -0400
@@ -0,0 +1,588 @@
+"""
+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
+import pylearn
+import theano,pylearn.version,ift6266
+from pylearn.io import filetensor as ft
+from ift6266 import datasets
+
+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,learning_rate, detection_mode=0):
+        """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))
+
+        #include the learning rate in the classifer so
+        #we can modify it on the fly when we want
+        lr_value=learning_rate
+        self.lr=theano.shared(value=lr_value)
+        # 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
+        if(detection_mode):
+            self.p_y_given_x= T.nnet.sigmoid(T.dot(self.hidden, self.W2)+self.b2)
+        else:
+            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)
+        self.y_pred_num = T.argmax( self.p_y_given_x[0:9], 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 cross_entropy(self, y):
+        return -T.mean(T.log(self.p_y_given_x)[T.arange(y.shape[0]),y]+T.sum(T.log(1-self.p_y_given_x), axis=1)-T.log(1-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 mlp_full_nist(      verbose = 1,\
+                        adaptive_lr = 0,\
+                        data_set=0,\
+                        learning_rate=0.01,\
+                        L1_reg = 0.00,\
+                        L2_reg = 0.0001,\
+                        nb_max_exemples=1000000,\
+                        batch_size=20,\
+                        nb_hidden = 30,\
+                        nb_targets = 62,
+                        tau=1e6,\
+                        lr_t2_factor=0.5,\
+                        detection_mode = 0,\
+                        reduce_label = 0):
+   
+    
+    configuration = [learning_rate,nb_max_exemples,nb_hidden,adaptive_lr, detection_mode, reduce_label]
+	
+    if(verbose):
+        print(('verbose: %i') % (verbose))
+        print(('adaptive_lr: %i') % (adaptive_lr))
+        print(('data_set: %i') % (data_set))
+        print(('learning_rate: %f') % (learning_rate))
+        print(('L1_reg: %f') % (L1_reg))
+        print(('L2_reg: %f') % (L2_reg))
+        print(('nb_max_exemples: %i') % (nb_max_exemples))
+        print(('batch_size: %i') % (batch_size))
+        print(('nb_hidden: %i') % (nb_hidden))
+        print(('nb_targets: %f') % (nb_targets))
+        print(('tau: %f') % (tau))
+        print(('lr_t2_factor: %f') % (lr_t2_factor))
+        print(('detection_mode: %i') % (detection_mode))
+        print(('reduce_label: %i') % (reduce_label))
+	
+    # define the number of output - reduce_label : merge the lower and upper case. i.e a and A will both have label 10
+    if(reduce_label):
+        nb_targets = 36
+    else:
+        nb_targets = 62	
+    
+    #save initial learning rate if classical adaptive lr is used
+    initial_lr=learning_rate
+    
+    total_validation_error_list = []
+    total_train_error_list = []
+    learning_rate_list=[]
+    best_training_error=float('inf');
+    
+    if data_set==0:
+    	dataset=datasets.nist_all()
+    
+    
+    
+
+    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,\
+                        n_in=32*32,\
+                        n_hidden=nb_hidden,\
+                        n_out=nb_targets,
+                        learning_rate=learning_rate,
+						detection_mode = detection_mode)
+                        
+                        
+   
+
+    # 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
+    if(detection_mode):
+        cost = classifier.cross_entropy(y) \
+         + L1_reg * classifier.L1 \
+         + L2_reg * classifier.L2_sqr 
+    else:
+	    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 - classifier.lr*g_W1 \
+        , classifier.b1: classifier.b1 - classifier.lr*g_b1 \
+        , classifier.W2: classifier.W2 - classifier.lr*g_W2 \
+        , classifier.b2: classifier.b2 - classifier.lr*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 )
+    
+    
+   
+
+   
+   
+    
+   
+   
+   #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 twice in a row(probable overfitting)
+   
+   # This means we no longer stop on slow convergence as low learning rates stopped
+   # too fast. 
+   
+    #approximate number of samples in the training set
+    #this is just to have a validation frequency
+    #roughly proportionnal to the training set
+    n_minibatches        = 650000/batch_size
+    
+    
+    patience              =nb_max_exemples/batch_size #in units of minibatch
+    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_validation_loss = float('inf')
+    best_iter            = 0
+    test_score           = 0.
+    start_time = time.clock()
+    time_n=0 #in unit of exemples
+    minibatch_index=0
+    epoch=0
+    temp=0
+    
+    
+    
+    if verbose == 1:
+        print 'looking at most at %i exemples' %nb_max_exemples
+    while(minibatch_index*batch_size<nb_max_exemples):
+        
+        for x, y in dataset.train(batch_size):
+
+            if reduce_label:
+                y[y > 35] = y[y > 35]-26
+            minibatch_index =  minibatch_index + 1
+            if adaptive_lr==2:
+                    classifier.lr.value = tau*initial_lr/(tau+time_n)
+        
+            
+            #train model
+            cost_ij = train_model(x,y)
+    
+            if (minibatch_index+1) % validation_frequency == 0: 
+                
+                #save the current learning rate
+                learning_rate_list.append(classifier.lr.value)
+                
+                # compute the validation error
+                this_validation_loss = 0.
+                temp=0
+                for xv,yv in dataset.valid(1):
+                    if reduce_label:
+                        yv[yv > 35] = yv[yv > 35]-26
+                    # sum up the errors for each minibatch
+                    axxa=test_model(xv,yv)
+                    this_validation_loss += axxa
+                    temp=temp+1
+                # get the average by dividing with the number of minibatches
+                this_validation_loss /= temp
+                #save the validation loss
+                total_validation_error_list.append(this_validation_loss)
+                if verbose == 1:
+                    print(('epoch %i, minibatch %i, learning rate %f current validation error %f ') % 
+                                (epoch, minibatch_index+1,classifier.lr.value,
+                                this_validation_loss*100.))
+    
+                # if we got the best validation score until now
+                if this_validation_loss < best_validation_loss:
+                    # save best validation score and iteration number
+                    best_validation_loss = this_validation_loss
+                    best_iter = minibatch_index
+                    # reset patience if we are going down again
+                    # so we continue exploring
+                    patience=nb_max_exemples/batch_size
+                    # test it on the test set
+                    test_score = 0.
+                    temp =0
+                    for xt,yt in dataset.test(batch_size):
+                        if reduce_label:
+                            yt[yt > 35] = yt[yt > 35]-26
+                        test_score += test_model(xt,yt)
+                        temp = temp+1
+                    test_score /= temp
+                    if verbose == 1:
+                        print(('epoch %i, minibatch %i, test error of best '
+                            'model %f %%') % 
+                                    (epoch, minibatch_index+1,
+                                    test_score*100.))
+                                    
+                # if the validation error is going up, we are overfitting (or oscillating)
+                # stop converging but run at least to next validation
+                # to check overfitting or ocsillation
+                # the saved weights of the model will be a bit off in that case
+                elif this_validation_loss >= best_validation_loss:
+                    #calculate the test error at this point and exit
+                    # test it on the test set
+                    # however, if adaptive_lr is true, try reducing the lr to
+                    # get us out of an oscilliation
+                    if adaptive_lr==1:
+                        classifier.lr.value=classifier.lr.value*lr_t2_factor
+    
+                    test_score = 0.
+                    #cap the patience so we are allowed one more validation error
+                    #calculation before aborting
+                    patience = minibatch_index+validation_frequency+1
+                    temp=0
+                    for xt,yt in dataset.test(batch_size):
+                        if reduce_label:
+                            yt[yt > 35] = yt[yt > 35]-26
+							
+                        test_score += test_model(xt,yt)
+                        temp=temp+1
+                    test_score /= temp
+                    if verbose == 1:
+                        print ' validation error is going up, possibly stopping soon'
+                        print(('     epoch %i, minibatch %i, test error of best '
+                            'model %f %%') % 
+                                    (epoch, minibatch_index+1,
+                                    test_score*100.))
+                                    
+                    
+    
+    
+            if minibatch_index>patience:
+                print 'we have diverged'
+                break
+    
+    
+            time_n= time_n + batch_size
+        epoch = epoch+1
+    end_time = time.clock()
+    if verbose == 1:
+        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 minibatch_index
+        
+    #save the model and the weights
+    numpy.savez('model.npy', config=configuration, W1=classifier.W1.value,W2=classifier.W2.value, b1=classifier.b1.value,b2=classifier.b2.value)
+    numpy.savez('results.npy',config=configuration,total_train_error_list=total_train_error_list,total_validation_error_list=total_validation_error_list,\
+    learning_rate_list=learning_rate_list)
+    
+    return (best_training_error*100.0,best_validation_loss * 100.,test_score*100.,best_iter*batch_size,(end_time-start_time)/60)
+
+def test_error(model_file):
+    
+    print((' test error on all NIST'))
+    # load the model
+    a=numpy.load(model_file)
+    W1=a['W1']
+    W2=a['W2']
+    b1=a['b1']
+    b2=a['b2']
+    configuration=a['config']
+    #configuration = [learning_rate,nb_max_exemples,nb_hidden,adaptive_lr]
+    learning_rate = configuration[0]
+    nb_max_exemples = configuration[1]
+    nb_hidden = configuration[2]
+    adaptive_lr =  configuration[3]
+	
+    if(len(configuration) == 6):
+        detection_mode = configuration[4]
+        reduce_label = configuration[5]
+    else:
+        detection_mode = 0
+        reduce_label = 0
+
+    # define the batch size
+    batch_size=20
+    #define the nb of target
+    nb_targets = 62
+    
+    # create the mlp
+    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,\
+                        n_in=32*32,\
+                        n_hidden=nb_hidden,\
+                        n_out=nb_targets,
+                        learning_rate=learning_rate,\
+                        detection_mode=detection_mode)
+		
+    		
+    # set the weight into the model
+    classifier.W1.value = W1
+    classifier.b1.value = b1
+    classifier.W2.value = W2
+    classifier.b2.value = b2
+
+						
+    # 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))
+	
+    # test it on the test set
+    
+    # load NIST ALL
+    dataset=datasets.nist_all()
+    test_score = 0.
+    temp =0
+    for xt,yt in dataset.test(batch_size):
+        if reduce_label:
+            yt[yt > 35] = yt[yt > 35]-26
+        test_score += test_model(xt,yt)
+        temp = temp+1
+    test_score /= temp
+
+    print(( ' test error NIST ALL : %f %%') %(test_score*100.0))
+	
+    # load NIST DIGITS
+    dataset=datasets.nist_digits()
+    test_score = 0.
+    temp =0
+    for xt,yt in dataset.test(batch_size):
+        if reduce_label:
+            yt[yt > 35] = yt[yt > 35]-26
+        test_score += test_model(xt,yt)
+        temp = temp+1
+    test_score /= temp
+
+    print(( ' test error NIST digits : %f %%') %(test_score*100.0))
+	
+    # load NIST lower
+    dataset=datasets.nist_lower()
+    test_score = 0.
+    temp =0
+    for xt,yt in dataset.test(batch_size):
+        if reduce_label:
+            yt[yt > 35] = yt[yt > 35]-26
+        test_score += test_model(xt,yt)
+        temp = temp+1
+    test_score /= temp
+
+    print(( ' test error NIST lower : %f %%') %(test_score*100.0))
+	
+    # load NIST upper
+    dataset=datasets.nist_upper()
+    test_score = 0.
+    temp =0
+    for xt,yt in dataset.test(batch_size):
+        if reduce_label:
+            yt[yt > 35] = yt[yt > 35]-26
+        test_score += test_model(xt,yt)
+        temp = temp+1
+    test_score /= temp
+
+    print(( ' test error NIST upper : %f %%') %(test_score*100.0))
+                                    
+
+if __name__ == '__main__':
+    '''
+	mlp_full_nist(      verbose = 1,\
+                        adaptive_lr = 1,\
+                        data_set=0,\
+                        learning_rate=0.5,\
+                        L1_reg = 0.00,\
+                        L2_reg = 0.0001,\
+                        nb_max_exemples=10000000,\
+                        batch_size=20,\
+                        nb_hidden = 500,\
+                        nb_targets = 62,
+			tau=100000,\
+			lr_t2_factor=0.5)
+    '''
+	
+    test_error('model.npy.npz')
+
+def jobman_mlp_full_nist(state,channel):
+    (train_error,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,\
+										adaptive_lr=state.adaptive_lr,\
+										tau=state.tau,\
+										verbose = state.verbose,\
+										lr_t2_factor=state.lr_t2_factor,\
+										detection_mode = state.detection_mode,\
+                                        reduce_label = state.reduce_label)
+    state.train_error=train_error
+    state.validation_error=validation_error
+    state.test_error=test_error
+    state.nb_exemples=nb_exemples
+    state.time=time
+    pylearn.version.record_versions(state,[theano,ift6266,pylearn])
+    return channel.COMPLETE
+                                                                
+