--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/deep/stacked_dae/v_youssouf/stacked_dae.py	Sun Apr 25 12:31:22 2010 -0400
@@ -0,0 +1,353 @@
+#!/usr/bin/python
+# coding: utf-8
+
+import numpy 
+import theano
+import time
+import theano.tensor as T
+from theano.tensor.shared_randomstreams import RandomStreams
+import copy
+
+from utils import update_locals
+
+# taken from LeDeepNet/daa.py
+# has a special case when taking log(0) (defined =0)
+# modified to not take the mean anymore
+from theano.tensor.xlogx import xlogx, xlogy0
+# it's target*log(output)
+def binary_cross_entropy(target, output, sum_axis=1):
+    XE = xlogy0(target, output) + xlogy0((1 - target), (1 - output))
+    return -T.sum(XE, axis=sum_axis)
+
+class LogisticRegression(object):
+    def __init__(self, input, n_in, n_out, detection_mode):
+        # initialize with 0 the weights W as a matrix of shape (n_in, n_out) 
+        self.W = theano.shared( value=numpy.zeros((n_in,n_out),
+                                            dtype = theano.config.floatX) )
+        # initialize the baises b as a vector of n_out 0s
+        self.b = theano.shared( value=numpy.zeros((n_out,), 
+                                            dtype = theano.config.floatX) )
+        # compute vector of class-membership probabilities in symbolic form
+        if detection_mode:
+            self.p_y_given_x = T.nnet.sigmoid(T.dot(input, self.W)+self.b)
+        else:
+            self.p_y_given_x = T.nnet.softmax(T.dot(input, self.W)+self.b)
+        
+        # compute prediction as class whose probability is maximal in 
+        # symbolic form
+        self.y_pred=T.argmax(self.p_y_given_x, axis=1)
+
+        # list of parameters for this layer
+        self.params = [self.W, self.b]
+        
+
+    def negative_log_likelihood(self, y):
+       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):
+        # 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()
+
+
+class SigmoidalLayer(object):
+    def __init__(self, rng, input, n_in, n_out):
+        self.input = input
+
+        W_values = numpy.asarray( rng.uniform( \
+              low = -numpy.sqrt(6./(n_in+n_out)), \
+              high = numpy.sqrt(6./(n_in+n_out)), \
+              size = (n_in, n_out)), dtype = theano.config.floatX)
+        self.W = theano.shared(value = W_values)
+
+        b_values = numpy.zeros((n_out,), dtype= theano.config.floatX)
+        self.b = theano.shared(value= b_values)
+
+        self.output = T.nnet.sigmoid(T.dot(input, self.W) + self.b)
+        self.params = [self.W, self.b]
+
+
+
+class dA(object):
+  def __init__(self, n_visible= 784, n_hidden= 500, corruption_level = 0.1,\
+               input = None, shared_W = None, shared_b = None):
+    self.n_visible = n_visible
+    self.n_hidden  = n_hidden
+    
+    # create a Theano random generator that gives symbolic random values
+    theano_rng = RandomStreams()
+    
+    if shared_W != None and shared_b != None : 
+        self.W = shared_W
+        self.b = shared_b
+    else:
+        # initial values for weights and biases
+        # note : W' was written as `W_prime` and b' as `b_prime`
+
+        # W is initialized with `initial_W` which is uniformely sampled
+        # from -6./sqrt(n_visible+n_hidden) and 6./sqrt(n_hidden+n_visible)
+        # the output of uniform if converted using asarray to dtype 
+        # theano.config.floatX so that the code is runable on GPU
+        initial_W = numpy.asarray( numpy.random.uniform( \
+              low = -numpy.sqrt(6./(n_hidden+n_visible)), \
+              high = numpy.sqrt(6./(n_hidden+n_visible)), \
+              size = (n_visible, n_hidden)), dtype = theano.config.floatX)
+        initial_b       = numpy.zeros(n_hidden, dtype = theano.config.floatX)
+    
+    
+        # theano shared variables for weights and biases
+        self.W       = theano.shared(value = initial_W,       name = "W")
+        self.b       = theano.shared(value = initial_b,       name = "b")
+    
+ 
+    initial_b_prime= numpy.zeros(n_visible)
+    # tied weights, therefore W_prime is W transpose
+    self.W_prime = self.W.T 
+    self.b_prime = theano.shared(value = initial_b_prime, name = "b'")
+
+    # if no input is given, generate a variable representing the input
+    if input == None : 
+        # we use a matrix because we expect a minibatch of several examples,
+        # each example being a row
+        self.x = T.matrix(name = 'input') 
+    else:
+        self.x = input
+    # Equation (1)
+    # keep 90% of the inputs the same and zero-out randomly selected subset of 10% of the inputs
+    # note : first argument of theano.rng.binomial is the shape(size) of 
+    #        random numbers that it should produce
+    #        second argument is the number of trials 
+    #        third argument is the probability of success of any trial
+    #
+    #        this will produce an array of 0s and 1s where 1 has a 
+    #        probability of 1 - ``corruption_level`` and 0 with
+    #        ``corruption_level``
+    self.tilde_x  = theano_rng.binomial( self.x.shape,  1,  1 - corruption_level, dtype=theano.config.floatX) * self.x
+    # Equation (2)
+    # note  : y is stored as an attribute of the class so that it can be 
+    #         used later when stacking dAs. 
+    self.y   = T.nnet.sigmoid(T.dot(self.tilde_x, self.W      ) + self.b)
+    # Equation (3)
+    #self.z   = T.nnet.sigmoid(T.dot(self.y, self.W_prime) + self.b_prime)
+    # Equation (4)
+    # note : we sum over the size of a datapoint; if we are using minibatches,
+    #        L will  be a vector, with one entry per example in minibatch
+    #self.L = - T.sum( self.x*T.log(self.z) + (1-self.x)*T.log(1-self.z), axis=1 ) 
+    #self.L = binary_cross_entropy(target=self.x, output=self.z, sum_axis=1)
+
+    # bypassing z to avoid running to log(0)
+    z_a = T.dot(self.y, self.W_prime) + self.b_prime
+    log_sigmoid = T.log(1.) - T.log(1.+T.exp(-z_a))
+    # log(1-sigmoid(z_a))
+    log_1_sigmoid = -z_a - T.log(1.+T.exp(-z_a))
+    self.L = -T.sum( self.x * (log_sigmoid) \
+                    + (1.0-self.x) * (log_1_sigmoid), axis=1 )
+
+    # I added this epsilon to avoid getting log(0) and 1/0 in grad
+    # This means conceptually that there'd be no probability of 0, but that
+    # doesn't seem to me as important (maybe I'm wrong?).
+    #eps = 0.00000001
+    #eps_1 = 1-eps
+    #self.L = - T.sum( self.x * T.log(eps + eps_1*self.z) \
+    #                + (1-self.x)*T.log(eps + eps_1*(1-self.z)), axis=1 )
+    # note : L is now a vector, where each element is the cross-entropy cost 
+    #        of the reconstruction of the corresponding example of the 
+    #        minibatch. We need to compute the average of all these to get 
+    #        the cost of the minibatch
+    self.cost = T.mean(self.L)
+
+    self.params = [ self.W, self.b, self.b_prime ]
+
+
+class SdA(object):
+    def __init__(self, batch_size, n_ins, 
+                 hidden_layers_sizes, n_outs, 
+                 corruption_levels, rng, pretrain_lr, finetune_lr, detection_mode):
+        # Just to make sure those are not modified somewhere else afterwards
+        hidden_layers_sizes = copy.deepcopy(hidden_layers_sizes)
+        corruption_levels = copy.deepcopy(corruption_levels)
+
+        update_locals(self, locals())      
+ 
+        self.layers             = []
+        self.pretrain_functions = []
+        self.params             = []
+        # MODIF: added this so we also get the b_primes
+        # (not used for finetuning... still using ".params")
+        self.all_params         = []
+        self.n_layers           = len(hidden_layers_sizes)
+        self.logistic_params    = []
+
+        print "Creating SdA with params:"
+        print "batch_size", batch_size
+        print "hidden_layers_sizes", hidden_layers_sizes
+        print "corruption_levels", corruption_levels
+        print "n_ins", n_ins
+        print "n_outs", n_outs
+        print "pretrain_lr", pretrain_lr
+        print "finetune_lr", finetune_lr
+        print "detection_mode", detection_mode
+        print "----"
+
+        if len(hidden_layers_sizes) < 1 :
+            raiseException (' You must have at least one hidden layer ')
+
+
+        # allocate symbolic variables for the data
+        #index   = T.lscalar()    # index to a [mini]batch 
+        self.x  = T.matrix('x')  # the data is presented as rasterized images
+        self.y  = T.ivector('y') # the labels are presented as 1D vector of 
+                                 # [int] labels
+        self.finetune_lr = T.fscalar('finetune_lr') #To get a dynamic finetune learning rate
+
+        for i in xrange( self.n_layers ):
+            # construct the sigmoidal layer
+
+            # the size of the input is either the number of hidden units of 
+            # the layer below or the input size if we are on the first layer
+            if i == 0 :
+                input_size = n_ins
+            else:
+                input_size = hidden_layers_sizes[i-1]
+
+            # the input to this layer is either the activation of the hidden
+            # layer below or the input of the SdA if you are on the first
+            # layer
+            if i == 0 : 
+                layer_input = self.x
+            else:
+                layer_input = self.layers[-1].output
+
+            layer = SigmoidalLayer(rng, layer_input, input_size, 
+                                   hidden_layers_sizes[i] )
+            # add the layer to the 
+            self.layers += [layer]
+            self.params += layer.params
+        
+            # Construct a denoising autoencoder that shared weights with this
+            # layer
+            dA_layer = dA(input_size, hidden_layers_sizes[i], \
+                          corruption_level = corruption_levels[0],\
+                          input = layer_input, \
+                          shared_W = layer.W, shared_b = layer.b)
+
+            self.all_params += dA_layer.params
+        
+            # Construct a function that trains this dA
+            # compute gradients of layer parameters
+            gparams = T.grad(dA_layer.cost, dA_layer.params)
+            # compute the list of updates
+            updates = {}
+            for param, gparam in zip(dA_layer.params, gparams):
+                updates[param] = param - gparam * pretrain_lr
+            
+            # create a function that trains the dA
+            update_fn = theano.function([self.x], dA_layer.cost, \
+                  updates = updates)#,
+            #     givens = { 
+            #         self.x : ensemble})
+            # collect this function into a list
+            #update_fn = theano.function([index], dA_layer.cost, \
+            #      updates = updates,
+            #      givens = { 
+            #         self.x : train_set_x[index*batch_size:(index+1)*batch_size] / self.shared_divider})
+            # collect this function into a list
+            self.pretrain_functions += [update_fn]
+
+        
+        # We now need to add a logistic layer on top of the SDA
+        self.logLayer = LogisticRegression(\
+                         input = self.layers[-1].output,\
+                         n_in = hidden_layers_sizes[-1], n_out = n_outs, detection_mode = detection_mode)
+
+        self.params += self.logLayer.params
+        self.all_params += self.logLayer.params
+        # construct a function that implements one step of finetunining
+
+        
+        if detection_mode:
+            # compute the cost, defined as the negative log likelihood 
+            cost = self.logLayer.cross_entropy(self.y)
+            # compute the gradients with respect to the logistic regression parameters
+            gparams = T.grad(cost, self.logLayer.params)
+            # compute list of updates
+            updates = {}
+            for param,gparam in zip(self.logLayer.params, gparams):
+                updates[param] = param - gparam*finetune_lr
+         
+        else:
+            # compute the cost, defined as the negative log likelihood 
+            cost = self.logLayer.negative_log_likelihood(self.y)
+            # compute the gradients with respect to the model parameters
+            gparams = T.grad(cost, self.params)
+            # compute list of updates
+            updates = {}
+            for param,gparam in zip(self.params, gparams):
+                updates[param] = param - gparam*self.finetune_lr
+            
+        self.finetune = theano.function([self.x,self.y,self.finetune_lr], cost, 
+                updates = updates)#,
+                
+        # symbolic variable that points to the number of errors made on the
+        # minibatch given by self.x and self.y
+
+        self.errors = self.logLayer.errors(self.y)
+        
+        
+        #STRUCTURE FOR THE FINETUNING OF THE LOGISTIC REGRESSION ON THE TOP WITH
+        #ALL HIDDEN LAYERS AS INPUT
+        '''
+        
+        all_h=[]
+        for i in xrange(self.n_layers):
+            all_h.append(self.layers[i].output)
+        self.all_hidden=T.concatenate(all_h,axis=1)
+
+
+        self.logLayer2 = LogisticRegression(\
+                         input = self.all_hidden,\
+                         n_in = sum(hidden_layers_sizes), n_out = n_outs)
+                         #n_in=hidden_layers_sizes[0],n_out=n_outs)
+
+        #self.logistic_params+= self.logLayer2.params
+        # construct a function that implements one step of finetunining
+        
+        self.logistic_params+=self.logLayer2.params
+        # compute the cost, defined as the negative log likelihood
+        if DETECTION_MODE:
+            cost2 = self.logLayer2.cross_entropy(self.y)
+        else:
+			cost2 = self.logLayer2.negative_log_likelihood(self.y)
+        # compute the gradients with respect to the model parameters
+        gparams2 = T.grad(cost2, self.logistic_params)
+
+        # compute list of updates
+        updates2 = {}
+        for param,gparam in zip(self.logistic_params, gparams2):
+            updates2[param] = param - gparam*finetune_lr
+   
+        self.finetune2 = theano.function([self.x,self.y], cost2, 
+                updates = updates2)
+
+        # symbolic variable that points to the number of errors made on the
+        # minibatch given by self.x and self.y
+
+        self.errors2 = self.logLayer2.errors(self.y)
+        '''
+
+if __name__ == '__main__':
+    import sys
+    args = sys.argv[1:]
+