--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/baseline/deep_mlp/deepmlp.py	Fri Feb 26 14:28:48 2010 -0500
@@ -0,0 +1,310 @@
+#
+
+import numpy, cPickle, gzip
+
+
+import theano
+import theano.tensor as T
+
+import time 
+
+import theano.tensor.nnet
+
+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: List representing the number of units for each 
+		hidden layer
+		
+		#:param n_layer: Number of hidden layers
+
+        :param n_out: number of output units, the dimension of the space in 
+        which the labels lie
+
+        """
+
+        # initialize the parameters theta = (W,b) ; Here W and b are lists 
+        # where W[i] and b[i] represent the parameters and the bias vector
+        # of the i-th layer.
+        n_layer=len(n_hidden)
+        W_values=[]
+        b_values=[]
+        self.W=[]
+        self.b=[]
+		
+	# We first initialize the matrix W[0] and b[0] that represent the parameters
+	# from the input to the first hidden layer
+        W_values.append(numpy.asarray( numpy.random.uniform( \
+		      low = -numpy.sqrt(6./(n_in+n_hidden[0])), \
+			  high = numpy.sqrt(6./(n_in+n_hidden[0])), \
+			  size = (n_in, n_hidden[0])), dtype = theano.config.floatX))
+        self.W.append(theano.shared( value = W_values[0] ))
+        self.b.append(theano.shared( value = numpy.zeros((n_hidden[0],), 
+                                                dtype= theano.config.floatX)))
+												
+        # We initialize the parameters between all consecutive hidden layers
+        for i in range(1,n_layer):
+        # Each `W[i]` is initialized with `W_values[i]` which is uniformely sampled
+        # from -6./sqrt(n_hidden[i]+n_hidden[i+1]) and 6./sqrt(n_hidden[i]+n_hidden[i+1])
+        # the output of uniform if converted using asarray to dtype 
+        # theano.config.floatX so that the code is runable on GPU
+            W_values.append(numpy.asarray( numpy.random.uniform( \
+		          low = -numpy.sqrt(6./(n_hidden[i-1]+n_hidden[i])), \
+			      high = numpy.sqrt(6./(n_hidden[i-1]+n_hidden[i])), \
+			      size = (n_hidden[i-1], n_hidden[i])), dtype = theano.config.floatX))
+            self.W.append(theano.shared( value = W_values[i] ))
+            self.b.append(theano.shared( value = numpy.zeros((n_hidden[i],), 
+                                                dtype= theano.config.floatX)))
+
+        # We initialize the matrix W[n_layer] and b[n_layer] that represent 
+        # the parameters from the last hidden layer to the output layer using the
+        # same uniform sampling.
+        W_values.append(numpy.asarray( numpy.random.uniform( 
+              low = -numpy.sqrt(6./(n_hidden[n_layer-1]+n_out)), \
+              high= numpy.sqrt(6./(n_hidden[n_layer-1]+n_out)),\
+              size= (n_hidden[n_layer-1], n_out)), dtype = theano.config.floatX))
+        self.W.append(theano.shared( value = W_values[n_layer]))
+        self.b.append(theano.shared( value = numpy.zeros((n_out,), 
+                                                dtype= theano.config.floatX)))
+
+        # List of the symbolic expressions computing the values each hidden layer
+        self.hidden = []
+
+	# Symbolic expression of the first hidden layer
+        self.hidden.append(T.tanh(T.dot(input, self.W[0])+ self.b[0]))
+        for i in range(1,n_layer):
+	# Symbolic expression of the i-th hidden layer
+            self.hidden.append(T.tanh(T.dot(self.hidden[i-1], self.W[i])+ self.b[i]))
+
+        # symbolic expression computing the values of the top layer 
+        self.p_y_given_x= T.nnet.softmax(T.dot(self.hidden[n_layer-1], self.W[n_layer])+self.b[n_layer])
+
+        # 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.W[0]).sum()
+        self.L2_sqr=abs(self.W[0]).sum()
+        for i in range(1,n_layer+1):
+            self.L1 += abs(self.W[i]).sum()
+        # square of L2 norm ; one regularization option is to enforce 
+        # square of L2 norm to be small
+        for i in range(n_layer+1):
+            self.L2_sqr += abs(self.W[i]**2).sum()
+
+    def negative_log_likelihood(self, y):
+        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 sgd_optimization_mnist( learning_rate=0.01, L1_reg = 0.00, \
+                            L2_reg = 0.0001, n_iter=100,n_hidden=[200,100,90,80,70]):
+    """
+    Demonstrate stochastic gradient descent optimization for a multilayer 
+    perceptron
+
+    This is demonstrated on MNIST.
+
+    :param learning_rate: learning rate used (factor for the stochastic 
+    gradient
+
+    :param L1_reg: L1-norm's weight when added to the cost (see 
+    regularization)
+
+    :param L2_reg: L2-norm's weight when added to the cost (see 
+    regularization)
+ 
+    :param n_iter: maximal number of iterations ot run the optimizer 
+
+   """
+
+    # Load the dataset 
+    f = gzip.open('mnist.pkl.gz','rb')
+    train_set, valid_set, test_set = cPickle.load(f)
+    f.close()
+
+    # make minibatches of size 20 
+    batch_size = 20    # sized of the minibatch
+
+    # Dealing with the training set
+    # get the list of training images (x) and their labels (y)
+    (train_set_x, train_set_y) = train_set
+	
+    # initialize the list of training minibatches with empty list
+    train_batches = []
+    for i in xrange(0, len(train_set_x), batch_size):
+        # add to the list of minibatches the minibatch starting at 
+        # position i, ending at position i+batch_size
+        # a minibatch is a pair ; the first element of the pair is a list 
+        # of datapoints, the second element is the list of corresponding 
+        # labels
+        train_batches = train_batches + \
+               [(train_set_x[i:i+batch_size], train_set_y[i:i+batch_size])]
+
+    # Dealing with the validation set
+    (valid_set_x, valid_set_y) = valid_set
+    # initialize the list of validation minibatches 
+    valid_batches = []
+    for i in xrange(0, len(valid_set_x), batch_size):
+        valid_batches = valid_batches + \
+               [(valid_set_x[i:i+batch_size], valid_set_y[i:i+batch_size])]
+
+    # Dealing with the testing set
+    (test_set_x, test_set_y) = test_set
+    # initialize the list of testing minibatches 
+    test_batches = []
+    for i in xrange(0, len(test_set_x), batch_size):
+        test_batches = test_batches + \
+              [(test_set_x[i:i+batch_size], test_set_y[i:i+batch_size])]
+
+
+    ishape     = (28,28) # this is the size of MNIST 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,28*28)),\
+                      n_in=28*28, n_hidden=n_hidden, n_out=10)
+    
+    # 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))
+    g_W=[]
+    g_b=[]
+    # compute the gradient of cost with respect to theta = (W1, b1, W2, b2) 
+    for i in range(len(n_hidden)+1):
+        g_W.append(T.grad(cost, classifier.W[i]))
+        g_b.append(T.grad(cost, classifier.b[i]))
+	
+	
+    # specify how to update the parameters of the model as a dictionary
+    updates={}
+    for i in range(len(n_hidden)+1):
+        updates[classifier.W[i]]= classifier.W[i] - learning_rate*g_W[i]
+        updates[classifier.b[i]]= classifier.b[i] - learning_rate*g_b[i]
+    # 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) 
+ 
+    # 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  = n_minibatches  # 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()
+    # have a maximum of `n_iter` iterations through the entire dataset
+    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 ]
+        cost_ij = train_model(x,y)
+
+        if (iter+1) % validation_frequency == 0: 
+            # compute zero-one loss on validation set 
+            this_validation_loss = 0.
+            for x,y in valid_batches:
+                # sum up the errors for each minibatch
+                this_validation_loss += test_model(x,y)
+            # get the average by dividing with the number of minibatches
+            this_validation_loss /= len(valid_batches)
+
+            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)
+
+                # 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:
+                    test_score += test_model(x,y)
+                test_score /= len(test_batches)
+                print(('     epoch %i, minibatch %i/%i, test error of best '
+                      'model %f %%') % 
+                             (epoch, minibatch_index+1, n_minibatches,
+                              test_score*100.))
+
+        if patience <= iter :
+            break
+
+    end_time = time.clock()
+    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.))		
+    #test on NIST (you need pylearn and access to NIST to do that)
+if __name__ == '__main__':
+    sgd_optimization_mnist()
+							   

--- a/scripts/deepmlp.py	Fri Feb 26 14:24:11 2010 -0500
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,310 +0,0 @@
-#
-
-import numpy, cPickle, gzip
-
-
-import theano
-import theano.tensor as T
-
-import time 
-
-import theano.tensor.nnet
-
-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: List representing the number of units for each 
-		hidden layer
-		
-		#:param n_layer: Number of hidden layers
-
-        :param n_out: number of output units, the dimension of the space in 
-        which the labels lie
-
-        """
-
-        # initialize the parameters theta = (W,b) ; Here W and b are lists 
-        # where W[i] and b[i] represent the parameters and the bias vector
-        # of the i-th layer.
-        n_layer=len(n_hidden)
-        W_values=[]
-        b_values=[]
-        self.W=[]
-        self.b=[]
-		
-	# We first initialize the matrix W[0] and b[0] that represent the parameters
-	# from the input to the first hidden layer
-        W_values.append(numpy.asarray( numpy.random.uniform( \
-		      low = -numpy.sqrt(6./(n_in+n_hidden[0])), \
-			  high = numpy.sqrt(6./(n_in+n_hidden[0])), \
-			  size = (n_in, n_hidden[0])), dtype = theano.config.floatX))
-        self.W.append(theano.shared( value = W_values[0] ))
-        self.b.append(theano.shared( value = numpy.zeros((n_hidden[0],), 
-                                                dtype= theano.config.floatX)))
-												
-        # We initialize the parameters between all consecutive hidden layers
-        for i in range(1,n_layer):
-        # Each `W[i]` is initialized with `W_values[i]` which is uniformely sampled
-        # from -6./sqrt(n_hidden[i]+n_hidden[i+1]) and 6./sqrt(n_hidden[i]+n_hidden[i+1])
-        # the output of uniform if converted using asarray to dtype 
-        # theano.config.floatX so that the code is runable on GPU
-            W_values.append(numpy.asarray( numpy.random.uniform( \
-		          low = -numpy.sqrt(6./(n_hidden[i-1]+n_hidden[i])), \
-			      high = numpy.sqrt(6./(n_hidden[i-1]+n_hidden[i])), \
-			      size = (n_hidden[i-1], n_hidden[i])), dtype = theano.config.floatX))
-            self.W.append(theano.shared( value = W_values[i] ))
-            self.b.append(theano.shared( value = numpy.zeros((n_hidden[i],), 
-                                                dtype= theano.config.floatX)))
-
-        # We initialize the matrix W[n_layer] and b[n_layer] that represent 
-        # the parameters from the last hidden layer to the output layer using the
-        # same uniform sampling.
-        W_values.append(numpy.asarray( numpy.random.uniform( 
-              low = -numpy.sqrt(6./(n_hidden[n_layer-1]+n_out)), \
-              high= numpy.sqrt(6./(n_hidden[n_layer-1]+n_out)),\
-              size= (n_hidden[n_layer-1], n_out)), dtype = theano.config.floatX))
-        self.W.append(theano.shared( value = W_values[n_layer]))
-        self.b.append(theano.shared( value = numpy.zeros((n_out,), 
-                                                dtype= theano.config.floatX)))
-
-        # List of the symbolic expressions computing the values each hidden layer
-        self.hidden = []
-
-	# Symbolic expression of the first hidden layer
-        self.hidden.append(T.tanh(T.dot(input, self.W[0])+ self.b[0]))
-        for i in range(1,n_layer):
-	# Symbolic expression of the i-th hidden layer
-            self.hidden.append(T.tanh(T.dot(self.hidden[i-1], self.W[i])+ self.b[i]))
-
-        # symbolic expression computing the values of the top layer 
-        self.p_y_given_x= T.nnet.softmax(T.dot(self.hidden[n_layer-1], self.W[n_layer])+self.b[n_layer])
-
-        # 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.W[0]).sum()
-        self.L2_sqr=abs(self.W[0]).sum()
-        for i in range(1,n_layer+1):
-            self.L1 += abs(self.W[i]).sum()
-        # square of L2 norm ; one regularization option is to enforce 
-        # square of L2 norm to be small
-        for i in range(n_layer+1):
-            self.L2_sqr += abs(self.W[i]**2).sum()
-
-    def negative_log_likelihood(self, y):
-        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 sgd_optimization_mnist( learning_rate=0.01, L1_reg = 0.00, \
-                            L2_reg = 0.0001, n_iter=100,n_hidden=[200,100,90,80,70]):
-    """
-    Demonstrate stochastic gradient descent optimization for a multilayer 
-    perceptron
-
-    This is demonstrated on MNIST.
-
-    :param learning_rate: learning rate used (factor for the stochastic 
-    gradient
-
-    :param L1_reg: L1-norm's weight when added to the cost (see 
-    regularization)
-
-    :param L2_reg: L2-norm's weight when added to the cost (see 
-    regularization)
- 
-    :param n_iter: maximal number of iterations ot run the optimizer 
-
-   """
-
-    # Load the dataset 
-    f = gzip.open('mnist.pkl.gz','rb')
-    train_set, valid_set, test_set = cPickle.load(f)
-    f.close()
-
-    # make minibatches of size 20 
-    batch_size = 20    # sized of the minibatch
-
-    # Dealing with the training set
-    # get the list of training images (x) and their labels (y)
-    (train_set_x, train_set_y) = train_set
-	
-    # initialize the list of training minibatches with empty list
-    train_batches = []
-    for i in xrange(0, len(train_set_x), batch_size):
-        # add to the list of minibatches the minibatch starting at 
-        # position i, ending at position i+batch_size
-        # a minibatch is a pair ; the first element of the pair is a list 
-        # of datapoints, the second element is the list of corresponding 
-        # labels
-        train_batches = train_batches + \
-               [(train_set_x[i:i+batch_size], train_set_y[i:i+batch_size])]
-
-    # Dealing with the validation set
-    (valid_set_x, valid_set_y) = valid_set
-    # initialize the list of validation minibatches 
-    valid_batches = []
-    for i in xrange(0, len(valid_set_x), batch_size):
-        valid_batches = valid_batches + \
-               [(valid_set_x[i:i+batch_size], valid_set_y[i:i+batch_size])]
-
-    # Dealing with the testing set
-    (test_set_x, test_set_y) = test_set
-    # initialize the list of testing minibatches 
-    test_batches = []
-    for i in xrange(0, len(test_set_x), batch_size):
-        test_batches = test_batches + \
-              [(test_set_x[i:i+batch_size], test_set_y[i:i+batch_size])]
-
-
-    ishape     = (28,28) # this is the size of MNIST 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,28*28)),\
-                      n_in=28*28, n_hidden=n_hidden, n_out=10)
-    
-    # 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))
-    g_W=[]
-    g_b=[]
-    # compute the gradient of cost with respect to theta = (W1, b1, W2, b2) 
-    for i in range(len(n_hidden)+1):
-        g_W.append(T.grad(cost, classifier.W[i]))
-        g_b.append(T.grad(cost, classifier.b[i]))
-	
-	
-    # specify how to update the parameters of the model as a dictionary
-    updates={}
-    for i in range(len(n_hidden)+1):
-        updates[classifier.W[i]]= classifier.W[i] - learning_rate*g_W[i]
-        updates[classifier.b[i]]= classifier.b[i] - learning_rate*g_b[i]
-    # 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) 
- 
-    # 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  = n_minibatches  # 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()
-    # have a maximum of `n_iter` iterations through the entire dataset
-    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 ]
-        cost_ij = train_model(x,y)
-
-        if (iter+1) % validation_frequency == 0: 
-            # compute zero-one loss on validation set 
-            this_validation_loss = 0.
-            for x,y in valid_batches:
-                # sum up the errors for each minibatch
-                this_validation_loss += test_model(x,y)
-            # get the average by dividing with the number of minibatches
-            this_validation_loss /= len(valid_batches)
-
-            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)
-
-                # 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:
-                    test_score += test_model(x,y)
-                test_score /= len(test_batches)
-                print(('     epoch %i, minibatch %i/%i, test error of best '
-                      'model %f %%') % 
-                             (epoch, minibatch_index+1, n_minibatches,
-                              test_score*100.))
-
-        if patience <= iter :
-            break
-
-    end_time = time.clock()
-    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.))		
-    #test on NIST (you need pylearn and access to NIST to do that)
-if __name__ == '__main__':
-    sgd_optimization_mnist()
-