comparison Code/genre_classification/classification/convolutional_mlp_7digital.py @ 24:68a62ca32441

Organized python scripts

+"""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 os
+import sys
+import timeit
+
+import numpy
+
+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
+
+# Paulo: Additional libraries
+import cPickle
+from theano.sandbox.rng_mrg import MRG_RandomStreams as RandomStreams
+
+# Paulo: Rectifier Linear Unit
+# Source: http://stackoverflow.com/questions/26497564/theano-hiddenlayer-activation-function
+def relu(x):
+    return T.maximum(0.,x)
+        
+# Paulo: Random Streams
+srng = RandomStreams()
+
+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
+
+        # 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))
+        # initialize weights with random weights
+        W_bound = numpy.sqrt(6. / (fan_in + fan_out))
+        self.W = theano.shared(
+            numpy.asarray(
+                rng.uniform(low=-W_bound, high=W_bound, size=filter_shape),
+                dtype=theano.config.floatX
+            ),
+            borrow=True
+        )
+
+        # 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, borrow=True)
+
+        # convolve input feature maps with filters
+        conv_out = conv.conv2d(
+            input=input,
+            filters=self.W,
+            filter_shape=filter_shape,
+            image_shape=image_shape
+        )
+
+        # downsample each feature map individually, using maxpooling
+        pooled_out = downsample.max_pool_2d(
+            input=conv_out,
+            ds=poolsize,
+            ignore_border=True
+        )
+        
+        # Paulo: dropout
+        # Source: https://github.com/Newmu/Theano-Tutorials/blob/master/5_convolutional_net.py
+        retain_prob = 1 - 0.20
+        pooled_out *= srng.binomial(
+            pooled_out.shape,
+            p=retain_prob,
+            dtype=theano.config.floatX)
+        pooled_out /= retain_prob
+        
+        # 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'))
+        self.output = relu(pooled_out + self.b.dimshuffle('x', 0, 'x', 'x'))
+        
+        # store parameters of this layer
+        self.params = [self.W, self.b]
+
+        # keep track of model input
+        self.input = input
+
+'''
+def evaluate_lenet5(learning_rate=0.01, n_epochs=200,
+                    dataset='mnist.pkl.gz',
+                    nkerns=[32, 32], batch_size=10):
+    """ 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]
+
+    # compute number of minibatches for training, validation and testing
+    n_train_batches = train_set_x.get_value(borrow=True).shape[0]
+    n_valid_batches = valid_set_x.get_value(borrow=True).shape[0]
+    n_test_batches = test_set_x.get_value(borrow=True).shape[0]
+    
+    n_train_batches /= batch_size
+    n_valid_batches /= batch_size
+    n_test_batches /= batch_size
+
+    # allocate symbolic variables for the data
+    index = T.lscalar()  # index to a [mini]batch
+
+    # start-snippet-1
+    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
+
+    ######################
+    # 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
+    # (28, 28) is the size of MNIST images.
+    #layer0_input = x.reshape((batch_size, 1, 28, 28))
+    layer0_input = x.reshape((batch_size, 1, 130, 128))
+    # 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),
+        image_shape=(batch_size, 1, 130, 128),
+        #filter_shape=(nkerns[0], 1, 5, 5),
+        filter_shape=(nkerns[0], 1, 8, 1),
+        #poolsize=(2, 2)
+        poolsize=(4, 1)
+    )
+
+    # 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 (batch_size, nkerns[1], 4, 4)
+    layer1 = LeNetConvPoolLayer(
+        rng,
+        input=layer0.output,
+        #image_shape=(batch_size, nkerns[0], 12, 12),
+        image_shape=(batch_size, nkerns[0], 30, 128),
+        #filter_shape=(nkerns[1], nkerns[0], 5, 5),
+        filter_shape=(nkerns[1], nkerns[0], 8, 1),
+        #poolsize=(2, 2)
+        poolsize=(4, 1)
+    )
+    
+    # the HiddenLayer 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 (batch_size, nkerns[1] * 4 * 4),
+    # or (500, 50 * 4 * 4) = (500, 800) with the default values.
+    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_in=nkerns[1] * 5 * 128,
+        n_out=500,
+        #n_out=100,
+        #activation=T.tanh
+        activation=relu
+    )
+
+    # classify the values of the fully-connected sigmoidal layer
+    layer3 = LogisticRegression(input=layer2.output, n_in=500, n_out=10)
+    #layer4 = LogisticRegression(input=layer3.output, n_in=50, 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]
+        }
+    )
+    
+    # Paulo: Set best param for MLP pre-training
+    f = file('/homes/pchilguano/deep_learning/best_params.pkl', 'rb')
+    params0, params1, params2, params3 = cPickle.load(f)
+    f.close()
+    layer0.W.set_value(params0[0])
+    layer0.b.set_value(params0[1])
+    layer1.W.set_value(params1[0])
+    layer1.b.set_value(params1[1])
+    layer2.W.set_value(params2[0])
+    layer2.b.set_value(params2[1])
+    layer3.W.set_value(params3[0])
+    layer3.b.set_value(params3[1])
+    
+    # create a list of all model parameters to be fit by gradient descent
+    params = layer3.params + layer2.params + layer1.params + layer0.params
+    #params = layer4.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 list by automatically looping over all
+    # (params[i], grads[i]) pairs.
+    updates = [
+        (param_i, param_i - learning_rate * grad_i)
+        for param_i, grad_i in zip(params, grads)
+    ]
+
+    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]
+        }
+    )
+    # end-snippet-1
+
+    ###############
+    # TRAIN MODEL #
+    ###############
+    print '... training'
+    # early-stopping parameters
+    patience = 1000  # 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_validation_loss = numpy.inf
+    best_iter = 0
+    test_score = 0.
+    start_time = timeit.default_timer()
+
+    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 - 1) * 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.))
+                    # Paulo: Get best parameters for MLP
+                    best_params0 = [param.get_value().copy() for param in layer0.params]
+                    best_params1 = [param.get_value().copy() for param in layer1.params]
+                    best_params2 = [param.get_value().copy() for param in layer2.params]
+                    best_params3 = [param.get_value().copy() for param in layer3.params]
+                    #best_params4 = [param.get_value().copy() for param in layer4.params]
+                    
+            if patience <= iter:
+                done_looping = True
+                break
+
+    end_time = timeit.default_timer()
+    print('Optimization complete.')
+    print('Best validation score of %f %% obtained at iteration %i, '
+          'with test performance %f %%' %
+          (best_validation_loss * 100., best_iter + 1, test_score * 100.))
+    print >> sys.stderr, ('The code for file ' +
+                          os.path.split(__file__)[1] +
+                          ' ran for %.2fm' % ((end_time - start_time) / 60.))
+    # Paulo: Save best param for MLP
+    f = file('/homes/pchilguano/deep_learning/best_params.pkl', 'wb')
+    cPickle.dump((best_params0, best_params1, best_params2, best_params3), f, protocol=cPickle.HIGHEST_PROTOCOL)
+    f.close()
+'''    
+def genres_lenet5(dataset, nkerns=[32, 32], batch_size=10):
+    """
+    :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)
+    
+    f = file(dataset, 'rb')
+    data_x = cPickle.load(f)
+    f.close()
+    
+    test_set_x = theano.shared(
+        numpy.asarray(
+            data_x,
+            dtype=theano.config.floatX
+        ),
+        borrow=True
+    )    
+    
+
+    #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]
+
+    # compute number of minibatches for training, validation and testing
+    #n_train_batches = train_set_x.get_value(borrow=True).shape[0]
+    #n_valid_batches = valid_set_x.get_value(borrow=True).shape[0]
+    n_test_batches = test_set_x.get_value(borrow=True).shape[0]
+    
+    #n_train_batches /= batch_size
+    #n_valid_batches /= batch_size
+    n_test_batches /= batch_size
+
+    # allocate symbolic variables for the data
+    index = T.lscalar()  # index to a [mini]batch
+
+    # start-snippet-1
+    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
+
+    ######################
+    # 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
+    # (28, 28) is the size of MNIST images.
+    layer0_input = x.reshape((batch_size, 1, 130, 128))
+    # 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, 130, 128),
+        filter_shape=(nkerns[0], 1, 8, 1),
+        poolsize=(4, 1)
+    )
+
+    # 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 (batch_size, nkerns[1], 4, 4)
+    layer1 = LeNetConvPoolLayer(
+        rng,
+        input=layer0.output,
+        image_shape=(batch_size, nkerns[0], 30, 128),
+        filter_shape=(nkerns[1], nkerns[0], 8, 1),
+        poolsize=(4, 1)
+    )
+    
+    # the HiddenLayer 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 (batch_size, nkerns[1] * 4 * 4),
+    # or (500, 50 * 4 * 4) = (500, 800) with the default values.
+    layer2_input = layer1.output.flatten(2)
+
+    # construct a fully-connected sigmoidal layer
+    layer2 = HiddenLayer(
+        rng,
+        input=layer2_input,
+        n_in=nkerns[1] * 5 * 128,
+        n_out=500,
+        activation=relu
+    )
+
+    # 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]
+        }
+    )
+    '''
+    # Genre soft classification
+    test_model = theano.function(
+        [index],
+        layer3.p_y_given_x,
+        givens={
+            x: test_set_x[index * batch_size: (index + 1) * batch_size]
+        }
+    )
+    
+    # Paulo: Set best paramaters
+    f = file('/homes/pchilguano/msc_project/dataset/genre_classification/\
+best_params.pkl', 'rb')
+    params0, params1, params2, params3 = cPickle.load(f)
+    f.close()
+    layer0.W.set_value(params0[0])
+    layer0.b.set_value(params0[1])
+    layer1.W.set_value(params1[0])
+    layer1.b.set_value(params1[1])
+    layer2.W.set_value(params2[0])
+    layer2.b.set_value(params2[1])
+    layer3.W.set_value(params3[0])
+    layer3.b.set_value(params3[1])
+    
+    # Probabilities
+    print "Computing probabilities..."
+    start_time = timeit.default_timer()
+    genre_prob_batch = [test_model(i).tolist() for i in xrange(n_test_batches)]
+    end_time = timeit.default_timer()
+    print >> sys.stderr, ('The code for file ' +
+                          os.path.split(__file__)[1] +
+                          ' ran for %.2fm' % ((end_time - start_time) / 60.))
+    genre_prob = [item for sublist in genre_prob_batch for item in sublist]
+    
+    filename = '/homes/pchilguano/msc_project/dataset/7digital/lists/\
+audio_files.txt'
+    with open(filename, 'r') as f:
+        songID = [line.strip().split('/')[-1][:-4] for line in f]
+    
+    items = dict(zip(songID, genre_prob))    
+    print "Saving songs feature vectors in dictionary..."
+    f = file('/homes/pchilguano/msc_project/dataset/genre_classification/\
+genre_prob.pkl', 'wb')
+    cPickle.dump(items, f, protocol=cPickle.HIGHEST_PROTOCOL)
+    f.close()
+    
+    '''
+    # 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 list by automatically looping over all
+    # (params[i], grads[i]) pairs.
+    updates = [
+        (param_i, param_i - learning_rate * grad_i)
+        for param_i, grad_i in zip(params, grads)
+    ]
+
+    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]
+        }
+    )
+    # end-snippet-1
+
+    ###############
+    # TRAIN MODEL #
+    ###############
+    print '... training'
+    # early-stopping parameters
+    patience = 1000  # 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_validation_loss = numpy.inf
+    best_iter = 0
+    test_score = 0.
+    start_time = timeit.default_timer()
+
+    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 - 1) * 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.))
+                    # Paulo: Get best parameters for MLP
+                    best_params0 = [param.get_value().copy() for param in layer0.params]
+                    best_params1 = [param.get_value().copy() for param in layer1.params]
+                    best_params2 = [param.get_value().copy() for param in layer2.params]
+                    best_params3 = [param.get_value().copy() for param in layer3.params]
+                    
+            if patience <= iter:
+                done_looping = True
+                break
+
+    end_time = timeit.default_timer()
+    print('Optimization complete.')
+    print('Best validation score of %f %% obtained at iteration %i, '
+          'with test performance %f %%' %
+          (best_validation_loss * 100., best_iter + 1, test_score * 100.))
+    print >> sys.stderr, ('The code for file ' +
+                          os.path.split(__file__)[1] +
+                          ' ran for %.2fm' % ((end_time - start_time) / 60.))
+    
+    # Paulo: Save best param for MLP
+    f = file('/homes/pchilguano/deep_learning/genre_prob.pkl', 'wb')
+    cPickle.dump((best_params0, best_params1, best_params2, best_params3), f, protocol=cPickle.HIGHEST_PROTOCOL)
+    f.close()
+    '''
+if __name__ == '__main__':
+    #evaluate_lenet5()
+    genres_lenet5(
+        dataset='/homes/pchilguano/msc_project/dataset/7digital/features/\
+feats.pkl'
+    )
+
+#def experiment(state, channel):
+#    evaluate_lenet5(state.learning_rate, dataset=state.dataset)