Mercurial > hg > hybrid-music-recommender-using-content-based-and-social-information
changeset 24:68a62ca32441
Organized python scripts
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/Code/7digital_fetch_audio.py Sat Aug 15 19:16:17 2015 +0100 @@ -0,0 +1,96 @@ +# -*- coding: utf-8 -*- +""" +Created on Wed Jul 15 00:41:44 2015 + +@author: paulochiliguano +""" + + +import csv +import time +from pyechonest import song, config #http://echonest.github.io/pyechonest/ +import oauth2 as oauth #https://github.com/jasonrubenstein/python_oauth2 +import urllib2 +import os + +# 7digital keys +consumer_key = '7ds28qendsk9' +consumer_secret = 'm5nsktn3hu6x45cy' +consumer = oauth.Consumer(consumer_key, consumer_secret) + +# EchoNest key +config.ECHO_NEST_API_KEY="LINDFDUTQZQ781IE8" + +# Retrieve audio clips +mp3_folder = '/Users/paulochiliguano/Documents/msc-project/Dataset/clips/' +filename_echonest = '/Users/paulochiliguano/Documents/msc-project/Dataset/\ +CF_dataset_songID.txt' +filename_7digital = '/Users/paulochiliguano/Documents/msc-project/Dataset/\ +CF_dataset_metadata.txt' +with open(filename_echonest, 'rb') as f, open(filename_7digital, 'wb') as out: + writer = csv.writer(out, delimiter='\t') + for i in xrange(1218): + f.readline() + next = f.readline() + while next != "": + try: + s = song.Song(next) + #s = song.Song('SOPEXHZ12873FD2AC7') + #except: + except IndexError: + time.sleep(3) + print "%s not available" % next[:-1] + next = f.readline() + else: + time.sleep(3) + try: + ss_tracks = s.get_tracks('7digital-UK') + except: + time.sleep(3) + print "%s not in UK catalog" % next[:-1] + next = f.readline() + else: + #print(len(ss_tracks)) + if len(ss_tracks) != 0: + ss_track = ss_tracks[0] + preview_url = ss_track.get('preview_url') + track_id = ss_track.get('id') + + req = oauth.Request( + method="GET", + url=preview_url, + is_form_encoded=True + ) + req['oauth_timestamp'] = oauth.Request.make_timestamp() + req['oauth_nonce'] = oauth.Request.make_nonce() + req['country'] = "GB" + sig_method = oauth.SignatureMethod_HMAC_SHA1() + req.sign_request(sig_method, consumer, token=None) + + try: + response = urllib2.urlopen(req.to_url()) + except: + #time.sleep(16) + print "No available preview for %s" % next[:-1] + #writer.writerow([next[:-2], 'NA', s.artist_name.encode("utf-8"), s.title.encode("utf-8")]) + else: + print([ + next[:-1], + track_id, + s.artist_name, + s.title, + preview_url + ]) + writer.writerow([ + next[:-1], + track_id, + s.artist_name.encode("utf-8"), + s.title.encode("utf-8"), + preview_url + ]) + mp3_file = os.path.join(mp3_folder, next[:-1]+'.mp3') + with open(mp3_file, 'wb') as songfile: + songfile.write(response.read()) + time.sleep(16) + next = f.readline() + \ No newline at end of file
--- a/Code/convolutional_mlp.py Tue Aug 11 14:23:42 2015 +0100 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,403 +0,0 @@ -"""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() - -if __name__ == '__main__': - evaluate_lenet5() - - -def experiment(state, channel): - evaluate_lenet5(state.learning_rate, dataset=state.dataset) -
--- a/Code/eda.py Tue Aug 11 14:23:42 2015 +0100 +++ b/Code/eda.py Sat Aug 15 19:16:17 2015 +0100 @@ -8,8 +8,11 @@ from math import sqrt, log10 import numpy as np +import pandas as pd from sklearn import mixture +#Fine tuning + #User-item dictionary users = {"Angelica": {"SOAJJPC12AB017D63F": 3.5, "SOAKIXJ12AC3DF7152": 2.0, "SOAKPFH12A8C13BA4A": 4.5, "SOAGTJW12A6701F1F5": 5.0, @@ -49,43 +52,140 @@ "SOAJZEP12A8C14379B": [5, 5, 4, 2, 1, 1, 1, 5, 4, 1], "SOAHQFM12A8C134B65": [2.5, 4, 4, 1, 1, 1, 1, 5, 4, 1]} -#Functions to compute similarity between items or between profiles +# Functions to compute similarity between items or between profiles # Source: http://www.guidetodatamining.com def manhattan(vector1, vector2): """Computes the Manhattan distance.""" - distance = 0 - total = 0 - n = len(vector1) - for i in range(n): - distance += abs(vector1[i] - vector2[i]) - return distance + return sum(map(lambda v1, v2: abs(v1 - v2), vector1, vector2)) -def computeNearestNeighbor(itemName, itemVector, items): - """creates a sorted list of items based on their distance to item""" - distances = [] - for otherItem in items: - if otherItem != itemName: - distance = manhattan(itemVector, items[otherItem]) - distances.append((distance, otherItem)) - # sort based on distance -- closest first - distances.sort() - return distances +def nearestNeighbor(self, itemVector): + """return nearest neighbor to itemVector""" + return min([( + self.manhattan(itemVector, item[1]), item) for item in self.data + ]) -def classify(user, itemName, itemVector): - """Classify the itemName based on user ratings - Should really have items and users as parameters""" - # first find nearest neighbor - nearest = computeNearestNeighbor(itemName, itemVector, items)[0][1] - rating = users[user][nearest] - return rating +def classify(self, itemVector): + """Return class we think item Vector is in""" + return(self.nearestNeighbor(self.normalizeVector(itemVector))[1][0]) +''' +# Median +# http://stackoverflow.com/questions/24101524/finding-median-of-list-in-python +def get_median(lst): + return np.median(np.array(lst)) -# Fitness function of EDA -def Fitness(profile, user): - nearest = computeNearestNeighbor(itemName, itemVector, items)[0][1] - rating = users[user][nearest] - return rating +# Absolute Standard Deviation +def get_asd(lst, median): + sum = 0 + for item in lst: + sum += abs(item - median) + return sum / len(lst) - +# Normalisation rating with Modified Standard Score +def normalize_rating(ratings, median, asd): + for i in range(len(ratings)): + ratings[i] = (ratings[i] - median) / asd + return ratings +''' +# Normalise user play count +for userID in users: + song_play_count = pd.DataFrame( + users[userID].items(), + columns=["songID", "play_count"] + ) + '''Coefficient of variation''' + cv = song_play_count.play_count.std() / song_play_count.play_count.mean() + #user_ratings = np.array(users[userID].values()) + #cv = user_ratings.std()/user_ratings.mean() + #print userID, cv + if cv <= 0.5: + for songID, play_count in users[userID].items(): + users[userID][songID] = 3 + else: + song_play_count_q = pd.cut( + song_play_count["play_count"], + 5, + labels=False + ) + 1 + song_play_count.play_count = song_play_count_q + users[userID] = song_play_count.set_index('songID')['play_count'].to_dict() + #print song_play_count + #median = get_median(user_ratings) + #asd = get_asd(user_ratings, median) + #for songID, play_count in users[userID].items(): + #users[userID][songID] = (play_count - median) / asd + +# Subset of most-liked items +users_subset = {} +for userID, songs in users.iteritems(): + scores_above_threshold = { + songID: score for songID, score in songs.iteritems() if score > 2 + } + users_subset[userID]= scores_above_threshold + ''' + for songID, score in songs.iteritems(): + print score >0 + if score > 0: + print {userID: {songID: score}} + +{k: v for k, v in users.iteritems() for i,j in v.iteritems() if j > 0} +''' +# Fitness function for EDA +def Fitness(profile, user_subset): + fitness_value = 0 + for songID, score in user_subset.iteritems(): + fitness_value += log10(score * manhattan(profile, items[songID])) + return fitness_value + +# Given parameters for EDA +population_size = len(users_subset) +fraction_of_population = int(round(0.5 * population_size)) + +# Generation of M individuals uniformly +np.random.seed(len(users_subset)) +M = np.random.rand(population_size, len(items.values()[0])) +#M.shape = (-1, len(items.values()[0])) +profile = {} +i = 0 +for userID in users_subset: + profile[userID] = M.tolist()[i] + i += 1 + +# Compute fitness values +users_fitness = {} +for userID in profile: + users_fitness[userID] = Fitness(profile[userID], users_subset[userID]) +users_fitness_df = pd.DataFrame( + users_fitness.items(), + columns=["userID", "fitness"] +) + +# Selection of best individuals based on fitness values +best_individuals = {} +users_fitness_df = users_fitness_df.sort(columns='fitness') +M_sel = users_fitness_df.head(fraction_of_population) +M_sel_dict = M_sel.set_index('userID')['fitness'].to_dict() +for userID in M_sel_dict: + best_individuals[userID] = profile[userID] + +# Calculate sample mean and standard deviation +np.random.seed(1) +g = mixture.GMM(n_components=10) +# Generate random observations with two modes centered on 0 +# and 10 to use for training. +obs = np.concatenate((np.random.randn(100, 1), 10 + np.random.randn(300, 1))) +g.fit(obs) +np.round(g.weights_, 2) +np.round(g.means_, 2) +np.round(g.covars_, 2) +g.predict([[0], [2], [9], [10]]) +np.round(g.score([[0], [2], [9], [10]]), 2) +# Refit the model on new data (initial parameters remain the +# same), this time with an even split between the two modes. +g.fit(20 * [[0]] + 20 * [[10]]) +np.round(g.weights_, 2) + + +''' # Pearson Correlation Coefficient def pearson(rating1, rating2): sum_xy = 0 @@ -145,7 +245,7 @@ return sum_xy / denominator -''' + def Fitness(profile, user_index): sim = 0 sum_log = 0 @@ -163,40 +263,12 @@ #sum_log += log10(rating * sim) return sim ''' -# Generation of M individuals uniformly -population_size = len(users) -fraction_of_population = 0.5 -np.random.seed(len(users)) -M = np.random.uniform(size=population_size * len(items.values()[0])) -M.shape = (-1, len(items.values()[0])) -profile = {} -i = 0 -for row in M.tolist(): - profile["Profile" + str(i)] = M.tolist()[i] - i = i + 1 -''' -Calculate fitness values -''' -Fitness(profile, 0) -np.random.seed(1) -g = mixture.GMM(n_components=7) -# Generate random observations with two modes centered on 0 -# and 10 to use for training. -obs = np.concatenate((np.random.randn(100, 1), 10 + np.random.randn(300, 1))) -g.fit(obs) -np.round(g.weights_, 2) -np.round(g.means_, 2) -np.round(g.covars_, 2) -g.predict([[0], [2], [9], [10]]) -np.round(g.score([[0], [2], [9], [10]]), 2) -# Refit the model on new data (initial parameters remain the -# same), this time with an even split between the two modes. -g.fit(20 * [[0]] + 20 * [[10]]) -np.round(g.weights_, 2) + +
--- a/Code/feature_extraction.py Tue Aug 11 14:23:42 2015 +0100 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,108 +0,0 @@ -""" -Feature extraction. -Siddharth Sigia -Feb,2014 -C4DM -""" -import numpy as np #Paulo: numpy as standard -#import subprocess -#import sys -import os -#from spectrogram import SpecGram -import tables -#import pdb -# Paulo Chiliguano: library for mel spectrogram -import librosa -#import random - -def read_wav(filename): - #bits_per_sample = '16' - #cmd = ['sox',filename,'-t','raw','-e','unsigned-integer','-L','-c','1','-b',bits_per_sample,'-','pad','0','30.0','rate','22050.0','trim','0','30.0'] - #cmd = ' '.join(cmd) - #print cmd - #raw_audio = numpy.fromstring(subprocess.Popen(cmd,stdout=subprocess.PIPE,shell=True).communicate()[0],dtype='uint16') - audioFile, sr = librosa.load(filename, sr=22050, mono=True, duration=28) - #random.randint(0,audioFile.size) - #max_amp = 2.**(int(bits_per_sample)-1) - #raw_audio = (raw_audio- max_amp)/max_amp - return audioFile - -def calc_specgram(x,fs,winSize,): - S = librosa.feature.melspectrogram(y=x, sr=fs, n_mels=128, S=None, n_fft=winSize, hop_length=512) - log_S = librosa.logamplitude(S, ref_power=np.max) - log_S = np.transpose(log_S) - #spec = SpecGram(x,fs,winSize) - #return spec.specMat - return log_S - - -def make_4tensor(x): - assert x.ndim <= 4 - while x.ndim < 4: - x = np.expand_dims(x,0) - return x - -class FeatExtraction(): - def __init__(self,dataset_dir): - self.dataset_dir = dataset_dir - self.list_dir = os.path.join(self.dataset_dir,'lists') - self.get_filenames() - self.feat_dir = os.path.join(self.dataset_dir,'features') - self.make_feat_dir() - self.h5_filename = os.path.join(self.feat_dir,'feats.h5') - self.make_h5() - self.setup_h5() - self.extract_features() - self.close_h5() - - - def get_filenames(self,): - dataset_files = os.path.join(self.list_dir,'audio_files.txt') - self.filenames = [l.strip() for l in open(dataset_files,'r').readlines()] - self.num_files = len(self.filenames) - - def make_feat_dir(self,): - if not os.path.exists(self.feat_dir): - print 'Making output dir.' - os.mkdir(self.feat_dir) - else: - print 'Output dir already exists.' - - def make_h5(self,): - if not os.path.exists(self.h5_filename): - self.h5 = tables.openFile(self.h5_filename,'w') - else: - print 'Feature file already exists.' - self.h5 = tables.openFile(self.h5_filename,'a') - - def setup_h5(self,): - filename = self.filenames[0] - x = read_wav(filename) - spec_x = calc_specgram(x,22050,1024) - spec_x = make_4tensor(spec_x) - self.data_shape = spec_x.shape[1:] - self.x_earray_shape = (0,) + self.data_shape - self.chunkshape = (1,) + self.data_shape - self.h5_x = self.h5.createEArray('/','x',tables.FloatAtom(itemsize=4),self.x_earray_shape,chunkshape=self.chunkshape,expectedrows=self.num_files) - self.h5_filenames = self.h5.createEArray('/','filenames',tables.StringAtom(256),(0,),expectedrows=self.num_files) - self.h5_x.append(spec_x) - self.h5_filenames.append([filename]) - - - def extract_features(self,): - for i in xrange(1,self.num_files): - filename = self.filenames[i] - #print 'Filename: ',filename - x = read_wav(filename) - spec_x = calc_specgram(x,22050,1024) - spec_x = make_4tensor(spec_x) - self.h5_x.append(spec_x) - self.h5_filenames.append([filename]) - - def close_h5(self,): - self.h5.flush() - self.h5.close() - -if __name__ == '__main__': - test = FeatExtraction('/home/paulo/Downloads') -
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/Code/genre_classification/classification/convolutional_mlp_7digital.py Sat Aug 15 19:16:17 2015 +0100 @@ -0,0 +1,688 @@ +"""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)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/Code/genre_classification/classification/logistic_sgd.py Sat Aug 15 19:16:17 2015 +0100 @@ -0,0 +1,472 @@ +""" +This tutorial introduces logistic regression using Theano and stochastic +gradient descent. + +Logistic regression is a probabilistic, linear classifier. It is parametrized +by a weight matrix :math:`W` and a bias vector :math:`b`. Classification is +done by projecting data points onto a set of hyperplanes, the distance to +which is used to determine a class membership probability. + +Mathematically, this can be written as: + +.. math:: + P(Y=i|x, W,b) &= softmax_i(W x + b) \\ + &= \frac {e^{W_i x + b_i}} {\sum_j e^{W_j x + b_j}} + + +The output of the model or prediction is then done by taking the argmax of +the vector whose i'th element is P(Y=i|x). + +.. math:: + + y_{pred} = argmax_i P(Y=i|x,W,b) + + +This tutorial presents a stochastic gradient descent optimization method +suitable for large datasets. + + +References: + + - textbooks: "Pattern Recognition and Machine Learning" - + Christopher M. Bishop, section 4.3.2 + +""" +__docformat__ = 'restructedtext en' + +import cPickle +import gzip +import os +import sys +import timeit + +import numpy + +import theano +import theano.tensor as T + + +class LogisticRegression(object): + """Multi-class Logistic Regression Class + + The logistic regression is fully described by a weight matrix :math:`W` + and bias vector :math:`b`. Classification is done by projecting data + points onto a set of hyperplanes, the distance to which is used to + determine a class membership probability. + """ + + def __init__(self, input, n_in, n_out): + """ Initialize the parameters of the logistic regression + + :type input: theano.tensor.TensorType + :param input: symbolic variable that describes the input of the + architecture (one minibatch) + + :type n_in: int + :param n_in: number of input units, the dimension of the space in + which the datapoints lie + + :type n_out: int + :param n_out: number of output units, the dimension of the space in + which the labels lie + + """ + # start-snippet-1 + # 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 + ), + name='W', + borrow=True + ) + # initialize the baises b as a vector of n_out 0s + self.b = theano.shared( + value=numpy.zeros( + (n_out,), + dtype=theano.config.floatX + ), + name='b', + borrow=True + ) + + # symbolic expression for computing the matrix of class-membership + # probabilities + # Where: + # W is a matrix where column-k represent the separation hyperplane for + # class-k + # x is a matrix where row-j represents input training sample-j + # b is a vector where element-k represent the free parameter of + # hyperplane-k + self.p_y_given_x = T.nnet.softmax(T.dot(input, self.W) + self.b) + + # symbolic description of how to compute prediction as class whose + # probability is maximal + self.y_pred = T.argmax(self.p_y_given_x, axis=1) + # end-snippet-1 + + # parameters of the model + self.params = [self.W, self.b] + + # keep track of model input + self.input = input + + 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}) + + :type y: theano.tensor.TensorType + :param y: corresponds to a vector that gives for each example the + correct label + + Note: we use the mean instead of the sum so that + the learning rate is less dependent on the batch size + """ + # start-snippet-2 + # y.shape[0] is (symbolically) the number of rows in y, i.e., + # number of examples (call it n) in the minibatch + # T.arange(y.shape[0]) is a symbolic vector which will contain + # [0,1,2,... n-1] T.log(self.p_y_given_x) is a matrix of + # Log-Probabilities (call it LP) with one row per example and + # one column per class LP[T.arange(y.shape[0]),y] is a vector + # v containing [LP[0,y[0]], LP[1,y[1]], LP[2,y[2]], ..., + # LP[n-1,y[n-1]]] and T.mean(LP[T.arange(y.shape[0]),y]) is + # the mean (across minibatch examples) of the elements in v, + # i.e., the mean log-likelihood across the minibatch. + return -T.mean(T.log(self.p_y_given_x)[T.arange(y.shape[0]), y]) + # end-snippet-2 + + def errors(self, y): + """Return a float representing the number of errors in the minibatch + over the total number of examples of the minibatch ; zero one + loss over the size of the minibatch + + :type y: theano.tensor.TensorType + :param y: corresponds to a vector that gives for each example the + correct label + """ + + # 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', y.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 load_data(dataset): + ''' Loads the dataset + + :type dataset: string + :param dataset: the path to the dataset (here MNIST) + ''' + ############# + # LOAD DATA # + ############# + ''' + # Download the MNIST dataset if it is not present + data_dir, data_file = os.path.split(dataset) + if data_dir == "" and not os.path.isfile(dataset): + # Check if dataset is in the data directory. + new_path = os.path.join( + os.path.split(__file__)[0], + "..", + "data", + dataset + ) + if os.path.isfile(new_path) or data_file == 'mnist.pkl.gz': + dataset = new_path + + if (not os.path.isfile(dataset)) and data_file == 'mnist.pkl.gz': + import urllib + origin = ( + 'http://www.iro.umontreal.ca/~lisa/deep/data/mnist/mnist.pkl.gz' + ) + print 'Downloading data from %s' % origin + urllib.urlretrieve(origin, dataset) + + print '... loading data' + + # Load the dataset + f = gzip.open(dataset, 'rb') + ''' + f = file(dataset, 'rb') + train_set, valid_set, test_set = cPickle.load(f) + f.close() + #train_set, valid_set, test_set format: tuple(input, target) + #input is an numpy.ndarray of 2 dimensions (a matrix) + #witch row's correspond to an example. target is a + #numpy.ndarray of 1 dimensions (vector)) that have the same length as + #the number of rows in the input. It should give the target + #target to the example with the same index in the input. + + def shared_dataset(data_xy, borrow=True): + """ Function that loads the dataset into shared variables + + The reason we store our dataset in shared variables is to allow + Theano to copy it into the GPU memory (when code is run on GPU). + Since copying data into the GPU is slow, copying a minibatch everytime + is needed (the default behaviour if the data is not in a shared + variable) would lead to a large decrease in performance. + """ + data_x, data_y = data_xy + shared_x = theano.shared(numpy.asarray(data_x, + dtype=theano.config.floatX), + borrow=borrow) + shared_y = theano.shared(numpy.asarray(data_y, + dtype=theano.config.floatX), + borrow=borrow) + # When storing data on the GPU it has to be stored as floats + # therefore we will store the labels as ``floatX`` as well + # (``shared_y`` does exactly that). But during our computations + # we need them as ints (we use labels as index, and if they are + # floats it doesn't make sense) therefore instead of returning + # ``shared_y`` we will have to cast it to int. This little hack + # lets ous get around this issue + return shared_x, T.cast(shared_y, 'int32') + + test_set_x, test_set_y = shared_dataset(test_set) + valid_set_x, valid_set_y = shared_dataset(valid_set) + train_set_x, train_set_y = shared_dataset(train_set) + + rval = [(train_set_x, train_set_y), (valid_set_x, valid_set_y), + (test_set_x, test_set_y)] + return rval + + +def sgd_optimization_mnist(learning_rate=0.13, n_epochs=1000, + dataset='mnist.pkl.gz', + batch_size=600): + """ + Demonstrate stochastic gradient descent optimization of a log-linear + model + + This is demonstrated on MNIST. + + :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: the path of the MNIST dataset file from + http://www.iro.umontreal.ca/~lisa/deep/data/mnist/mnist.pkl.gz + + """ + 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] / batch_size + n_valid_batches = valid_set_x.get_value(borrow=True).shape[0] / batch_size + n_test_batches = test_set_x.get_value(borrow=True).shape[0] / batch_size + + ###################### + # BUILD ACTUAL MODEL # + ###################### + print '... building the model' + + # allocate symbolic variables for the data + index = T.lscalar() # index to a [mini]batch + + # generate symbolic variables for input (x and y represent a + # minibatch) + x = T.matrix('x') # data, presented as rasterized images + y = T.ivector('y') # labels, presented as 1D vector of [int] labels + + # construct the logistic regression class + # Each MNIST image has size 28*28 + classifier = LogisticRegression(input=x, n_in=28 * 28, n_out=10) + + # the cost we minimize during training is the negative log likelihood of + # the model in symbolic format + cost = classifier.negative_log_likelihood(y) + + # compiling a Theano function that computes the mistakes that are made by + # the model on a minibatch + test_model = theano.function( + inputs=[index], + outputs=classifier.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( + inputs=[index], + outputs=classifier.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] + } + ) + + # compute the gradient of cost with respect to theta = (W,b) + g_W = T.grad(cost=cost, wrt=classifier.W) + g_b = T.grad(cost=cost, wrt=classifier.b) + + # start-snippet-3 + # specify how to update the parameters of the model as a list of + # (variable, update expression) pairs. + updates = [(classifier.W, classifier.W - learning_rate * g_W), + (classifier.b, classifier.b - learning_rate * g_b)] + + # 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( + inputs=[index], + outputs=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-3 + + ############### + # TRAIN MODEL # + ############### + print '... training the model' + # early-stopping parameters + patience = 5000 # 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 + test_score = 0. + start_time = timeit.default_timer() + + done_looping = False + epoch = 0 + while (epoch < n_epochs) and (not done_looping): + epoch = epoch + 1 + for minibatch_index in xrange(n_train_batches): + + minibatch_avg_cost = train_model(minibatch_index) + # iteration number + iter = (epoch - 1) * n_train_batches + 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) + + best_validation_loss = this_validation_loss + # 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. + ) + ) + + # save the best model + with open('best_model.pkl', 'w') as f: + cPickle.dump(classifier, f) + + if patience <= iter: + done_looping = True + break + + end_time = timeit.default_timer() + print( + ( + 'Optimization complete with best validation score of %f %%,' + 'with test performance %f %%' + ) + % (best_validation_loss * 100., test_score * 100.) + ) + print 'The code run for %d epochs, with %f epochs/sec' % ( + epoch, 1. * epoch / (end_time - start_time)) + print >> sys.stderr, ('The code for file ' + + os.path.split(__file__)[1] + + ' ran for %.1fs' % ((end_time - start_time))) + + +def predict(): + """ + An example of how to load a trained model and use it + to predict labels. + """ + + # load the saved model + classifier = cPickle.load(open('best_model.pkl')) + + # compile a predictor function + predict_model = theano.function( + inputs=[classifier.input], + outputs=classifier.y_pred) + + # We can test it on some examples from test test + dataset='mnist.pkl.gz' + datasets = load_data(dataset) + test_set_x, test_set_y = datasets[2] + test_set_x = test_set_x.get_value() + + predicted_values = predict_model(test_set_x[:10]) + print ("Predicted values for the first 10 examples in test set:") + print predicted_values + + +if __name__ == '__main__': + sgd_optimization_mnist() +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/Code/genre_classification/classification/mlp.py Sat Aug 15 19:16:17 2015 +0100 @@ -0,0 +1,412 @@ +""" +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 +intermediate 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 + +""" +__docformat__ = 'restructedtext en' + + +import os +import sys +import timeit + +import numpy + +import theano +import theano.tensor as T + + +from logistic_sgd import LogisticRegression, load_data + + +# start-snippet-1 +class HiddenLayer(object): + def __init__(self, rng, input, n_in, n_out, W=None, b=None, + activation=T.tanh): + """ + Typical hidden layer of a MLP: units are fully-connected and have + sigmoidal activation function. Weight matrix W is of shape (n_in,n_out) + and the bias vector b is of shape (n_out,). + + NOTE : The nonlinearity used here is tanh + + Hidden unit activation is given by: tanh(dot(input,W) + b) + + :type rng: numpy.random.RandomState + :param rng: a random number generator used to initialize weights + + :type input: theano.tensor.dmatrix + :param input: a symbolic tensor of shape (n_examples, n_in) + + :type n_in: int + :param n_in: dimensionality of input + + :type n_out: int + :param n_out: number of hidden units + + :type activation: theano.Op or function + :param activation: Non linearity to be applied in the hidden + layer + """ + self.input = input + # end-snippet-1 + + # `W` is initialized with `W_values` which is uniformely sampled + # from sqrt(-6./(n_in+n_hidden)) and sqrt(6./(n_in+n_hidden)) + # for tanh activation function + # the output of uniform if converted using asarray to dtype + # theano.config.floatX so that the code is runable on GPU + # Note : optimal initialization of weights is dependent on the + # activation function used (among other things). + # For example, results presented in [Xavier10] suggest that you + # should use 4 times larger initial weights for sigmoid + # compared to tanh + # We have no info for other function, so we use the same as + # tanh. + if W is None: + 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 + ) + if activation == theano.tensor.nnet.sigmoid: + W_values *= 4 + + W = theano.shared(value=W_values, name='W', borrow=True) + + if b is None: + b_values = numpy.zeros((n_out,), dtype=theano.config.floatX) + b = theano.shared(value=b_values, name='b', borrow=True) + + self.W = W + self.b = b + + lin_output = T.dot(input, self.W) + self.b + self.output = ( + lin_output if activation is None + else activation(lin_output) + ) + # parameters of the model + self.params = [self.W, self.b] + + +# start-snippet-2 +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. + Intermediate layers usually have as activation function tanh or the + sigmoid function (defined here by a ``HiddenLayer`` class) while the + top layer is a softmax layer (defined here by a ``LogisticRegression`` + class). + """ + + def __init__(self, rng, input, n_in, n_hidden, n_out): + """Initialize the parameters for the multilayer perceptron + + :type rng: numpy.random.RandomState + :param rng: a random number generator used to initialize weights + + :type input: theano.tensor.TensorType + :param input: symbolic variable that describes the input of the + architecture (one minibatch) + + :type n_in: int + :param n_in: number of input units, the dimension of the space in + which the datapoints lie + + :type n_hidden: int + :param n_hidden: number of hidden units + + :type n_out: int + :param n_out: number of output units, the dimension of the space in + which the labels lie + + """ + + # Since we are dealing with a one hidden layer MLP, this will translate + # into a HiddenLayer with a tanh activation function connected to the + # LogisticRegression layer; the activation function can be replaced by + # sigmoid or any other nonlinear function + self.hiddenLayer = HiddenLayer( + rng=rng, + input=input, + n_in=n_in, + n_out=n_hidden, + activation=T.tanh + ) + + # The logistic regression layer gets as input the hidden units + # of the hidden layer + self.logRegressionLayer = LogisticRegression( + input=self.hiddenLayer.output, + n_in=n_hidden, + n_out=n_out + ) + # end-snippet-2 start-snippet-3 + # L1 norm ; one regularization option is to enforce L1 norm to + # be small + self.L1 = ( + abs(self.hiddenLayer.W).sum() + + abs(self.logRegressionLayer.W).sum() + ) + + # square of L2 norm ; one regularization option is to enforce + # square of L2 norm to be small + self.L2_sqr = ( + (self.hiddenLayer.W ** 2).sum() + + (self.logRegressionLayer.W ** 2).sum() + ) + + # negative log likelihood of the MLP is given by the negative + # log likelihood of the output of the model, computed in the + # logistic regression layer + self.negative_log_likelihood = ( + self.logRegressionLayer.negative_log_likelihood + ) + # same holds for the function computing the number of errors + self.errors = self.logRegressionLayer.errors + + # the parameters of the model are the parameters of the two layer it is + # made out of + self.params = self.hiddenLayer.params + self.logRegressionLayer.params + # end-snippet-3 + + # keep track of model input + self.input = input + + +def test_mlp(learning_rate=0.01, L1_reg=0.00, L2_reg=0.0001, n_epochs=1000, + dataset='mnist.pkl.gz', batch_size=20, n_hidden=500): + """ + Demonstrate stochastic gradient descent optimization for a multilayer + perceptron + + This is demonstrated on MNIST. + + :type learning_rate: float + :param learning_rate: learning rate used (factor for the stochastic + gradient + + :type L1_reg: float + :param L1_reg: L1-norm's weight when added to the cost (see + regularization) + + :type L2_reg: float + :param L2_reg: L2-norm's weight when added to the cost (see + regularization) + + :type n_epochs: int + :param n_epochs: maximal number of epochs to run the optimizer + + :type dataset: string + :param dataset: the path of the MNIST dataset file from + http://www.iro.umontreal.ca/~lisa/deep/data/mnist/mnist.pkl.gz + + + """ + 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] / batch_size + n_valid_batches = valid_set_x.get_value(borrow=True).shape[0] / batch_size + n_test_batches = test_set_x.get_value(borrow=True).shape[0] / batch_size + + ###################### + # BUILD ACTUAL MODEL # + ###################### + print '... building the model' + + # allocate symbolic variables for the data + index = T.lscalar() # index to a [mini]batch + 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 + + rng = numpy.random.RandomState(1234) + + # construct the MLP class + classifier = MLP( + rng=rng, + input=x, + n_in=28 * 28, + n_hidden=n_hidden, + n_out=10 + ) + + # start-snippet-4 + # 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 + ) + # end-snippet-4 + + # compiling a Theano function that computes the mistakes that are made + # by the model on a minibatch + test_model = theano.function( + inputs=[index], + outputs=classifier.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( + inputs=[index], + outputs=classifier.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] + } + ) + + # start-snippet-5 + # compute the gradient of cost with respect to theta (sotred in params) + # the resulting gradients will be stored in a list gparams + gparams = [T.grad(cost, param) for param in classifier.params] + + # specify how to update the parameters of the model as a list of + # (variable, update expression) pairs + + # given two lists of the same length, A = [a1, a2, a3, a4] and + # B = [b1, b2, b3, b4], zip generates a list C of same size, where each + # element is a pair formed from the two lists : + # C = [(a1, b1), (a2, b2), (a3, b3), (a4, b4)] + updates = [ + (param, param - learning_rate * gparam) + for param, gparam in zip(classifier.params, gparams) + ] + + # 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( + inputs=[index], + outputs=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-5 + + ############### + # TRAIN MODEL # + ############### + print '... training' + + # 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 = 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): + + minibatch_avg_cost = train_model(minibatch_index) + # iteration number + iter = (epoch - 1) * n_train_batches + 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) + + 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.)) + + if patience <= iter: + done_looping = True + break + + end_time = timeit.default_timer() + print(('Optimization complete. 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.)) + + +if __name__ == '__main__': + test_mlp() + +# Rectifier Linear Unit +#Source: http://stackoverflow.com/questions/26497564/theano-hiddenlayer-activation-function +def relu(x): + return T.maximum(0.,x)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/Code/genre_classification/classification/preprocess_spectrograms_7digital.py Sat Aug 15 19:16:17 2015 +0100 @@ -0,0 +1,33 @@ +# -*- coding: utf-8 -*- +""" +Created on Thu Jul 23 21:55:58 2015 + +@author: paulochiliguano +""" + + +import tables +import numpy as np +import cPickle +import sklearn.preprocessing as preprocessing + +#Read HDF5 file that contains log-mel spectrograms +filename = '/homes/pchilguano/msc_project/dataset/7digital/features/\ +feats.h5' +with tables.openFile(filename, 'r') as f: + features = f.root.x.read() + #filenames = f.root.filenames.read() + +#Pre-processing of spectrograms mean=0 and std=1 +n_per_example = np.prod(features.shape[1:-1]) +number_of_features = features.shape[-1] +flat_data = features.view() +flat_data.shape = (-1, number_of_features) +scaler = preprocessing.StandardScaler().fit(flat_data) +flat_data = scaler.transform(flat_data) +flat_data.shape = (features.shape[0], -1) + +f = file('/homes/pchilguano/msc_project/dataset/7digital/features/\ +feats.pkl', 'wb') +cPickle.dump(flat_data, f, protocol=cPickle.HIGHEST_PROTOCOL) +f.close()
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/Code/genre_classification/learning/convolutional_mlp.py Sat Aug 15 19:16:17 2015 +0100 @@ -0,0 +1,415 @@ +"""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 Chiliguano: Additional libraries +import cPickle +from theano.sandbox.rng_mrg import MRG_RandomStreams as RandomStreams + +# Paulo Chiliguano: 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(seed=234) + +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=0.1, n_epochs=200, + dataset='mnist.pkl.gz', + nkerns=[20, 50], batch_size=500): + """ 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/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]) + + # 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/msc_project/dataset/genre_classification/\ +best_params.pkl', 'wb') + cPickle.dump( + (best_params0, best_params1, best_params2, best_params3), + f, + protocol=cPickle.HIGHEST_PROTOCOL + ) + f.close() + +if __name__ == '__main__': + evaluate_lenet5( + learning_rate=0.01, + n_epochs=200, + dataset='/homes/pchilguano/msc_project/dataset/gtzan/features/\ +gtzan_3sec_2.pkl', + nkerns=[32, 32], + batch_size=10 + ) + +def experiment(state, channel): + evaluate_lenet5(state.learning_rate, dataset=state.dataset) +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/Code/genre_classification/learning/logistic_sgd.py Sat Aug 15 19:16:17 2015 +0100 @@ -0,0 +1,472 @@ +""" +This tutorial introduces logistic regression using Theano and stochastic +gradient descent. + +Logistic regression is a probabilistic, linear classifier. It is parametrized +by a weight matrix :math:`W` and a bias vector :math:`b`. Classification is +done by projecting data points onto a set of hyperplanes, the distance to +which is used to determine a class membership probability. + +Mathematically, this can be written as: + +.. math:: + P(Y=i|x, W,b) &= softmax_i(W x + b) \\ + &= \frac {e^{W_i x + b_i}} {\sum_j e^{W_j x + b_j}} + + +The output of the model or prediction is then done by taking the argmax of +the vector whose i'th element is P(Y=i|x). + +.. math:: + + y_{pred} = argmax_i P(Y=i|x,W,b) + + +This tutorial presents a stochastic gradient descent optimization method +suitable for large datasets. + + +References: + + - textbooks: "Pattern Recognition and Machine Learning" - + Christopher M. Bishop, section 4.3.2 + +""" +__docformat__ = 'restructedtext en' + +import cPickle +import gzip +import os +import sys +import timeit + +import numpy + +import theano +import theano.tensor as T + + +class LogisticRegression(object): + """Multi-class Logistic Regression Class + + The logistic regression is fully described by a weight matrix :math:`W` + and bias vector :math:`b`. Classification is done by projecting data + points onto a set of hyperplanes, the distance to which is used to + determine a class membership probability. + """ + + def __init__(self, input, n_in, n_out): + """ Initialize the parameters of the logistic regression + + :type input: theano.tensor.TensorType + :param input: symbolic variable that describes the input of the + architecture (one minibatch) + + :type n_in: int + :param n_in: number of input units, the dimension of the space in + which the datapoints lie + + :type n_out: int + :param n_out: number of output units, the dimension of the space in + which the labels lie + + """ + # start-snippet-1 + # 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 + ), + name='W', + borrow=True + ) + # initialize the baises b as a vector of n_out 0s + self.b = theano.shared( + value=numpy.zeros( + (n_out,), + dtype=theano.config.floatX + ), + name='b', + borrow=True + ) + + # symbolic expression for computing the matrix of class-membership + # probabilities + # Where: + # W is a matrix where column-k represent the separation hyperplane for + # class-k + # x is a matrix where row-j represents input training sample-j + # b is a vector where element-k represent the free parameter of + # hyperplane-k + self.p_y_given_x = T.nnet.softmax(T.dot(input, self.W) + self.b) + + # symbolic description of how to compute prediction as class whose + # probability is maximal + self.y_pred = T.argmax(self.p_y_given_x, axis=1) + # end-snippet-1 + + # parameters of the model + self.params = [self.W, self.b] + + # keep track of model input + self.input = input + + 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}) + + :type y: theano.tensor.TensorType + :param y: corresponds to a vector that gives for each example the + correct label + + Note: we use the mean instead of the sum so that + the learning rate is less dependent on the batch size + """ + # start-snippet-2 + # y.shape[0] is (symbolically) the number of rows in y, i.e., + # number of examples (call it n) in the minibatch + # T.arange(y.shape[0]) is a symbolic vector which will contain + # [0,1,2,... n-1] T.log(self.p_y_given_x) is a matrix of + # Log-Probabilities (call it LP) with one row per example and + # one column per class LP[T.arange(y.shape[0]),y] is a vector + # v containing [LP[0,y[0]], LP[1,y[1]], LP[2,y[2]], ..., + # LP[n-1,y[n-1]]] and T.mean(LP[T.arange(y.shape[0]),y]) is + # the mean (across minibatch examples) of the elements in v, + # i.e., the mean log-likelihood across the minibatch. + return -T.mean(T.log(self.p_y_given_x)[T.arange(y.shape[0]), y]) + # end-snippet-2 + + def errors(self, y): + """Return a float representing the number of errors in the minibatch + over the total number of examples of the minibatch ; zero one + loss over the size of the minibatch + + :type y: theano.tensor.TensorType + :param y: corresponds to a vector that gives for each example the + correct label + """ + + # 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', y.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 load_data(dataset): + ''' Loads the dataset + + :type dataset: string + :param dataset: the path to the dataset (here MNIST) + ''' + ############# + # LOAD DATA # + ############# + ''' + # Download the MNIST dataset if it is not present + data_dir, data_file = os.path.split(dataset) + if data_dir == "" and not os.path.isfile(dataset): + # Check if dataset is in the data directory. + new_path = os.path.join( + os.path.split(__file__)[0], + "..", + "data", + dataset + ) + if os.path.isfile(new_path) or data_file == 'mnist.pkl.gz': + dataset = new_path + + if (not os.path.isfile(dataset)) and data_file == 'mnist.pkl.gz': + import urllib + origin = ( + 'http://www.iro.umontreal.ca/~lisa/deep/data/mnist/mnist.pkl.gz' + ) + print 'Downloading data from %s' % origin + urllib.urlretrieve(origin, dataset) + + print '... loading data' + + # Load the dataset + f = gzip.open(dataset, 'rb') + ''' + f = file(dataset, 'rb') + train_set, valid_set, test_set = cPickle.load(f) + f.close() + #train_set, valid_set, test_set format: tuple(input, target) + #input is an numpy.ndarray of 2 dimensions (a matrix) + #witch row's correspond to an example. target is a + #numpy.ndarray of 1 dimensions (vector)) that have the same length as + #the number of rows in the input. It should give the target + #target to the example with the same index in the input. + + def shared_dataset(data_xy, borrow=True): + """ Function that loads the dataset into shared variables + + The reason we store our dataset in shared variables is to allow + Theano to copy it into the GPU memory (when code is run on GPU). + Since copying data into the GPU is slow, copying a minibatch everytime + is needed (the default behaviour if the data is not in a shared + variable) would lead to a large decrease in performance. + """ + data_x, data_y = data_xy + shared_x = theano.shared(numpy.asarray(data_x, + dtype=theano.config.floatX), + borrow=borrow) + shared_y = theano.shared(numpy.asarray(data_y, + dtype=theano.config.floatX), + borrow=borrow) + # When storing data on the GPU it has to be stored as floats + # therefore we will store the labels as ``floatX`` as well + # (``shared_y`` does exactly that). But during our computations + # we need them as ints (we use labels as index, and if they are + # floats it doesn't make sense) therefore instead of returning + # ``shared_y`` we will have to cast it to int. This little hack + # lets ous get around this issue + return shared_x, T.cast(shared_y, 'int32') + + test_set_x, test_set_y = shared_dataset(test_set) + valid_set_x, valid_set_y = shared_dataset(valid_set) + train_set_x, train_set_y = shared_dataset(train_set) + + rval = [(train_set_x, train_set_y), (valid_set_x, valid_set_y), + (test_set_x, test_set_y)] + return rval + + +def sgd_optimization_mnist(learning_rate=0.13, n_epochs=1000, + dataset='mnist.pkl.gz', + batch_size=600): + """ + Demonstrate stochastic gradient descent optimization of a log-linear + model + + This is demonstrated on MNIST. + + :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: the path of the MNIST dataset file from + http://www.iro.umontreal.ca/~lisa/deep/data/mnist/mnist.pkl.gz + + """ + 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] / batch_size + n_valid_batches = valid_set_x.get_value(borrow=True).shape[0] / batch_size + n_test_batches = test_set_x.get_value(borrow=True).shape[0] / batch_size + + ###################### + # BUILD ACTUAL MODEL # + ###################### + print '... building the model' + + # allocate symbolic variables for the data + index = T.lscalar() # index to a [mini]batch + + # generate symbolic variables for input (x and y represent a + # minibatch) + x = T.matrix('x') # data, presented as rasterized images + y = T.ivector('y') # labels, presented as 1D vector of [int] labels + + # construct the logistic regression class + # Each MNIST image has size 28*28 + classifier = LogisticRegression(input=x, n_in=28 * 28, n_out=10) + + # the cost we minimize during training is the negative log likelihood of + # the model in symbolic format + cost = classifier.negative_log_likelihood(y) + + # compiling a Theano function that computes the mistakes that are made by + # the model on a minibatch + test_model = theano.function( + inputs=[index], + outputs=classifier.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( + inputs=[index], + outputs=classifier.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] + } + ) + + # compute the gradient of cost with respect to theta = (W,b) + g_W = T.grad(cost=cost, wrt=classifier.W) + g_b = T.grad(cost=cost, wrt=classifier.b) + + # start-snippet-3 + # specify how to update the parameters of the model as a list of + # (variable, update expression) pairs. + updates = [(classifier.W, classifier.W - learning_rate * g_W), + (classifier.b, classifier.b - learning_rate * g_b)] + + # 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( + inputs=[index], + outputs=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-3 + + ############### + # TRAIN MODEL # + ############### + print '... training the model' + # early-stopping parameters + patience = 5000 # 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 + test_score = 0. + start_time = timeit.default_timer() + + done_looping = False + epoch = 0 + while (epoch < n_epochs) and (not done_looping): + epoch = epoch + 1 + for minibatch_index in xrange(n_train_batches): + + minibatch_avg_cost = train_model(minibatch_index) + # iteration number + iter = (epoch - 1) * n_train_batches + 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) + + best_validation_loss = this_validation_loss + # 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. + ) + ) + + # save the best model + with open('best_model.pkl', 'w') as f: + cPickle.dump(classifier, f) + + if patience <= iter: + done_looping = True + break + + end_time = timeit.default_timer() + print( + ( + 'Optimization complete with best validation score of %f %%,' + 'with test performance %f %%' + ) + % (best_validation_loss * 100., test_score * 100.) + ) + print 'The code run for %d epochs, with %f epochs/sec' % ( + epoch, 1. * epoch / (end_time - start_time)) + print >> sys.stderr, ('The code for file ' + + os.path.split(__file__)[1] + + ' ran for %.1fs' % ((end_time - start_time))) + + +def predict(): + """ + An example of how to load a trained model and use it + to predict labels. + """ + + # load the saved model + classifier = cPickle.load(open('best_model.pkl')) + + # compile a predictor function + predict_model = theano.function( + inputs=[classifier.input], + outputs=classifier.y_pred) + + # We can test it on some examples from test test + dataset='mnist.pkl.gz' + datasets = load_data(dataset) + test_set_x, test_set_y = datasets[2] + test_set_x = test_set_x.get_value() + + predicted_values = predict_model(test_set_x[:10]) + print ("Predicted values for the first 10 examples in test set:") + print predicted_values + + +if __name__ == '__main__': + sgd_optimization_mnist() +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/Code/genre_classification/learning/mlp.py Sat Aug 15 19:16:17 2015 +0100 @@ -0,0 +1,412 @@ +""" +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 +intermediate 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 + +""" +__docformat__ = 'restructedtext en' + + +import os +import sys +import timeit + +import numpy + +import theano +import theano.tensor as T + + +from logistic_sgd import LogisticRegression, load_data + + +# start-snippet-1 +class HiddenLayer(object): + def __init__(self, rng, input, n_in, n_out, W=None, b=None, + activation=T.tanh): + """ + Typical hidden layer of a MLP: units are fully-connected and have + sigmoidal activation function. Weight matrix W is of shape (n_in,n_out) + and the bias vector b is of shape (n_out,). + + NOTE : The nonlinearity used here is tanh + + Hidden unit activation is given by: tanh(dot(input,W) + b) + + :type rng: numpy.random.RandomState + :param rng: a random number generator used to initialize weights + + :type input: theano.tensor.dmatrix + :param input: a symbolic tensor of shape (n_examples, n_in) + + :type n_in: int + :param n_in: dimensionality of input + + :type n_out: int + :param n_out: number of hidden units + + :type activation: theano.Op or function + :param activation: Non linearity to be applied in the hidden + layer + """ + self.input = input + # end-snippet-1 + + # `W` is initialized with `W_values` which is uniformely sampled + # from sqrt(-6./(n_in+n_hidden)) and sqrt(6./(n_in+n_hidden)) + # for tanh activation function + # the output of uniform if converted using asarray to dtype + # theano.config.floatX so that the code is runable on GPU + # Note : optimal initialization of weights is dependent on the + # activation function used (among other things). + # For example, results presented in [Xavier10] suggest that you + # should use 4 times larger initial weights for sigmoid + # compared to tanh + # We have no info for other function, so we use the same as + # tanh. + if W is None: + 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 + ) + if activation == theano.tensor.nnet.sigmoid: + W_values *= 4 + + W = theano.shared(value=W_values, name='W', borrow=True) + + if b is None: + b_values = numpy.zeros((n_out,), dtype=theano.config.floatX) + b = theano.shared(value=b_values, name='b', borrow=True) + + self.W = W + self.b = b + + lin_output = T.dot(input, self.W) + self.b + self.output = ( + lin_output if activation is None + else activation(lin_output) + ) + # parameters of the model + self.params = [self.W, self.b] + + +# start-snippet-2 +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. + Intermediate layers usually have as activation function tanh or the + sigmoid function (defined here by a ``HiddenLayer`` class) while the + top layer is a softmax layer (defined here by a ``LogisticRegression`` + class). + """ + + def __init__(self, rng, input, n_in, n_hidden, n_out): + """Initialize the parameters for the multilayer perceptron + + :type rng: numpy.random.RandomState + :param rng: a random number generator used to initialize weights + + :type input: theano.tensor.TensorType + :param input: symbolic variable that describes the input of the + architecture (one minibatch) + + :type n_in: int + :param n_in: number of input units, the dimension of the space in + which the datapoints lie + + :type n_hidden: int + :param n_hidden: number of hidden units + + :type n_out: int + :param n_out: number of output units, the dimension of the space in + which the labels lie + + """ + + # Since we are dealing with a one hidden layer MLP, this will translate + # into a HiddenLayer with a tanh activation function connected to the + # LogisticRegression layer; the activation function can be replaced by + # sigmoid or any other nonlinear function + self.hiddenLayer = HiddenLayer( + rng=rng, + input=input, + n_in=n_in, + n_out=n_hidden, + activation=T.tanh + ) + + # The logistic regression layer gets as input the hidden units + # of the hidden layer + self.logRegressionLayer = LogisticRegression( + input=self.hiddenLayer.output, + n_in=n_hidden, + n_out=n_out + ) + # end-snippet-2 start-snippet-3 + # L1 norm ; one regularization option is to enforce L1 norm to + # be small + self.L1 = ( + abs(self.hiddenLayer.W).sum() + + abs(self.logRegressionLayer.W).sum() + ) + + # square of L2 norm ; one regularization option is to enforce + # square of L2 norm to be small + self.L2_sqr = ( + (self.hiddenLayer.W ** 2).sum() + + (self.logRegressionLayer.W ** 2).sum() + ) + + # negative log likelihood of the MLP is given by the negative + # log likelihood of the output of the model, computed in the + # logistic regression layer + self.negative_log_likelihood = ( + self.logRegressionLayer.negative_log_likelihood + ) + # same holds for the function computing the number of errors + self.errors = self.logRegressionLayer.errors + + # the parameters of the model are the parameters of the two layer it is + # made out of + self.params = self.hiddenLayer.params + self.logRegressionLayer.params + # end-snippet-3 + + # keep track of model input + self.input = input + + +def test_mlp(learning_rate=0.01, L1_reg=0.00, L2_reg=0.0001, n_epochs=1000, + dataset='mnist.pkl.gz', batch_size=20, n_hidden=500): + """ + Demonstrate stochastic gradient descent optimization for a multilayer + perceptron + + This is demonstrated on MNIST. + + :type learning_rate: float + :param learning_rate: learning rate used (factor for the stochastic + gradient + + :type L1_reg: float + :param L1_reg: L1-norm's weight when added to the cost (see + regularization) + + :type L2_reg: float + :param L2_reg: L2-norm's weight when added to the cost (see + regularization) + + :type n_epochs: int + :param n_epochs: maximal number of epochs to run the optimizer + + :type dataset: string + :param dataset: the path of the MNIST dataset file from + http://www.iro.umontreal.ca/~lisa/deep/data/mnist/mnist.pkl.gz + + + """ + 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] / batch_size + n_valid_batches = valid_set_x.get_value(borrow=True).shape[0] / batch_size + n_test_batches = test_set_x.get_value(borrow=True).shape[0] / batch_size + + ###################### + # BUILD ACTUAL MODEL # + ###################### + print '... building the model' + + # allocate symbolic variables for the data + index = T.lscalar() # index to a [mini]batch + 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 + + rng = numpy.random.RandomState(1234) + + # construct the MLP class + classifier = MLP( + rng=rng, + input=x, + n_in=28 * 28, + n_hidden=n_hidden, + n_out=10 + ) + + # start-snippet-4 + # 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 + ) + # end-snippet-4 + + # compiling a Theano function that computes the mistakes that are made + # by the model on a minibatch + test_model = theano.function( + inputs=[index], + outputs=classifier.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( + inputs=[index], + outputs=classifier.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] + } + ) + + # start-snippet-5 + # compute the gradient of cost with respect to theta (sotred in params) + # the resulting gradients will be stored in a list gparams + gparams = [T.grad(cost, param) for param in classifier.params] + + # specify how to update the parameters of the model as a list of + # (variable, update expression) pairs + + # given two lists of the same length, A = [a1, a2, a3, a4] and + # B = [b1, b2, b3, b4], zip generates a list C of same size, where each + # element is a pair formed from the two lists : + # C = [(a1, b1), (a2, b2), (a3, b3), (a4, b4)] + updates = [ + (param, param - learning_rate * gparam) + for param, gparam in zip(classifier.params, gparams) + ] + + # 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( + inputs=[index], + outputs=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-5 + + ############### + # TRAIN MODEL # + ############### + print '... training' + + # 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 = 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): + + minibatch_avg_cost = train_model(minibatch_index) + # iteration number + iter = (epoch - 1) * n_train_batches + 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) + + 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.)) + + if patience <= iter: + done_looping = True + break + + end_time = timeit.default_timer() + print(('Optimization complete. 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.)) + + +if __name__ == '__main__': + test_mlp() + +# Rectifier Linear Unit +#Source: http://stackoverflow.com/questions/26497564/theano-hiddenlayer-activation-function +def relu(x): + return T.maximum(0.,x)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/Code/genre_classification/learning/preprocess_spectrograms_gtzan.py Sat Aug 15 19:16:17 2015 +0100 @@ -0,0 +1,83 @@ +# -*- coding: utf-8 -*- +""" +Created on Thu Jul 23 21:55:58 2015 + +@author: paulochiliguano +""" + + +import tables +import numpy as np +import cPickle +import sklearn.preprocessing as preprocessing + +#Read HDF5 file that contains log-mel spectrograms +filename = '/homes/pchilguano/msc_project/dataset/gtzan/features/\ +feats_3sec_9.h5' +with tables.openFile(filename, 'r') as f: + features = f.root.x.read() + #filenames = f.root.filenames.read() + +#Pre-processing of spectrograms mean=0 and std=1 +#initial_shape = features.shape[1:] +n_per_example = np.prod(features.shape[1:-1]) +number_of_features = features.shape[-1] +flat_data = features.view() +flat_data.shape = (-1, number_of_features) +scaler = preprocessing.StandardScaler().fit(flat_data) +flat_data = scaler.transform(flat_data) +flat_data.shape = (features.shape[0], -1) +#flat_targets = filenames.repeat(n_per_example) +#genre = np.asarray([line.strip().split('\t')[1] for line in open(filename,'r').readlines()]) + +#Read labels from ground truth +filename = '/homes/pchilguano/msc_project/dataset/gtzan/lists/ground_truth.txt' +with open(filename, 'r') as f: + tag_set = set() + for line in f: + tag = line.strip().split('\t')[1] + tag_set.add(tag) + +#Assign label to a discrete number +tag_dict = dict([(item, index) for index, item in enumerate(sorted(tag_set))]) +with open(filename, 'r') as f: + target = np.asarray([], dtype='int32') + mp3_dict = {} + for line in f: + tag = line.strip().split('\t')[1] + target = np.append(target, tag_dict[tag]) + +train_input, valid_input, test_input = np.array_split( + flat_data, + [flat_data.shape[0]*1/2, + flat_data.shape[0]*3/4] +) +train_target, valid_target, test_target = np.array_split( + target, + [target.shape[0]*1/2, + target.shape[0]*3/4] +) + +f = file('/homes/pchilguano/msc_project/dataset/gtzan/features/\ +gtzan_3sec_9.pkl', 'wb') +cPickle.dump( + ( + (train_input, train_target), + (valid_input, valid_target), + (test_input, test_target) + ), + f, + protocol=cPickle.HIGHEST_PROTOCOL +) +f.close() + +''' +flat_target = target.repeat(n_per_example) + +train_input, valid_input, test_input = np.array_split(flat_data, [flat_data.shape[0]*4/5, flat_data.shape[0]*9/10]) +train_target, valid_target, test_target = np.array_split(flat_target, [flat_target.shape[0]*4/5, flat_target.shape[0]*9/10]) + +f = file('/homes/pchilguano/deep_learning/gtzan_logistic.pkl', 'wb') +cPickle.dump(((train_input, train_target), (valid_input, valid_target), (test_input, test_target)), f, protocol=cPickle.HIGHEST_PROTOCOL) +f.close() +'''
--- a/Code/logistic_sgd.py Tue Aug 11 14:23:42 2015 +0100 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,480 +0,0 @@ -""" -This tutorial introduces logistic regression using Theano and stochastic -gradient descent. - -Logistic regression is a probabilistic, linear classifier. It is parametrized -by a weight matrix :math:`W` and a bias vector :math:`b`. Classification is -done by projecting data points onto a set of hyperplanes, the distance to -which is used to determine a class membership probability. - -Mathematically, this can be written as: - -.. math:: - P(Y=i|x, W,b) &= softmax_i(W x + b) \\ - &= \frac {e^{W_i x + b_i}} {\sum_j e^{W_j x + b_j}} - - -The output of the model or prediction is then done by taking the argmax of -the vector whose i'th element is P(Y=i|x). - -.. math:: - - y_{pred} = argmax_i P(Y=i|x,W,b) - - -This tutorial presents a stochastic gradient descent optimization method -suitable for large datasets. - - -References: - - - textbooks: "Pattern Recognition and Machine Learning" - - Christopher M. Bishop, section 4.3.2 - -""" -__docformat__ = 'restructedtext en' - -import cPickle -import gzip -import os -import sys -import timeit - -import numpy - -import theano -import theano.tensor as T - - -class LogisticRegression(object): - """Multi-class Logistic Regression Class - - The logistic regression is fully described by a weight matrix :math:`W` - and bias vector :math:`b`. Classification is done by projecting data - points onto a set of hyperplanes, the distance to which is used to - determine a class membership probability. - """ - - def __init__(self, input, n_in, n_out): - """ Initialize the parameters of the logistic regression - - :type input: theano.tensor.TensorType - :param input: symbolic variable that describes the input of the - architecture (one minibatch) - - :type n_in: int - :param n_in: number of input units, the dimension of the space in - which the datapoints lie - - :type n_out: int - :param n_out: number of output units, the dimension of the space in - which the labels lie - - """ - # start-snippet-1 - # 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 - ), - name='W', - borrow=True - ) - # initialize the baises b as a vector of n_out 0s - self.b = theano.shared( - value=numpy.zeros( - (n_out,), - dtype=theano.config.floatX - ), - name='b', - borrow=True - ) - - # symbolic expression for computing the matrix of class-membership - # probabilities - # Where: - # W is a matrix where column-k represent the separation hyperplane for - # class-k - # x is a matrix where row-j represents input training sample-j - # b is a vector where element-k represent the free parameter of - # hyperplane-k - self.p_y_given_x = T.nnet.softmax(T.dot(input, self.W) + self.b) - #self.p_y_given_x = relu(T.dot(input, self.W) + self.b) - - # symbolic description of how to compute prediction as class whose - # probability is maximal - self.y_pred = T.argmax(self.p_y_given_x, axis=1) - # end-snippet-1 - - # parameters of the model - self.params = [self.W, self.b] - - # keep track of model input - self.input = input - - 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}) - - :type y: theano.tensor.TensorType - :param y: corresponds to a vector that gives for each example the - correct label - - Note: we use the mean instead of the sum so that - the learning rate is less dependent on the batch size - """ - # start-snippet-2 - # y.shape[0] is (symbolically) the number of rows in y, i.e., - # number of examples (call it n) in the minibatch - # T.arange(y.shape[0]) is a symbolic vector which will contain - # [0,1,2,... n-1] T.log(self.p_y_given_x) is a matrix of - # Log-Probabilities (call it LP) with one row per example and - # one column per class LP[T.arange(y.shape[0]),y] is a vector - # v containing [LP[0,y[0]], LP[1,y[1]], LP[2,y[2]], ..., - # LP[n-1,y[n-1]]] and T.mean(LP[T.arange(y.shape[0]),y]) is - # the mean (across minibatch examples) of the elements in v, - # i.e., the mean log-likelihood across the minibatch. - return -T.mean(T.log(self.p_y_given_x)[T.arange(y.shape[0]), y]) - # end-snippet-2 - - def errors(self, y): - """Return a float representing the number of errors in the minibatch - over the total number of examples of the minibatch ; zero one - loss over the size of the minibatch - - :type y: theano.tensor.TensorType - :param y: corresponds to a vector that gives for each example the - correct label - """ - - # 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', y.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 load_data(dataset): - ''' Loads the dataset - - :type dataset: string - :param dataset: the path to the dataset (here MNIST) - ''' - - ############# - # LOAD DATA # - ############# - - # Download the MNIST dataset if it is not present - '''data_dir, data_file = os.path.split(dataset) - if data_dir == "" and not os.path.isfile(dataset): - # Check if dataset is in the data directory. - new_path = os.path.join( - os.path.split(__file__)[0], - "..", - "data", - dataset - ) - if os.path.isfile(new_path) or data_file == 'mnist.pkl.gz': - dataset = new_path - - if (not os.path.isfile(dataset)) and data_file == 'mnist.pkl.gz': - import urllib - origin = ( - 'http://www.iro.umontreal.ca/~lisa/deep/data/mnist/mnist.pkl.gz' - ) - print 'Downloading data from %s' % origin - urllib.urlretrieve(origin, dataset) - - print '... loading data' - - # Load the dataset - f = gzip.open(dataset, 'rb') - train_set, valid_set, test_set = cPickle.load(f) - f.close()''' - f = file('/homes/pchilguano/deep_learning/features/gtzan_3sec_1.pkl', 'rb') - train_set, valid_set, test_set = cPickle.load(f) - f.close() - #train_set, valid_set, test_set format: tuple(input, target) - #input is an numpy.ndarray of 2 dimensions (a matrix) - #witch row's correspond to an example. target is a - #numpy.ndarray of 1 dimensions (vector)) that have the same length as - #the number of rows in the input. It should give the target - #target to the example with the same index in the input. - - def shared_dataset(data_xy, borrow=True): - """ Function that loads the dataset into shared variables - - The reason we store our dataset in shared variables is to allow - Theano to copy it into the GPU memory (when code is run on GPU). - Since copying data into the GPU is slow, copying a minibatch everytime - is needed (the default behaviour if the data is not in a shared - variable) would lead to a large decrease in performance. - """ - data_x, data_y = data_xy - shared_x = theano.shared(numpy.asarray(data_x, - dtype=theano.config.floatX), - borrow=borrow) - shared_y = theano.shared(numpy.asarray(data_y, - dtype=theano.config.floatX), - borrow=borrow) - # When storing data on the GPU it has to be stored as floats - # therefore we will store the labels as ``floatX`` as well - # (``shared_y`` does exactly that). But during our computations - # we need them as ints (we use labels as index, and if they are - # floats it doesn't make sense) therefore instead of returning - # ``shared_y`` we will have to cast it to int. This little hack - # lets ous get around this issue - return shared_x, T.cast(shared_y, 'int32') - - test_set_x, test_set_y = shared_dataset(test_set) - valid_set_x, valid_set_y = shared_dataset(valid_set) - train_set_x, train_set_y = shared_dataset(train_set) - - rval = [(train_set_x, train_set_y), (valid_set_x, valid_set_y), - (test_set_x, test_set_y)] - return rval - - -def sgd_optimization_mnist(learning_rate=0.13, n_epochs=1000, - dataset='mnist.pkl.gz', - batch_size=600): - """ - Demonstrate stochastic gradient descent optimization of a log-linear - model - - This is demonstrated on MNIST. - - :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: the path of the MNIST dataset file from - http://www.iro.umontreal.ca/~lisa/deep/data/mnist/mnist.pkl.gz - - """ - 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] / batch_size - n_valid_batches = valid_set_x.get_value(borrow=True).shape[0] / batch_size - n_test_batches = test_set_x.get_value(borrow=True).shape[0] / batch_size - - ###################### - # BUILD ACTUAL MODEL # - ###################### - print '... building the model' - - # allocate symbolic variables for the data - index = T.lscalar() # index to a [mini]batch - - # generate symbolic variables for input (x and y represent a - # minibatch) - x = T.matrix('x') # data, presented as rasterized images - y = T.ivector('y') # labels, presented as 1D vector of [int] labels - - # construct the logistic regression class - # Each MNIST image has size 28*28 - classifier = LogisticRegression(input=x, n_in=28 * 28, n_out=10) - - # the cost we minimize during training is the negative log likelihood of - # the model in symbolic format - cost = classifier.negative_log_likelihood(y) - - # compiling a Theano function that computes the mistakes that are made by - # the model on a minibatch - test_model = theano.function( - inputs=[index], - outputs=classifier.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( - inputs=[index], - outputs=classifier.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] - } - ) - - # compute the gradient of cost with respect to theta = (W,b) - g_W = T.grad(cost=cost, wrt=classifier.W) - g_b = T.grad(cost=cost, wrt=classifier.b) - - # start-snippet-3 - # specify how to update the parameters of the model as a list of - # (variable, update expression) pairs. - updates = [(classifier.W, classifier.W - learning_rate * g_W), - (classifier.b, classifier.b - learning_rate * g_b)] - - # 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( - inputs=[index], - outputs=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-3 - - ############### - # TRAIN MODEL # - ############### - print '... training the model' - # early-stopping parameters - patience = 5000 # 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 - test_score = 0. - start_time = timeit.default_timer() - - done_looping = False - epoch = 0 - while (epoch < n_epochs) and (not done_looping): - epoch = epoch + 1 - for minibatch_index in xrange(n_train_batches): - - minibatch_avg_cost = train_model(minibatch_index) - # iteration number - iter = (epoch - 1) * n_train_batches + 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) - - best_validation_loss = this_validation_loss - # 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. - ) - ) - - # save the best model - with open('best_model.pkl', 'w') as f: - cPickle.dump(classifier, f) - - if patience <= iter: - done_looping = True - break - - end_time = timeit.default_timer() - print( - ( - 'Optimization complete with best validation score of %f %%,' - 'with test performance %f %%' - ) - % (best_validation_loss * 100., test_score * 100.) - ) - print 'The code run for %d epochs, with %f epochs/sec' % ( - epoch, 1. * epoch / (end_time - start_time)) - print >> sys.stderr, ('The code for file ' + - os.path.split(__file__)[1] + - ' ran for %.1fs' % ((end_time - start_time))) - - -def predict(): - """ - An example of how to load a trained model and use it - to predict labels. - """ - - # load the saved model - classifier = cPickle.load(open('best_model.pkl')) - - # compile a predictor function - predict_model = theano.function( - inputs=[classifier.input], - outputs=classifier.y_pred) - - # We can test it on some examples from test test - dataset='mnist.pkl.gz' - datasets = load_data(dataset) - test_set_x, test_set_y = datasets[2] - test_set_x = test_set_x.get_value() - - predicted_values = predict_model(test_set_x[:10]) - print ("Predicted values for the first 10 examples in test set:") - print predicted_values - - -if __name__ == '__main__': - sgd_optimization_mnist() - - -# Rectifier Linear Unit -#Source: http://stackoverflow.com/questions/26497564/theano-hiddenlayer-activation-function -def relu(x): - return T.maximum(0.,x)
--- a/Code/make_lists.py Tue Aug 11 14:23:42 2015 +0100 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,149 +0,0 @@ - -import numpy -import numpy.random as random -import os -import pickle -import sys -import utils as U -#import pdb - - -def read_file(filename): - """ - Loads a file into a list - """ - file_list=[l.strip() for l in open(filename,'r').readlines()] - return file_list - -def get_folds(filelist, n_folds): - n_per_fold = len(filelist) / n_folds - folds = [] - for i in range(n_folds-1): - folds.append(filelist[i * n_per_fold: (i + 1) * n_per_fold]) - i = n_folds - 1 - folds.append(filelist[i * n_per_fold:]) - return folds - -def generate_mirex_list(train_list, annotations): - out_list = [] - for song in train_list: - annot = annotations.get(song,None) - if annot is None: - print 'No annotations for song %s' % song - continue - assert(type('') == type(annot)) - out_list.append('%s\t%s\n' % (song,annot)) - - return out_list - - -def make_file_list(gtzan_path, n_folds=5,): - """ - Generates lists - """ - audio_path = os.path.join(gtzan_path,'audio') - out_path = os.path.join(gtzan_path,'lists') - files_list = [] - for ext in ['.au', '.mp3', '.wav']: - files = U.getFiles(audio_path, ext) - files_list.extend(files) - random.shuffle(files_list) - - if not os.path.exists(out_path): - os.makedirs(out_path) - - audio_list_path = os.path.join(out_path, 'audio_files.txt') - open(audio_list_path,'w').writelines(['%s\n' % f for f in files_list]) - - annotations = get_annotations(files_list) - - ground_truth_path = os.path.join(out_path, 'ground_truth.txt') - open(ground_truth_path,'w').writelines(generate_mirex_list(files_list, annotations)) - generate_ground_truth_pickle(ground_truth_path) - - folds = get_folds(files_list, n_folds=n_folds) - - ### Single fold for quick experiments - create_fold(0, 1, folds, annotations, out_path) - - for n in range(n_folds): - create_fold(n, n_folds, folds, annotations, out_path) - - -def create_fold(n, n_folds, folds, annotations, out_path): - train_path = os.path.join(out_path, 'train_%i_of_%i.txt' % (n+1, n_folds)) - valid_path = os.path.join(out_path, 'valid_%i_of_%i.txt' % (n+1, n_folds)) - test_path = os.path.join(out_path, 'test_%i_of_%i.txt' % (n+1, n_folds)) - - test_list = folds[n] - train_list = [] - for m in range(len(folds)): - if m != n: - train_list.extend(folds[m]) - - open(train_path,'w').writelines(generate_mirex_list(train_list, annotations)) - open(test_path,'w').writelines(generate_mirex_list(test_list, annotations)) - split_list_file(train_path, train_path, valid_path, ratio=0.8) - -def split_list_file(input_file, out_file1, out_file2, ratio=0.8): - input_list = open(input_file,'r').readlines() - - n = len(input_list) - nsplit = int(n *ratio) - - list1 = input_list[:nsplit] - list2 = input_list[nsplit:] - - open(out_file1, 'w').writelines(list1) - open(out_file2, 'w').writelines(list2) - - -def get_annotation(filename): - genre = os.path.split(U.parseFile(filename)[0])[-1] - return genre - -def get_annotations(files_list): - annotations = {} - for filename in files_list: - annotations[filename] = get_annotation(filename) - - return annotations - -def generate_ground_truth_pickle(gt_file): - gt_path,_ = os.path.split(gt_file) - tag_file = os.path.join(gt_path,'tags.txt') - gt_pickle = os.path.join(gt_path,'ground_truth.pickle') - - lines = open(gt_file,'r').readlines() - - tag_set = set() - for line in lines: - filename,tag = line.strip().split('\t') - tag_set.add(tag) - tag_list = sorted(list(tag_set)) - open(tag_file,'w').writelines('\n'.join(tag_list + [''])) - - tag_dict = dict([(tag,i) for i,tag in enumerate(tag_list)]) - n_tags = len(tag_dict) - - mp3_dict = {} - for line in lines: - filename,tag = line.strip().split('\t') - tag_vector = mp3_dict.get(filename,numpy.zeros(n_tags)) - if tag != '': - tag_vector[tag_dict[tag]] = 1. - mp3_dict[filename] = tag_vector - pickle.dump(mp3_dict,open(gt_pickle,'w')) - -if __name__ == '__main__': - if len(sys.argv) < 2: - print 'Usage: python %s gtzan_path [n_folds=10]' % sys.argv[0] - sys.exit() - - gtzan_path = os.path.abspath(sys.argv[1]) - if len(sys.argv) > 2: - n_folds = int(sys.argv[2]) - else: - n_folds = 10 - - make_file_list(gtzan_path, n_folds) \ No newline at end of file
--- a/Code/mlp.py Tue Aug 11 14:23:42 2015 +0100 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,412 +0,0 @@ -""" -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 -intermediate 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 - -""" -__docformat__ = 'restructedtext en' - - -import os -import sys -import timeit - -import numpy - -import theano -import theano.tensor as T - - -from logistic_sgd import LogisticRegression, load_data - - -# start-snippet-1 -class HiddenLayer(object): - def __init__(self, rng, input, n_in, n_out, W=None, b=None, - activation=T.tanh): - """ - Typical hidden layer of a MLP: units are fully-connected and have - sigmoidal activation function. Weight matrix W is of shape (n_in,n_out) - and the bias vector b is of shape (n_out,). - - NOTE : The nonlinearity used here is tanh - - Hidden unit activation is given by: tanh(dot(input,W) + b) - - :type rng: numpy.random.RandomState - :param rng: a random number generator used to initialize weights - - :type input: theano.tensor.dmatrix - :param input: a symbolic tensor of shape (n_examples, n_in) - - :type n_in: int - :param n_in: dimensionality of input - - :type n_out: int - :param n_out: number of hidden units - - :type activation: theano.Op or function - :param activation: Non linearity to be applied in the hidden - layer - """ - self.input = input - # end-snippet-1 - - # `W` is initialized with `W_values` which is uniformely sampled - # from sqrt(-6./(n_in+n_hidden)) and sqrt(6./(n_in+n_hidden)) - # for tanh activation function - # the output of uniform if converted using asarray to dtype - # theano.config.floatX so that the code is runable on GPU - # Note : optimal initialization of weights is dependent on the - # activation function used (among other things). - # For example, results presented in [Xavier10] suggest that you - # should use 4 times larger initial weights for sigmoid - # compared to tanh - # We have no info for other function, so we use the same as - # tanh. - if W is None: - 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 - ) - if activation == theano.tensor.nnet.sigmoid: - W_values *= 4 - - W = theano.shared(value=W_values, name='W', borrow=True) - - if b is None: - b_values = numpy.zeros((n_out,), dtype=theano.config.floatX) - b = theano.shared(value=b_values, name='b', borrow=True) - - self.W = W - self.b = b - - lin_output = T.dot(input, self.W) + self.b - self.output = ( - lin_output if activation is None - else activation(lin_output) - ) - # parameters of the model - self.params = [self.W, self.b] - - -# start-snippet-2 -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. - Intermediate layers usually have as activation function tanh or the - sigmoid function (defined here by a ``HiddenLayer`` class) while the - top layer is a softmax layer (defined here by a ``LogisticRegression`` - class). - """ - - def __init__(self, rng, input, n_in, n_hidden, n_out): - """Initialize the parameters for the multilayer perceptron - - :type rng: numpy.random.RandomState - :param rng: a random number generator used to initialize weights - - :type input: theano.tensor.TensorType - :param input: symbolic variable that describes the input of the - architecture (one minibatch) - - :type n_in: int - :param n_in: number of input units, the dimension of the space in - which the datapoints lie - - :type n_hidden: int - :param n_hidden: number of hidden units - - :type n_out: int - :param n_out: number of output units, the dimension of the space in - which the labels lie - - """ - - # Since we are dealing with a one hidden layer MLP, this will translate - # into a HiddenLayer with a tanh activation function connected to the - # LogisticRegression layer; the activation function can be replaced by - # sigmoid or any other nonlinear function - self.hiddenLayer = HiddenLayer( - rng=rng, - input=input, - n_in=n_in, - n_out=n_hidden, - activation=T.tanh - ) - - # The logistic regression layer gets as input the hidden units - # of the hidden layer - self.logRegressionLayer = LogisticRegression( - input=self.hiddenLayer.output, - n_in=n_hidden, - n_out=n_out - ) - # end-snippet-2 start-snippet-3 - # L1 norm ; one regularization option is to enforce L1 norm to - # be small - self.L1 = ( - abs(self.hiddenLayer.W).sum() - + abs(self.logRegressionLayer.W).sum() - ) - - # square of L2 norm ; one regularization option is to enforce - # square of L2 norm to be small - self.L2_sqr = ( - (self.hiddenLayer.W ** 2).sum() - + (self.logRegressionLayer.W ** 2).sum() - ) - - # negative log likelihood of the MLP is given by the negative - # log likelihood of the output of the model, computed in the - # logistic regression layer - self.negative_log_likelihood = ( - self.logRegressionLayer.negative_log_likelihood - ) - # same holds for the function computing the number of errors - self.errors = self.logRegressionLayer.errors - - # the parameters of the model are the parameters of the two layer it is - # made out of - self.params = self.hiddenLayer.params + self.logRegressionLayer.params - # end-snippet-3 - - # keep track of model input - self.input = input - - -def test_mlp(learning_rate=0.01, L1_reg=0.00, L2_reg=0.0001, n_epochs=1000, - dataset='mnist.pkl.gz', batch_size=20, n_hidden=500): - """ - Demonstrate stochastic gradient descent optimization for a multilayer - perceptron - - This is demonstrated on MNIST. - - :type learning_rate: float - :param learning_rate: learning rate used (factor for the stochastic - gradient - - :type L1_reg: float - :param L1_reg: L1-norm's weight when added to the cost (see - regularization) - - :type L2_reg: float - :param L2_reg: L2-norm's weight when added to the cost (see - regularization) - - :type n_epochs: int - :param n_epochs: maximal number of epochs to run the optimizer - - :type dataset: string - :param dataset: the path of the MNIST dataset file from - http://www.iro.umontreal.ca/~lisa/deep/data/mnist/mnist.pkl.gz - - - """ - 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] / batch_size - n_valid_batches = valid_set_x.get_value(borrow=True).shape[0] / batch_size - n_test_batches = test_set_x.get_value(borrow=True).shape[0] / batch_size - - ###################### - # BUILD ACTUAL MODEL # - ###################### - print '... building the model' - - # allocate symbolic variables for the data - index = T.lscalar() # index to a [mini]batch - 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 - - rng = numpy.random.RandomState(1234) - - # construct the MLP class - classifier = MLP( - rng=rng, - input=x, - n_in=28 * 28, - n_hidden=n_hidden, - n_out=10 - ) - - # start-snippet-4 - # 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 - ) - # end-snippet-4 - - # compiling a Theano function that computes the mistakes that are made - # by the model on a minibatch - test_model = theano.function( - inputs=[index], - outputs=classifier.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( - inputs=[index], - outputs=classifier.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] - } - ) - - # start-snippet-5 - # compute the gradient of cost with respect to theta (sotred in params) - # the resulting gradients will be stored in a list gparams - gparams = [T.grad(cost, param) for param in classifier.params] - - # specify how to update the parameters of the model as a list of - # (variable, update expression) pairs - - # given two lists of the same length, A = [a1, a2, a3, a4] and - # B = [b1, b2, b3, b4], zip generates a list C of same size, where each - # element is a pair formed from the two lists : - # C = [(a1, b1), (a2, b2), (a3, b3), (a4, b4)] - updates = [ - (param, param - learning_rate * gparam) - for param, gparam in zip(classifier.params, gparams) - ] - - # 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( - inputs=[index], - outputs=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-5 - - ############### - # TRAIN MODEL # - ############### - print '... training' - - # 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 = 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): - - minibatch_avg_cost = train_model(minibatch_index) - # iteration number - iter = (epoch - 1) * n_train_batches + 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) - - 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.)) - - if patience <= iter: - done_looping = True - break - - end_time = timeit.default_timer() - print(('Optimization complete. 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.)) - - -if __name__ == '__main__': - test_mlp() - -# Rectifier Linear Unit -#Source: http://stackoverflow.com/questions/26497564/theano-hiddenlayer-activation-function -def relu(x): - return T.maximum(0.,x)
--- a/Code/prepare_dataset.py Tue Aug 11 14:23:42 2015 +0100 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,72 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Created on Thu Jul 23 21:55:58 2015 - -@author: paulochiliguano -""" - - -import tables -import numpy as np -import cPickle -import sklearn.preprocessing as preprocessing - -''' -Read HDF5 file that contains log-mel spectrograms -''' -filename = '/homes/pchilguano/deep_learning/features/feats.h5' -with tables.openFile(filename, 'r') as f: - features = f.root.x.read() - #filenames = f.root.filenames.read() - -''' -Pre-processing of spectrograms mean=0 and std=1 -''' -#initial_shape = features.shape[1:] -n_per_example = np.prod(features.shape[1:-1]) -number_of_features = features.shape[-1] -flat_data = features.view() -flat_data.shape = (-1, number_of_features) -scaler = preprocessing.StandardScaler().fit(flat_data) -flat_data = scaler.transform(flat_data) -flat_data.shape = (features.shape[0], -1) -#flat_targets = filenames.repeat(n_per_example) - -#genre = np.asarray([line.strip().split('\t')[1] for line in open(filename,'r').readlines()]) - -''' -Read labels from ground truth -''' -filename = '/homes/pchilguano/deep_learning/lists/ground_truth.txt' -with open(filename, 'r') as f: - tag_set = set() - for line in f: - tag = line.strip().split('\t')[1] - tag_set.add(tag) -''' -Assign label to a discrete number -''' -tag_dict = dict([(item, index) for index, item in enumerate(sorted(tag_set))]) -with open(filename, 'r') as f: - target = np.asarray([], dtype='int32') - mp3_dict = {} - for line in f: - tag = line.strip().split('\t')[1] - target = np.append(target, tag_dict[tag]) - -train_input, valid_input, test_input = np.array_split(flat_data, [flat_data.shape[0]*1/2, flat_data.shape[0]*3/4]) -train_target, valid_target, test_target = np.array_split(target, [target.shape[0]*1/2, target.shape[0]*3/4]) - -f = file('/homes/pchilguano/deep_learning/gtzan.pkl', 'wb') -cPickle.dump(((train_input, train_target), (valid_input, valid_target), (test_input, test_target)), f, protocol=cPickle.HIGHEST_PROTOCOL) -f.close() -''' -flat_target = target.repeat(n_per_example) - -train_input, valid_input, test_input = np.array_split(flat_data, [flat_data.shape[0]*4/5, flat_data.shape[0]*9/10]) -train_target, valid_target, test_target = np.array_split(flat_target, [flat_target.shape[0]*4/5, flat_target.shape[0]*9/10]) - -f = file('/homes/pchilguano/deep_learning/gtzan_logistic.pkl', 'wb') -cPickle.dump(((train_input, train_target), (valid_input, valid_target), (test_input, test_target)), f, protocol=cPickle.HIGHEST_PROTOCOL) -f.close() -''' \ No newline at end of file
--- a/Code/preview_clip.py Tue Aug 11 14:23:42 2015 +0100 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,96 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Created on Wed Jul 15 00:41:44 2015 - -@author: paulochiliguano -""" - - -import csv -import time -from pyechonest import song, config #http://echonest.github.io/pyechonest/ -import oauth2 as oauth #https://github.com/jasonrubenstein/python_oauth2 -import urllib2 -import os - -# 7digital keys -consumer_key = '7ds28qendsk9' -consumer_secret = 'm5nsktn3hu6x45cy' -consumer = oauth.Consumer(consumer_key, consumer_secret) - -# EchoNest key -config.ECHO_NEST_API_KEY="LINDFDUTQZQ781IE8" - -# Retrieve audio clips -mp3_folder = '/Users/paulochiliguano/Documents/msc-project/Dataset/clips/' -filename_echonest = '/Users/paulochiliguano/Documents/msc-project/Dataset/\ -CF_dataset_songID.txt' -filename_7digital = '/Users/paulochiliguano/Documents/msc-project/Dataset/\ -CF_dataset_metadata.txt' -with open(filename_echonest, 'rb') as f, open(filename_7digital, 'wb') as out: - writer = csv.writer(out, delimiter='\t') - for i in xrange(1218): - f.readline() - next = f.readline() - while next != "": - try: - s = song.Song(next) - #s = song.Song('SOPEXHZ12873FD2AC7') - #except: - except IndexError: - time.sleep(3) - print "%s not available" % next[:-1] - next = f.readline() - else: - time.sleep(3) - try: - ss_tracks = s.get_tracks('7digital-UK') - except: - time.sleep(3) - print "%s not in UK catalog" % next[:-1] - next = f.readline() - else: - #print(len(ss_tracks)) - if len(ss_tracks) != 0: - ss_track = ss_tracks[0] - preview_url = ss_track.get('preview_url') - track_id = ss_track.get('id') - - req = oauth.Request( - method="GET", - url=preview_url, - is_form_encoded=True - ) - req['oauth_timestamp'] = oauth.Request.make_timestamp() - req['oauth_nonce'] = oauth.Request.make_nonce() - req['country'] = "GB" - sig_method = oauth.SignatureMethod_HMAC_SHA1() - req.sign_request(sig_method, consumer, token=None) - - try: - response = urllib2.urlopen(req.to_url()) - except: - #time.sleep(16) - print "No available preview for %s" % next[:-1] - #writer.writerow([next[:-2], 'NA', s.artist_name.encode("utf-8"), s.title.encode("utf-8")]) - else: - print([ - next[:-1], - track_id, - s.artist_name, - s.title, - preview_url - ]) - writer.writerow([ - next[:-1], - track_id, - s.artist_name.encode("utf-8"), - s.title.encode("utf-8"), - preview_url - ]) - mp3_file = os.path.join(mp3_folder, next[:-1]+'.mp3') - with open(mp3_file, 'wb') as songfile: - songfile.write(response.read()) - time.sleep(16) - next = f.readline() - \ No newline at end of file
--- a/Code/read_songID.py Tue Aug 11 14:23:42 2015 +0100 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,100 +0,0 @@ - - - - - - - - -import time -import pandas as pd - -# Read songIDs from Million Song Dataset songID-trackID mismatches -start_time = time.time() -print 'Reading songID mismatches...' -filename = '/Users/paulochiliguano/Documents/msc-project/Dataset/\ -sid_mismatches.txt' -with open(filename, 'rb') as f: - mismatches = set() - next = f.readline() - while next != "": - songID = next[8:26] - mismatches.add(songID) - #print(next[8:26]) - next = f.readline() - -# Delete rows with songIDs mismatches from Taste Profile Subset -print 'Reading Taste Profile subset...' -result = pd.DataFrame() -filename = '/Users/paulochiliguano/Documents/msc-project/Dataset/\ -train_triplets.txt' -filename_out = '/Users/paulochiliguano/Documents/msc-project/Dataset/\ -train_triplets_wo_mismatches.csv' -for chunk in pd.read_csv( - filename, - low_memory=False, - delim_whitespace=True, - chunksize=20000, - names=['user', 'song', 'plays'], - header=None): - chunk = chunk[~chunk.song.isin(mismatches)] - chunk.to_csv(filename_out, mode='a', header=False, index=False) - #result = result.append(chunk, ignore_index=True) -elapsed_time = time.time() - start_time -print 'Execution time: %.3f seconds' % elapsed_time -#result.to_pickle('/homes/pchilguano/dataset/train_triplets_wo_mismatch.pkl') - -# Select (most active) users with more than 1000 songs played -start_time = time.time() -print 'Reading (filtered) Taste Profile subset...' -df = pd.read_csv( - filename_out, - delim_whitespace=False, - header=None, - names=['user','song','plays']) -print 'Selecting most active users (>= 1000 songs played)...' -df_active = df.groupby('user').filter(lambda x: len(x) > 1000) -print 'Reducing Taste Profile subset to 1500 songs...' -counts = df_active['song'].value_counts().head(1500) -df_active = df_active.loc[df['song'].isin(counts.index), :] -df_active.to_pickle('/Users/paulochiliguano/Documents/msc-project/Dataset/\ -CF_dataset.pkl') -filename = '/Users/paulochiliguano/Documents/msc-project/Dataset/\ -CF_dataset_songID.txt' -with open(filename, 'wb') as f: - for item in counts.index.tolist(): - f.write("%s\n" % item) -elapsed_time = time.time() - start_time -print 'Execution time: %.3f seconds' % elapsed_time - - -''' -#important -#df['user'].value_counts().head(50) - -ddf = df.drop_duplicates(subset = 'song') -ddf.to_csv('/homes/pchilguano/dataset/train_triplets_songID.csv',columns=['song'], header=False, index=False) - - - -with open('/homes/pchilguano/dataset/sid_mismatches_songID.txt', 'rb') as input1, open('/homes/pchilguano/dataset/train_triplets_songID.csv', 'rb') as input2, open('/homes/pchilguano/dataset/echonest_songID.txt', 'wb') as myfile: - l1 = list(csv.reader(input1)) - chain1 = list(itertools.chain(*l1)) - l2 = list(csv.reader(input2)) - chain2 = list(itertools.chain(*l2)) - l3 = set(chain2) - set(chain1) - wr = csv.writer(myfile, delimiter=',') - for item in l3: - wr.writerow([item]) - -# Save Taste Profile dataset without SongID mismatches -mdf = df[df.song.isin(l3)] -mdf.to_csv('/homes/pchilguano/dataset/train_triplets_wo_mismatches.csv', header=False, index=False) - -result = pd.DataFrame() -for chunk in pd.read_csv('/homes/pchilguano/dataset/train_triplets_wo_mismatches.csv', low_memory = False, delim_whitespace=False, chunksize=10000, names=['user','song','plays'], header=None): - chunk = chunk[chunk.song.isin(l3)] - result = result.append(chunk.pivot(index='user', columns='song', values='plays') - , ignore_index=True) - print (result.shape) -''' \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/Code/taste_profile_cleaning.py Sat Aug 15 19:16:17 2015 +0100 @@ -0,0 +1,100 @@ + + + + + + + + +import time +import pandas as pd + +# Read songIDs from Million Song Dataset songID-trackID mismatches +start_time = time.time() +print 'Reading songID mismatches...' +filename = '/Users/paulochiliguano/Documents/msc-project/Dataset/\ +sid_mismatches.txt' +with open(filename, 'rb') as f: + mismatches = set() + next = f.readline() + while next != "": + songID = next[8:26] + mismatches.add(songID) + #print(next[8:26]) + next = f.readline() + +# Delete rows with songIDs mismatches from Taste Profile Subset +print 'Reading Taste Profile subset...' +result = pd.DataFrame() +filename = '/Users/paulochiliguano/Documents/msc-project/Dataset/\ +train_triplets.txt' +filename_out = '/Users/paulochiliguano/Documents/msc-project/Dataset/\ +train_triplets_wo_mismatches.csv' +for chunk in pd.read_csv( + filename, + low_memory=False, + delim_whitespace=True, + chunksize=20000, + names=['user', 'song', 'plays'], + header=None): + chunk = chunk[~chunk.song.isin(mismatches)] + chunk.to_csv(filename_out, mode='a', header=False, index=False) + #result = result.append(chunk, ignore_index=True) +elapsed_time = time.time() - start_time +print 'Execution time: %.3f seconds' % elapsed_time +#result.to_pickle('/homes/pchilguano/dataset/train_triplets_wo_mismatch.pkl') + +# Select (most active) users with more than 1000 songs played +start_time = time.time() +print 'Reading (filtered) Taste Profile subset...' +df = pd.read_csv( + filename_out, + delim_whitespace=False, + header=None, + names=['user','song','plays']) +print 'Selecting most active users (>= 1000 songs played)...' +df_active = df.groupby('user').filter(lambda x: len(x) > 1000) +print 'Reducing Taste Profile subset to 1500 songs...' +counts = df_active['song'].value_counts().head(1500) +df_active = df_active.loc[df_active['song'].isin(counts.index), :] +df_active.to_pickle('/Users/paulochiliguano/Documents/msc-project/Dataset/\ +CF_dataset.pkl') +filename = '/Users/paulochiliguano/Documents/msc-project/Dataset/\ +CF_dataset_songID.txt' +with open(filename, 'wb') as f: + for item in counts.index.tolist(): + f.write("%s\n" % item) +elapsed_time = time.time() - start_time +print 'Execution time: %.3f seconds' % elapsed_time + + +''' +#important +#df['user'].value_counts().head(50) + +ddf = df.drop_duplicates(subset = 'song') +ddf.to_csv('/homes/pchilguano/dataset/train_triplets_songID.csv',columns=['song'], header=False, index=False) + + + +with open('/homes/pchilguano/dataset/sid_mismatches_songID.txt', 'rb') as input1, open('/homes/pchilguano/dataset/train_triplets_songID.csv', 'rb') as input2, open('/homes/pchilguano/dataset/echonest_songID.txt', 'wb') as myfile: + l1 = list(csv.reader(input1)) + chain1 = list(itertools.chain(*l1)) + l2 = list(csv.reader(input2)) + chain2 = list(itertools.chain(*l2)) + l3 = set(chain2) - set(chain1) + wr = csv.writer(myfile, delimiter=',') + for item in l3: + wr.writerow([item]) + +# Save Taste Profile dataset without SongID mismatches +mdf = df[df.song.isin(l3)] +mdf.to_csv('/homes/pchilguano/dataset/train_triplets_wo_mismatches.csv', header=False, index=False) + +result = pd.DataFrame() +for chunk in pd.read_csv('/homes/pchilguano/dataset/train_triplets_wo_mismatches.csv', low_memory = False, delim_whitespace=False, chunksize=10000, names=['user','song','plays'], header=None): + chunk = chunk[chunk.song.isin(l3)] + result = result.append(chunk.pivot(index='user', columns='song', values='plays') + , ignore_index=True) + print (result.shape) +''' \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/Code/time_freq_representation/feature_extraction.py Sat Aug 15 19:16:17 2015 +0100 @@ -0,0 +1,126 @@ +""" +This script computes intermediate time-frequency representation (log-mel spectrogram) +from audio signals + +Source code: +https://github.com/sidsig/ICASSP-MLP-Code/blob/master/feature_extraction.py + +Modified by: +Paulo Chiliguano +MSc candidate Sound and Music Computing +Queen Mary University of London +2015 + +References: + - Sigtia, S., & Dixon, S. (2014, May). Improved music feature learning with deep neural + networks. In Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International + Conference on (pp. 6959-6963). IEEE. + - Van den Oord, A., Dieleman, S., & Schrauwen, B. (2013). Deep content-based music + recommendation. In Advances in Neural Information Processing Systems (pp. 2643-2651). +""" + +#import subprocess +#import sys +import os +#from spectrogram import SpecGram +import tables +#import pdb +# LibROSA is a package that allows feature extraction for Music Information Retrieval +import librosa +import numpy as np + +def read_wav(filename): + #bits_per_sample = '16' + #cmd = ['sox',filename,'-t','raw','-e','unsigned-integer','-L','-c','1','-b',bits_per_sample,'-','pad','0','30.0','rate','22050.0','trim','0','30.0'] + #cmd = ' '.join(cmd) + #print cmd + #raw_audio = numpy.fromstring(subprocess.Popen(cmd,stdout=subprocess.PIPE,shell=True).communicate()[0],dtype='uint16') + audioFile, sr = librosa.load(filename, sr=22050, mono=True, offset=0, duration=3) + #random.randint(0,audioFile.size) + #max_amp = 2.**(int(bits_per_sample)-1) + #raw_audio = (raw_audio- max_amp)/max_amp + return audioFile + +def calc_specgram(x,fs,winSize,): + S = librosa.feature.melspectrogram( + y=x, + sr=fs, + n_mels=128, + S=None, + n_fft=winSize, + hop_length=512 + ) + log_S = librosa.logamplitude(S, ref_power=np.max) + log_S = np.transpose(log_S) + return log_S + #spec = SpecGram(x,fs,winSize) + #return spec.specMat + +def make_4tensor(x): + assert x.ndim <= 4 + while x.ndim < 4: + x = np.expand_dims(x,0) + return x + +class FeatExtraction(): + def __init__(self,dataset_dir): + self.dataset_dir = dataset_dir + self.list_dir = os.path.join(self.dataset_dir,'lists') + self.get_filenames() + self.feat_dir = os.path.join(self.dataset_dir,'features') + self.make_feat_dir() + self.h5_filename = os.path.join(self.feat_dir,'feats.h5') + self.make_h5() + self.setup_h5() + self.extract_features() + self.close_h5() + + + def get_filenames(self,): + dataset_files = os.path.join(self.list_dir,'audio_files.txt') + self.filenames = [l.strip() for l in open(dataset_files,'r').readlines()] + self.num_files = len(self.filenames) + + def make_feat_dir(self,): + if not os.path.exists(self.feat_dir): + print 'Making output dir.' + os.mkdir(self.feat_dir) + else: + print 'Output dir already exists.' + + def make_h5(self,): + if not os.path.exists(self.h5_filename): + self.h5 = tables.openFile(self.h5_filename,'w') + else: + print 'Feature file already exists.' + self.h5 = tables.openFile(self.h5_filename,'a') + + def setup_h5(self,): + filename = self.filenames[0] + x = read_wav(filename) + spec_x = calc_specgram(x,22050,1024) + spec_x = make_4tensor(spec_x) + self.data_shape = spec_x.shape[1:] + self.x_earray_shape = (0,) + self.data_shape + self.chunkshape = (1,) + self.data_shape + self.h5_x = self.h5.createEArray('/','x',tables.FloatAtom(itemsize=4),self.x_earray_shape,chunkshape=self.chunkshape,expectedrows=self.num_files) + self.h5_filenames = self.h5.createEArray('/','filenames',tables.StringAtom(256),(0,),expectedrows=self.num_files) + self.h5_x.append(spec_x) + self.h5_filenames.append([filename]) + + def extract_features(self,): + for i in xrange(1,self.num_files): + filename = self.filenames[i] + #print 'Filename: ',filename + x = read_wav(filename) + spec_x = calc_specgram(x,22050,1024) + spec_x = make_4tensor(spec_x) + self.h5_x.append(spec_x) + self.h5_filenames.append([filename]) + + def close_h5(self,): + self.h5.flush() + self.h5.close() + +if __name__ == '__main__': + test = FeatExtraction('/home/paulo/Documents/msc_project/dataset/7digital')
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/Code/time_freq_representation/make_lists.py Sat Aug 15 19:16:17 2015 +0100 @@ -0,0 +1,165 @@ +""" +This script creates lists of audio files contained in a folder. + +Source code: +https://github.com/sidsig/ICASSP-MLP-Code/blob/master/make_lists.py + +Modified by: +Paulo Chiliguano +MSc candidate Sound and Music Computing +Queen Mary University of London +2015 + +References: + - Sigtia, S., & Dixon, S. (2014, May). Improved music feature learning with deep neural + networks. In Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International + Conference on (pp. 6959-6963). IEEE. +""" + +import numpy +import numpy.random as random +import os +import pickle +import sys +import utils as U +#import pdb + +def read_file(filename): + """ + Loads a file into a list + """ + file_list=[l.strip() for l in open(filename,'r').readlines()] + return file_list + +def get_folds(filelist, n_folds): + n_per_fold = len(filelist) / n_folds + folds = [] + for i in range(n_folds-1): + folds.append(filelist[i * n_per_fold: (i + 1) * n_per_fold]) + i = n_folds - 1 + folds.append(filelist[i * n_per_fold:]) + return folds + +def generate_mirex_list(train_list, annotations): + out_list = [] + for song in train_list: + annot = annotations.get(song,None) + if annot is None: + print 'No annotations for song %s' % song + continue + assert(type('') == type(annot)) + out_list.append('%s\t%s\n' % (song,annot)) + + return out_list + + +def make_file_list(gtzan_path, n_folds=5,): + """ + Generates lists + """ + audio_path = os.path.join(gtzan_path,'audio') + out_path = os.path.join(gtzan_path,'lists') + files_list = [] + for ext in ['.au', '.mp3', '.wav']: + files = U.getFiles(audio_path, ext) + files_list.extend(files) + random.shuffle(files_list) + + if not os.path.exists(out_path): + os.makedirs(out_path) + + audio_list_path = os.path.join(out_path, 'audio_files.txt') + open(audio_list_path,'w').writelines(['%s\n' % f for f in files_list]) + + annotations = get_annotations(files_list) + + ground_truth_path = os.path.join(out_path, 'ground_truth.txt') + open(ground_truth_path,'w').writelines(generate_mirex_list(files_list, annotations)) + generate_ground_truth_pickle(ground_truth_path) + + folds = get_folds(files_list, n_folds=n_folds) + + ### Single fold for quick experiments + create_fold(0, 1, folds, annotations, out_path) + + for n in range(n_folds): + create_fold(n, n_folds, folds, annotations, out_path) + + +def create_fold(n, n_folds, folds, annotations, out_path): + train_path = os.path.join(out_path, 'train_%i_of_%i.txt' % (n+1, n_folds)) + valid_path = os.path.join(out_path, 'valid_%i_of_%i.txt' % (n+1, n_folds)) + test_path = os.path.join(out_path, 'test_%i_of_%i.txt' % (n+1, n_folds)) + + test_list = folds[n] + train_list = [] + for m in range(len(folds)): + if m != n: + train_list.extend(folds[m]) + + open(train_path,'w').writelines(generate_mirex_list(train_list, annotations)) + open(test_path,'w').writelines(generate_mirex_list(test_list, annotations)) + split_list_file(train_path, train_path, valid_path, ratio=0.8) + +def split_list_file(input_file, out_file1, out_file2, ratio=0.8): + input_list = open(input_file,'r').readlines() + + n = len(input_list) + nsplit = int(n *ratio) + + list1 = input_list[:nsplit] + list2 = input_list[nsplit:] + + open(out_file1, 'w').writelines(list1) + open(out_file2, 'w').writelines(list2) + + +def get_annotation(filename): + genre = os.path.split(U.parseFile(filename)[0])[-1] + return genre + +def get_annotations(files_list): + annotations = {} + for filename in files_list: + annotations[filename] = get_annotation(filename) + + return annotations + +def generate_ground_truth_pickle(gt_file): + gt_path,_ = os.path.split(gt_file) + tag_file = os.path.join(gt_path,'tags.txt') + gt_pickle = os.path.join(gt_path,'ground_truth.pickle') + + lines = open(gt_file,'r').readlines() + + tag_set = set() + for line in lines: + filename,tag = line.strip().split('\t') + tag_set.add(tag) + tag_list = sorted(list(tag_set)) + open(tag_file,'w').writelines('\n'.join(tag_list + [''])) + + tag_dict = dict([(tag,i) for i,tag in enumerate(tag_list)]) + n_tags = len(tag_dict) + + mp3_dict = {} + for line in lines: + filename,tag = line.strip().split('\t') + tag_vector = mp3_dict.get(filename,numpy.zeros(n_tags)) + if tag != '': + tag_vector[tag_dict[tag]] = 1. + mp3_dict[filename] = tag_vector + pickle.dump(mp3_dict,open(gt_pickle,'w')) + +if __name__ == '__main__': + if len(sys.argv) < 2: + print 'Usage: python %s gtzan_path [n_folds=10]' % sys.argv[0] + sys.exit() + + gtzan_path = os.path.abspath(sys.argv[1]) + if len(sys.argv) > 2: + n_folds = int(sys.argv[2]) + else: + n_folds = 10 + + make_file_list(gtzan_path, n_folds) \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/Code/time_freq_representation/utils.py Sat Aug 15 19:16:17 2015 +0100 @@ -0,0 +1,41 @@ +""" +Source code: +https://github.com/sidsig/ICASSP-MLP-Code/blob/master/utils.py +""" + +import os + +def getFiles(root_dir,ext='.mp3',verbose=True) : + """ + Returns a list of files + """ + fileList=[] + if verbose: + print "Populating %s files..."%ext + for (root,dirs,files) in os.walk(root_dir): + for f in files: + if f.endswith(ext): + filePath=os.path.join(root,f) + fileList.append(filePath) + if verbose: + print "%i files found."%len(fileList) + return fileList + +def parseFile(filePath): + """ + Parses the file path and returns (root,fileName,ext) + """ + root,file=os.path.split(filePath) + fileName,fileExt=os.path.splitext(file) + return (root,fileName,fileExt) + +def read_file(filename): + """ + Loads a file into a list + """ + file_list=[l.strip() for l in open(filename,'r').readlines()] + return file_list + +def writeFile(dataList,filename): + with open(filename,'w') as f: + f.writelines(dataList) \ No newline at end of file
--- a/Code/utils.py Tue Aug 11 14:23:42 2015 +0100 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,36 +0,0 @@ -import os - -def getFiles(root_dir,ext='.mp3',verbose=True) : - """ - Returns a list of files - """ - fileList=[] - if verbose: - print "Populating %s files..."%ext - for (root,dirs,files) in os.walk(root_dir): - for f in files: - if f.endswith(ext): - filePath=os.path.join(root,f) - fileList.append(filePath) - if verbose: - print "%i files found."%len(fileList) - return fileList - -def parseFile(filePath): - """ - Parses the file path and returns (root,fileName,ext) - """ - root,file=os.path.split(filePath) - fileName,fileExt=os.path.splitext(file) - return (root,fileName,fileExt) - -def read_file(filename): - """ - Loads a file into a list - """ - file_list=[l.strip() for l in open(filename,'r').readlines()] - return file_list - -def writeFile(dataList,filename): - with open(filename,'w') as f: - f.writelines(dataList) \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/Dataset/7digital/CF_dataset_metadata.txt Sat Aug 15 19:16:17 2015 +0100 @@ -0,0 +1,640 @@ +SOZVCRW12A67ADA0B7 TRQBGQS13269B91E41 The Killers When You Were Young http://previews.7digital.com/clip/453787 +SOPUCYA12A8C13A694 TRIWUJJ12E5AE91ADF Five Iron Frenzy Canada http://previews.7digital.com/clip/2347979 +SOSXLTC12AF72A7F54 TRUKOYA12E5B315514 Kings of Leon Revelry http://previews.7digital.com/clip/4386514 +SOPXKYD12A6D4FA876 TRVKCWF12E4E5856DA Coldplay Yellow http://previews.7digital.com/clip/3326 +SOBUBLL12A58A795A8 TRXLPHX12E5ADEFECC Tiny Vipers They Might Follow You http://previews.7digital.com/clip/2435776 +SOEGIYH12A6D4FC0E3 TRFKAWM1380A667454 Barry Tuckwell Horn Concerto No. 4 in E Flat, K.495: II. 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--- a/Report/chapter2/background.tex Tue Aug 11 14:23:42 2015 +0100 +++ b/Report/chapter2/background.tex Sat Aug 15 19:16:17 2015 +0100 @@ -1,4 +1,4 @@ -\chapter{Background research} +\chapter{Background} Recommender systems set up opportunities and challenges for industry to understand consumption behaviour of users. In particular, for music industry, the develop of recommender systems could improve sales for artists and labels, and the discovery of new songs for listeners. However, regarding that music tastes vary from one person to another person, an advantageous music recommender system should be able to infer listeners needs through their historical listening preference information, similarities with another listeners, and audio signal features from their music collections. In the following sections, the importance of online social networks for retrieving user-item information among with previous work on music recommender systems are presented. Subsequently, a novel approach of an hybrid recommender system based on Estimation of Distribution Algorithm (EDA) is introduced and examined. @@ -8,6 +8,8 @@ During the last decade, online social networks have become the outstanding source of multimedia information. +social net info edges between user + \subsection{Last.fm} Last.fm is a social network system that accumulate a list of played audio tracks from registered users through \emph{scrobbling} to provide to any user a detail about listening preference and taste similarites between connected friends in the network. Last.fm also uses scrobbling to feed its music recommendation service to help to users to discover new artists.
--- a/Report/chiliguano_msc_finalproject.toc Tue Aug 11 14:23:42 2015 +0100 +++ b/Report/chiliguano_msc_finalproject.toc Sat Aug 15 19:16:17 2015 +0100 @@ -1,6 +1,6 @@ \contentsline {chapter}{\numberline {1}Introduction}{5}{chapter.1} \contentsline {section}{\numberline {1.1}Outline of the thesis}{6}{section.1.1} -\contentsline {chapter}{\numberline {2}Background research}{8}{chapter.2} +\contentsline {chapter}{\numberline {2}Background}{8}{chapter.2} \contentsline {section}{\numberline {2.1}Online Social Networks}{9}{section.2.1} \contentsline {subsection}{\numberline {2.1.1}Last.fm}{9}{subsection.2.1.1} \contentsline {section}{\numberline {2.2}Music services platforms}{10}{section.2.2}