Mercurial > hg > hybrid-music-recommender-using-content-based-and-social-information
changeset 15:2e3c57fba632
Convolutional Neural Network code
Scratch for Estimation of Distribution Algorithm
| author | Paulo Chiliguano <p.e.chiilguano@se14.qmul.ac.uk> |
|---|---|
| date | Sat, 25 Jul 2015 21:51:16 +0100 |
| parents | c63dac455296 |
| children | 68b8b088f50a |
| files | Code/convolutional_mlp.py Code/eda.py Code/feature_extraction.py Code/latent_vectors.py Code/logistic_sgd.py Code/make_lists.py Code/mlp.py Code/prepare_dataset.py |
| diffstat | 8 files changed, 1410 insertions(+), 10 deletions(-) [+] |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/Code/convolutional_mlp.py Sat Jul 25 21:51:16 2015 +0100 @@ -0,0 +1,372 @@ +"""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 + +# Rectifier Linear Unit +# Source: http://stackoverflow.com/questions/26497564/theano-hiddenlayer-activation-function +def relu(x): + return T.switch(x<0, 0, x) + + + +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 + ) + + # 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, 70], batch_size=100): + """ 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, 1206, 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, 1206, 128), + #filter_shape=(nkerns[0], 1, 5, 5), + filter_shape=(nkerns[0], 1, 11, 11), + poolsize=(2, 2) + ) + + # 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], 598, 59), + #filter_shape=(nkerns[1], nkerns[0], 5, 5), + filter_shape=(nkerns[1], nkerns[0], 5, 5), + poolsize=(2, 2) + ) + + # Construct the third convolutional pooling layer + layer2 = LeNetConvPoolLayer( + rng, + input=layer1.output, + image_shape=(batch_size, nkerns[1], 297, 27), + filter_shape=(nkerns[2], nkerns[1], 5, 5), + poolsize=(1, 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. + layer3_input = layer2.output.flatten(2) + + # construct a fully-connected sigmoidal layer + layer3 = HiddenLayer( + rng, + input=layer3_input, + #n_in=nkerns[1] * 4 * 4, + n_in=nkerns[2] * 293 * 23, + #n_out=500, + n_out=500, + #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=500, n_out=10) + + # the cost we minimize during training is the NLL of the model + cost = layer4.negative_log_likelihood(y) + + # create a function to compute the mistakes that are made by the model + test_model = theano.function( + [index], + layer4.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], + layer4.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] + } + ) + + # create a list of all model parameters to be fit by gradient descent + 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 = 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): + + 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.)) + + 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.)) + +if __name__ == '__main__': + evaluate_lenet5() + + +def experiment(state, channel): + evaluate_lenet5(state.learning_rate, dataset=state.dataset) +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/Code/eda.py Sat Jul 25 21:51:16 2015 +0100 @@ -0,0 +1,76 @@ +# -*- coding: utf-8 -*- +""" +Created on Wed Jul 22 17:42:09 2015 + +@author: paulochiliguano +""" + +import numpy as np +from sklearn import mixture + +#User-item dictionary +users = {"Angelica": {"SOAJJPC12AB017D63F": 3.5, "SOAKIXJ12AC3DF7152": 2.0, + "SOAKPFH12A8C13BA4A": 4.5, "SOAGTJW12A6701F1F5": 5.0, + "SOAKWCK12A8C139F81": 1.5, "SOAKNZI12A58A79CAC": 2.5, + "SOAJZEP12A8C14379B": 2.0}, + "Bill":{"SOAJJPC12AB017D63F": 2.0, "SOAKIXJ12AC3DF7152": 3.5, + "SOAHQFM12A8C134B65": 4.0, "SOAGTJW12A6701F1F5": 2.0, + "SOAKWCK12A8C139F81": 3.5, "SOAJZEP12A8C14379B": 3.0}, + "Chan": {"SOAJJPC12AB017D63F": 5.0, "SOAKIXJ12AC3DF7152": 1.0, + "SOAHQFM12A8C134B65": 1.0, "SOAKPFH12A8C13BA4A": 3.0, + "SOAGTJW12A6701F1F5": 5, "SOAKWCK12A8C139F81": 1.0}, + "Dan": {"SOAJJPC12AB017D63F": 3.0, "SOAKIXJ12AC3DF7152": 4.0, + "SOAHQFM12A8C134B65": 4.5, "SOAGTJW12A6701F1F5": 3.0, + "SOAKWCK12A8C139F81": 4.5, "SOAKNZI12A58A79CAC": 4.0, + "SOAJZEP12A8C14379B": 2.0}, + "Hailey": {"SOAKIXJ12AC3DF7152": 4.0, "SOAHQFM12A8C134B65": 1.0, + "SOAKPFH12A8C13BA4A": 4.0, "SOAKNZI12A58A79CAC": 4.0, + "SOAJZEP12A8C14379B": 1.0}, + "Jordyn": {"SOAKIXJ12AC3DF7152": 4.5, "SOAHQFM12A8C134B65": 4.0, + "SOAKPFH12A8C13BA4A": 5.0, "SOAGTJW12A6701F1F5": 5.0, + "SOAKWCK12A8C139F81": 4.5, "SOAKNZI12A58A79CAC": 4.0, + "SOAJZEP12A8C14379B": 4.0}, + "Sam": {"SOAJJPC12AB017D63F": 5.0, "SOAKIXJ12AC3DF7152": 2.0, + "SOAKPFH12A8C13BA4A": 3.0, "SOAGTJW12A6701F1F5": 5.0, + "SOAKWCK12A8C139F81": 4.0, "SOAKNZI12A58A79CAC": 5.0}, + "Veronica": {"SOAJJPC12AB017D63F": 3.0, "SOAKPFH12A8C13BA4A": 5.0, + "SOAGTJW12A6701F1F5": 4.0, "SOAKWCK12A8C139F81": 2.5, + "SOAKNZI12A58A79CAC": 3.0} + } + +items = {"SOAJJPC12AB017D63F": [2.5, 4, 3.5, 3, 5, 4, 1], + "SOAKIXJ12AC3DF7152": [2, 5, 5, 3, 2, 1, 1], + "SOAKPFH12A8C13BA4A": [1, 5, 4, 2, 4, 1, 1], + "SOAGTJW12A6701F1F5": [4, 5, 4, 4, 1, 5, 1], + "SOAKWCK12A8C139F81": [1, 4, 5, 3.5, 5, 1, 1], + "SOAKNZI12A58A79CAC": [1, 5, 3.5, 3, 4, 5, 1], + "SOAJZEP12A8C14379B": [5, 5, 4, 2, 1, 1, 1], + "SOAHQFM12A8C134B65": [2.5, 4, 4, 1, 1, 1, 1]} + +profile = {"Profile0": [2.5, 4, 3.5, 3, 5, 4, 1], + "Profile1": [2.5, 4, 3.5, 3, 5, 4, 1], + "Profile2": [2.5, 4, 3.5, 3, 5, 4, 1], + "Profile3": [2.5, 4, 3.5, 3, 5, 4, 1], + "Profile4": [2.5, 4, 3.5, 3, 5, 4, 1], + "Profile5": [2.5, 4, 3.5, 3, 5, 4, 1], + "Profile6": [2.5, 4, 3.5, 3, 5, 4, 1], + "Profile7": [2.5, 4, 3.5, 3, 5, 4, 1]} + + + + +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 Jul 21 11:54:30 2015 +0100 +++ b/Code/feature_extraction.py Sat Jul 25 21:51:16 2015 +0100 @@ -21,7 +21,7 @@ #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=3) + 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 @@ -30,7 +30,9 @@ 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 @@ -90,7 +92,7 @@ def extract_features(self,): for i in xrange(1,self.num_files): filename = self.filenames[i] - print 'Filename: ',filename + #print 'Filename: ',filename x = read_wav(filename) spec_x = calc_specgram(x,22050,1024) spec_x = make_4tensor(spec_x) @@ -102,5 +104,5 @@ self.h5.close() if __name__ == '__main__': - test = FeatExtraction('/media/paulo/5409-57C0') + test = FeatExtraction('/home/paulo/Downloads')
--- a/Code/latent_vectors.py Tue Jul 21 11:54:30 2015 +0100 +++ b/Code/latent_vectors.py Sat Jul 25 21:51:16 2015 +0100 @@ -13,17 +13,26 @@ import wmf # Read songID of downloaded audio clips -with open('/homes/pchilguano/dataset/audio_files.txt', 'rb') as input1: +with open('/homes/pchilguano/dataset/ten_songs.txt', 'rb') as input1: available = list(csv.reader(input1)) chain1 = list(itertools.chain(*available)) # Sparse user-item matrix 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(chain1)] - result = result.append(chunk.pivot(index='user', columns='song', values='plays') - , ignore_index=True) + chunk = chunk[chunk.song.isin(chain1)] + result = result.append(chunk, ignore_index=True) + #result = result.append(chunk.pivot(index='user', columns='song', values='plays')) print (result.shape) + + +cnames = result.set_index('user').T.to_dict().keys() +final = {} +for a in cnames: + final[a] ={result.set_index('user').T.to_dict()[a]["song"]: result.set_index('user').T.to_dict()[a]["plays"]} + +dict((k, v.dropna().to_dict()) for k, v in pd.compat.iteritems(result)) + sresult = result.to_sparse() sresult.to_pickle('/homes/pchilguano/dataset/taste_profile_sparse.pkl')
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/Code/logistic_sgd.py Sat Jul 25 21:51:16 2015 +0100 @@ -0,0 +1,481 @@ +""" +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) + #print(self.p_y_given_x) + + # 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/gtzan.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.switch(x<0, 0, x)
--- a/Code/make_lists.py Tue Jul 21 11:54:30 2015 +0100 +++ b/Code/make_lists.py Sat Jul 25 21:51:16 2015 +0100 @@ -1,6 +1,6 @@ import numpy -#import numpy.random as random +import numpy.random as random import os import pickle import sys @@ -41,13 +41,13 @@ """ Generates lists """ - audio_path = os.path.join(gtzan_path,'preview_clip') + 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) + random.shuffle(files_list) if not os.path.exists(out_path): os.makedirs(out_path)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/Code/mlp.py Sat Jul 25 21:51:16 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.switch(x<0, 0, x)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/Code/prepare_dataset.py Sat Jul 25 21:51:16 2015 +0100 @@ -0,0 +1,48 @@ +# -*- coding: utf-8 -*- +""" +Created on Thu Jul 23 21:55:58 2015 + +@author: paulochiliguano +""" + + +import tables +import numpy as np +import cPickle + +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() + +#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 = (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()]) + + +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) + +tag_dict = dict([(item, index) for index, item in enumerate(sorted(tag_set))]) +with open(filename, 'r') as f: + target = np.asarray([]) + 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]*4/5, flat_data.shape[0]*9/10]) +train_target, valid_target, test_target = np.array_split(target, [target.shape[0]*4/5, target.shape[0]*9/10]) + +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()