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GeezerVison is showing github.com — DeepLearningTutorials/code/SdA.py at master · dchichkov/DeepLearningTutorials Expand file tree/ Copy pathSdA.pyMore file actions executable file ·449 lines (346 loc) · 18.2 KB / Copy pathSdA.pyFile metadata and controlsexecutable file ·449 lines (346 loc) · 18.2 KB 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 #!/usr/bin/python # -*- coding: utf-8 -*- """ This tutorial introduces stacked denoising auto-encoders (SdA) using Theano. Denoising autoencoders are the building blocks for SdA. They are based on auto-encoders as the ones used in Bengio et al. 2007. An autoencoder takes an input x and first maps it to a hidden representation y = f_{\theta}(x) = s(Wx+b), parameterized by \theta={W,b}. The resulting latent representation y is then mapped back to a "reconstructed" vector z \in [0,1]^d in input space z = g_{\theta'}(y) = s(W'y + b'). The weight matrix W' can optionally be constrained such that W' = W^T, in which case the autoencoder is said to have tied weights. The network is trained such that to minimize the reconstruction error (the error between x and z). For the denosing autoencoder, during training, first x is corrupted into \tilde{x}, where \tilde{x} is a partially destroyed version of x by means of a stochastic mapping. Afterwards y is computed as before (using \tilde{x}), y = s(W\tilde{x} + b) and z as s(W'y + b'). The reconstruction error is now measured between z and the uncorrupted input x, which is computed as the cross-entropy : - \sum_{k=1}^d[ x_k \log z_k + (1-x_k) \log( 1-z_k)] References : - P. Vincent, H. Larochelle, Y. Bengio, P.A. Manzagol: Extracting and Composing Robust Features with Denoising Autoencoders, ICML'08, 1096-1103, 2008 - Y. Bengio, P. Lamblin, D. Popovici, H. Larochelle: Greedy Layer-Wise Training of Deep Networks, Advances in Neural Information Processing Systems 19, 2007 """ import numpy, time, cPickle, gzip, sys, os import theano import theano.tensor as T from theano.tensor.shared_randomstreams import RandomStreams from logistic_sgd import LogisticRegression from load_mp3 import * from mlp import HiddenLayer from dA import dA from dAau import dAau class SdA(object): """Stacked denoising auto-encoder class (SdA) A stacked denoising autoencoder model is obtained by stacking several dAs. The hidden layer of the dA at layer `i` becomes the input of the dA at layer `i+1`. The first layer dA gets as input the input of the SdA, and the hidden layer of the last dA represents the output. Note that after pretraining, the SdA is dealt with as a normal MLP, the dAs are only used to initialize the weights. """ def __init__(self, numpy_rng, theano_rng = None, n_ins = 784, hidden_layers_sizes = [500,500], n_outs = 10, corruption_levels = [0.1, 0.1]): """ This class is made to support a variable number of layers. :type numpy_rng: numpy.random.RandomState :param numpy_rng: numpy random number generator used to draw initial weights :type theano_rng: theano.tensor.shared_randomstreams.RandomStreams :param theano_rng: Theano random generator; if None is given one is generated based on a seed drawn from `rng` :type n_ins: int :param n_ins: dimension of the input to the sdA :type n_layers_sizes: list of ints :param n_layers_sizes: intermediate layers size, must contain at least one value :type n_outs: int :param n_outs: dimension of the output of the network :type corruption_levels: list of float :param corruption_levels: amount of corruption to use for each layer """ self.sigmoid_layers = [] self.dA_layers = [] self.params = [] self.n_layers = len(hidden_layers_sizes) assert self.n_layers > 0 if not theano_rng: theano_rng = RandomStreams(numpy_rng.randint(2**30)) # allocate symbolic variables for the data self.x = T.matrix('x') # the data is presented as rasterized images self.y = T.ivector('y') # the labels are presented as 1D vector of # [int] labels # The SdA is an MLP, for which all weights of intermediate layers # are shared with a different denoising autoencoders # We will first construct the SdA as a deep multilayer perceptron, # and when constructing each sigmoidal layer we also construct a # denoising autoencoder that shares weights with that layer # During pretraining we will train these autoencoders (which will # lead to chainging the weights of the MLP as well) # During finetunining we will finish training the SdA by doing # stochastich gradient descent on the MLP for i in xrange( self.n_layers ): # construct the sigmoidal layer # the size of the input is either the number of hidden units of # the layer below or the input size if we are on the first layer if i == 0 : input_size = n_ins else: input_size = hidden_layers_sizes[i-1] # the input to this layer is either the activation of the hidden # layer below or the input of the SdA if you are on the first # layer if i == 0 : layer_input = self.x else: layer_input = self.sigmoid_layers[-1].output sigmoid_layer = HiddenLayer(rng = numpy_rng, input = layer_input, n_in = input_size, n_out = hidden_layers_sizes[i], activation = T.nnet.sigmoid) # add the layer to our list of layers self.sigmoid_layers.append(sigmoid_layer) # its arguably a philosophical question... # but we are going to only declare that the parameters of the # sigmoid_layers are parameters of the StackedDAA # the visible biases in the dA are parameters of those # dA, but not the SdA self.params.extend(sigmoid_layer.params) # Construct a denoising autoencoder that shared weights with this # layer, use dAau for the first layer dAclass = (dA, dAau)[i == 0] dA_layer = dAclass(numpy_rng = numpy_rng, theano_rng = theano_rng, input = layer_input, n_visible = input_size, n_hidden = hidden_layers_sizes[i], W = sigmoid_layer.W, bhid = sigmoid_layer.b) self.dA_layers.append(dA_layer) # We now need to add a logistic layer on top of the MLP self.logLayer = LogisticRegression(\ input = self.sigmoid_layers[-1].output,\ n_in = hidden_layers_sizes[-1], n_out = n_outs) self.params.extend(self.logLayer.params) # construct a function that implements one step of finetunining # compute the cost for second phase of training, # defined as the negative log likelihood self.finetune_cost = self.logLayer.negative_log_likelihood(self.y) # compute the gradients with respect to the model parameters # symbolic variable that points to the number of errors made on the # minibatch given by self.x and self.y self.errors = self.logLayer.errors(self.y) def pretraining_functions(self, train_set_x, batch_size): ''' Generates a list of functions, each of them implementing one step in trainnig the dA corresponding to the layer with same index. The function will require as input the minibatch index, and to train a dA you just need to iterate, calling the corresponding function on all minibatch indexes. :type train_set_x: theano.tensor.TensorType :param train_set_x: Shared variable that contains all datapoints used for training the dA :type batch_size: int :param batch_size: size of a [mini]batch :type learning_rate: float :param learning_rate: learning rate used during training for any of the dA layers ''' # index to a [mini]batch index = T.lscalar('index') # index to a minibatch corruption_level = T.scalar('corruption') # amount of corruption to use learning_rate = T.scalar('lr') # learning rate to use # number of batches n_batches = train_set_x.value.shape[0] / batch_size # begining of a batch, given `index` batch_begin = index * batch_size # ending of a batch given `index` batch_end = batch_begin+batch_size pretrain_fns = [] for dA in self.dA_layers: # get the cost and the updates list cost,updates = dA.get_cost_updates( corruption_level, learning_rate) # compile the theano function fn = theano.function( inputs = [index, theano.Param(corruption_level, default = 0.2), theano.Param(learning_rate, default = 0.1)], outputs = cost, updates = updates, givens = {self.x :train_set_x[batch_begin:batch_end]}) # append `fn` to the list of functions pretrain_fns.append(fn) return pretrain_fns def build_finetune_functions(self, datasets, batch_size, learning_rate): '''Generates a function `train` that implements one step of finetuning, a function `validate` that computes the error on a batch from the validation set, and a function `test` that computes the error on a batch from the testing set :type datasets: list of pairs of theano.tensor.TensorType :param datasets: It is a list that contain all the datasets; the has to contain three pairs, `train`, `valid`, `test` in this order, where each pair is formed of two Theano variables, one for the datapoints, the other for the labels :type batch_size: int :param batch_size: size of a minibatch :type learning_rate: float :param learning_rate: learning rate used during finetune stage ''' (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_valid_batches = valid_set_x.value.shape[0] / batch_size n_test_batches = test_set_x.value.shape[0] / batch_size index = T.lscalar('index') # index to a [mini]batch # compute the gradients with respect to the model parameters gparams = T.grad(self.finetune_cost, self.params) # compute list of fine-tuning updates updates = {} for param, gparam in zip(self.params, gparams): updates[param] = param - gparam*learning_rate train_fn = theano.function(inputs = [index], outputs = self.finetune_cost, updates = updates, givens = { self.x : train_set_x[index*batch_size:(index+1)*batch_size], self.y : train_set_y[index*batch_size:(index+1)*batch_size]}) test_score_i = theano.function([index], self.errors, givens = { self.x: test_set_x[index*batch_size:(index+1)*batch_size], self.y: test_set_y[index*batch_size:(index+1)*batch_size]}) valid_score_i = theano.function([index], self.errors, givens = { self.x: valid_set_x[index*batch_size:(index+1)*batch_size], self.y: valid_set_y[index*batch_size:(index+1)*batch_size]}) # Create a function that scans the entire validation set def valid_score(): return [valid_score_i(i) for i in xrange(n_valid_batches)] # Create a function that scans the entire test set def test_score(): return [test_score_i(i) for i in xrange(n_test_batches)] return train_fn, valid_score, test_score def test_SdA( finetune_lr = 0.1, pretraining_epochs = 15, \ pretrain_lr = 0.001, training_epochs = 1000, \ dataset='../data/mnist.pkl.gz', batch_size = 1): """ Demonstrates how to train and test a stochastic denoising autoencoder. This is demonstrated on MNIST. :type learning_rate: float :param learning_rate: learning rate used in the finetune stage (factor for the stochastic gradient) :type pretraining_epochs: int :param pretraining_epochs: number of epoch to do pretraining :type pretrain_lr: float :param pretrain_lr: learning rate to be used during pre-training :type n_iter: int :param n_iter: maximal number of iterations ot run the optimizer :type dataset: string :param dataset: path the the pickled dataset """ 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.value.shape[0] / batch_size # numpy random generator numpy_rng = numpy.random.RandomState(89677) print '... building the model' # construct the stacked denoising autoencoder class sda = SdA( numpy_rng = numpy_rng, n_ins = S_DIM * F_DIM * X_DIM, hidden_layers_sizes = [1000,1000,1000], n_outs = 10) ######################### # PRETRAINING THE MODEL # ######################### print '... getting the pretraining functions' pretraining_fns = sda.pretraining_functions( train_set_x = train_set_x, batch_size = batch_size ) print '... pre-training the model' start_time = time.clock() ## Pre-train layer-wise corruption_levels = [.1,.2,.3] for i in xrange(sda.n_layers): # go through pretraining epochs for epoch in xrange(pretraining_epochs): # go through the training set c = [] for batch_index in xrange(n_train_batches): c.append( pretraining_fns[i](index = batch_index, corruption = corruption_levels[i], lr = pretrain_lr ) ) print 'Pre-training layer %i, epoch %d, cost '%(i,epoch),numpy.mean(c) end_time = time.clock() print >> sys.stderr, ('The pretraining code for file '+os.path.split(__file__)[1]+' ran for %.2fm' % ((end_time-start_time)/60.)) return ######################## # FINETUNING THE MODEL # ######################## # get the training, validation and testing function for the model print '... getting the finetuning functions' train_fn, validate_model, test_model = sda.build_finetune_functions ( datasets = datasets, batch_size = batch_size, learning_rate = finetune_lr) print '... finetunning the model' # early-stopping parameters patience = 10*n_train_batches # 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_params = None best_validation_loss = float('inf') test_score = 0. start_time = time.clock() done_looping = False epoch = 0 while (epoch < training_epochs) and (not done_looping): for minibatch_index in xrange(n_train_batches): minibatch_avg_cost = train_fn(minibatch_index) iter = epoch * n_train_batches + minibatch_index if (iter+1) % validation_frequency == 0: validation_losses = validate_model() 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() 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 epoch = epoch + 1 end_time = time.clock() print(('Optimization complete with best validation score of %f %%,' 'with test performance %f %%') % (best_validation_loss * 100., test_score*100.)) print >> sys.stderr, ('The training code for file '+os.path.split(__file__)[1]+' ran for %.2fm' % ((end_time-start_time)/60.)) if __name__ == '__main__': d = "/home/dmitry/mp3/01- Hitchhikers Guide to the Galaxy" dataset = sorted([os.path.join(d, f) for f in os.listdir(d)]) dataset = ["/home/dmitry/mp3/hhgttg0101006048k.mp3"] test_SdA(dataset = dataset[:1]) |
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