Mercurial > ift6266
annotate deep/autoencoder/DA_training.py @ 239:42005ec87747
Mergé (manuellement) les changements de Sylvain pour utiliser le code de dataset d'Arnaud, à cette différence près que je n'utilse pas les givens. J'ai probablement une approche différente pour limiter la taille du dataset dans mon débuggage, aussi.
| author | fsavard |
|---|---|
| date | Mon, 15 Mar 2010 18:30:21 -0400 |
| parents | e12702b88a2d |
| children |
| rev | line source |
|---|---|
| 190 | 1 """ |
| 2 This tutorial introduces stacked denoising auto-encoders (SdA) using Theano. | |
| 3 | |
| 4 Denoising autoencoders are the building blocks for SDAE. | |
| 5 They are based on auto-encoders as the ones used in Bengio et al. 2007. | |
| 6 An autoencoder takes an input x and first maps it to a hidden representation | |
| 7 y = f_{\theta}(x) = s(Wx+b), parameterized by \theta={W,b}. The resulting | |
| 8 latent representation y is then mapped back to a "reconstructed" vector | |
| 9 z \in [0,1]^d in input space z = g_{\theta'}(y) = s(W'y + b'). The weight | |
| 10 matrix W' can optionally be constrained such that W' = W^T, in which case | |
| 11 the autoencoder is said to have tied weights. The network is trained such | |
| 12 that to minimize the reconstruction error (the error between x and z). | |
| 13 | |
| 14 For the denosing autoencoder, during training, first x is corrupted into | |
| 15 \tilde{x}, where \tilde{x} is a partially destroyed version of x by means | |
| 16 of a stochastic mapping. Afterwards y is computed as before (using | |
| 17 \tilde{x}), y = s(W\tilde{x} + b) and z as s(W'y + b'). The reconstruction | |
| 18 error is now measured between z and the uncorrupted input x, which is | |
| 19 computed as the cross-entropy : | |
| 20 - \sum_{k=1}^d[ x_k \log z_k + (1-x_k) \log( 1-z_k)] | |
| 21 | |
| 22 For X iteration of the main program loop it takes *** minutes on an | |
| 23 Intel Core i7 and *** minutes on GPU (NVIDIA GTX 285 graphics processor). | |
| 24 | |
| 25 | |
| 26 References : | |
| 27 - P. Vincent, H. Larochelle, Y. Bengio, P.A. Manzagol: Extracting and | |
| 28 Composing Robust Features with Denoising Autoencoders, ICML'08, 1096-1103, | |
| 29 2008 | |
| 30 - Y. Bengio, P. Lamblin, D. Popovici, H. Larochelle: Greedy Layer-Wise | |
| 31 Training of Deep Networks, Advances in Neural Information Processing | |
| 32 Systems 19, 2007 | |
| 33 | |
| 34 """ | |
| 35 | |
| 36 import numpy | |
| 37 import theano | |
| 38 import time | |
| 39 import theano.tensor as T | |
| 40 from theano.tensor.shared_randomstreams import RandomStreams | |
| 41 | |
| 42 import gzip | |
| 43 import cPickle | |
| 44 | |
| 45 from pylearn.io import filetensor as ft | |
| 46 | |
| 47 class dA(): | |
| 48 """Denoising Auto-Encoder class (dA) | |
| 49 | |
| 50 A denoising autoencoders tries to reconstruct the input from a corrupted | |
| 51 version of it by projecting it first in a latent space and reprojecting | |
| 52 it afterwards back in the input space. Please refer to Vincent et al.,2008 | |
| 53 for more details. If x is the input then equation (1) computes a partially | |
| 54 destroyed version of x by means of a stochastic mapping q_D. Equation (2) | |
| 55 computes the projection of the input into the latent space. Equation (3) | |
| 56 computes the reconstruction of the input, while equation (4) computes the | |
| 57 reconstruction error. | |
| 58 | |
| 59 .. math:: | |
| 60 | |
| 61 \tilde{x} ~ q_D(\tilde{x}|x) (1) | |
| 62 | |
| 63 y = s(W \tilde{x} + b) (2) | |
| 64 | |
| 65 z = s(W' y + b') (3) | |
| 66 | |
| 67 L(x,z) = -sum_{k=1}^d [x_k \log z_k + (1-x_k) \log( 1-z_k)] (4) | |
| 68 | |
| 69 """ | |
| 70 | |
| 71 def __init__(self, n_visible= 784, n_hidden= 500, complexity = 0.1, input= None): | |
| 72 """ | |
| 73 Initialize the DAE class by specifying the number of visible units (the | |
| 74 dimension d of the input ), the number of hidden units ( the dimension | |
| 75 d' of the latent or hidden space ) and by giving a symbolic variable | |
| 76 for the input. Such a symbolic variable is useful when the input is | |
| 77 the result of some computations. For example when dealing with SDAEs, | |
| 78 the dA on layer 2 gets as input the output of the DAE on layer 1. | |
| 79 This output can be written as a function of the input to the entire | |
| 80 model, and as such can be computed by theano whenever needed. | |
| 81 | |
| 82 :param n_visible: number of visible units | |
| 83 | |
| 84 :param n_hidden: number of hidden units | |
| 85 | |
| 86 :param input: a symbolic description of the input or None | |
| 87 | |
| 88 """ | |
| 89 self.n_visible = n_visible | |
| 90 self.n_hidden = n_hidden | |
| 91 | |
| 92 # create a Theano random generator that gives symbolic random values | |
| 93 theano_rng = RandomStreams() | |
| 94 # create a numpy random generator | |
| 95 numpy_rng = numpy.random.RandomState() | |
|
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96 |
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97 # print the parameter of the DA |
|
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98 if True : |
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99 print 'input size = %d' %n_visible |
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100 print 'hidden size = %d' %n_hidden |
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101 print 'complexity = %2.2f' %complexity |
| 190 | 102 |
| 103 # initial values for weights and biases | |
| 104 # note : W' was written as `W_prime` and b' as `b_prime` | |
| 105 | |
| 106 # W is initialized with `initial_W` which is uniformely sampled | |
| 107 # from -6./sqrt(n_visible+n_hidden) and 6./sqrt(n_hidden+n_visible) | |
| 108 # the output of uniform if converted using asarray to dtype | |
| 109 # theano.config.floatX so that the code is runable on GPU | |
| 110 initial_W = numpy.asarray( numpy.random.uniform( \ | |
| 111 low = -numpy.sqrt(6./(n_visible+n_hidden)), \ | |
| 112 high = numpy.sqrt(6./(n_visible+n_hidden)), \ | |
| 113 size = (n_visible, n_hidden)), dtype = theano.config.floatX) | |
| 114 initial_b = numpy.zeros(n_hidden) | |
| 115 | |
| 116 # W' is initialized with `initial_W_prime` which is uniformely sampled | |
| 117 # from -6./sqrt(n_visible+n_hidden) and 6./sqrt(n_hidden+n_visible) | |
| 118 # the output of uniform if converted using asarray to dtype | |
| 119 # theano.config.floatX so that the code is runable on GPU | |
| 120 initial_b_prime= numpy.zeros(n_visible) | |
| 121 | |
| 122 | |
| 123 # theano shared variables for weights and biases | |
| 124 self.W = theano.shared(value = initial_W, name = "W") | |
| 125 self.b = theano.shared(value = initial_b, name = "b") | |
| 126 # tied weights, therefore W_prime is W transpose | |
| 127 self.W_prime = self.W.T | |
| 128 self.b_prime = theano.shared(value = initial_b_prime, name = "b'") | |
| 129 | |
| 130 # if no input is given, generate a variable representing the input | |
| 131 if input == None : | |
| 132 # we use a matrix because we expect a minibatch of several examples, | |
| 133 # each example being a row | |
| 134 x = T.dmatrix(name = 'input') | |
| 135 else: | |
| 136 x = input | |
| 137 # Equation (1) | |
| 138 # note : first argument of theano.rng.binomial is the shape(size) of | |
| 139 # random numbers that it should produce | |
| 140 # second argument is the number of trials | |
| 141 # third argument is the probability of success of any trial | |
| 142 # | |
| 143 # this will produce an array of 0s and 1s where 1 has a | |
| 144 # probability of 0.9 and 0 of 0.1 | |
| 145 | |
| 146 tilde_x = theano_rng.binomial( x.shape, 1, 1-complexity) * x | |
| 147 # Equation (2) | |
| 148 # note : y is stored as an attribute of the class so that it can be | |
| 149 # used later when stacking dAs. | |
| 150 self.y = T.nnet.sigmoid(T.dot(tilde_x, self.W ) + self.b) | |
| 151 # Equation (3) | |
| 152 z = T.nnet.sigmoid(T.dot(self.y, self.W_prime) + self.b_prime) | |
| 153 # Equation (4) | |
| 154 self.L = - T.sum( x*T.log(z) + (1-x)*T.log(1-z), axis=1 ) | |
| 155 # note : L is now a vector, where each element is the cross-entropy cost | |
| 156 # of the reconstruction of the corresponding example of the | |
| 157 # minibatch. We need to compute the average of all these to get | |
| 158 # the cost of the minibatch | |
| 159 self.cost = T.mean(self.L) | |
| 160 # note : y is computed from the corrupted `tilde_x`. Later on, | |
| 161 # we will need the hidden layer obtained from the uncorrupted | |
| 162 # input when for example we will pass this as input to the layer | |
| 163 # above | |
| 164 self.hidden_values = T.nnet.sigmoid( T.dot(x, self.W) + self.b) | |
| 165 | |
| 166 | |
| 167 | |
| 168 def sgd_optimization_nist( learning_rate=0.01, \ | |
| 169 n_iter = 300, n_code_layer = 400, \ | |
| 170 complexity = 0.1): | |
| 171 """ | |
| 172 Demonstrate stochastic gradient descent optimization for a denoising autoencoder | |
| 173 | |
| 174 This is demonstrated on MNIST. | |
| 175 | |
| 176 :param learning_rate: learning rate used (factor for the stochastic | |
| 177 gradient | |
| 178 | |
| 179 :param pretraining_epochs: number of epoch to do pretraining | |
| 180 | |
| 181 :param pretrain_lr: learning rate to be used during pre-training | |
| 182 | |
| 183 :param n_iter: maximal number of iterations ot run the optimizer | |
| 184 | |
| 185 """ | |
| 186 #open file to save the validation and test curve | |
| 187 filename = 'lr_' + str(learning_rate) + 'ni_' + str(n_iter) + 'nc_' + str(n_code_layer) + \ | |
| 188 'c_' + str(complexity) + '.txt' | |
| 189 | |
| 190 result_file = open(filename, 'w') | |
| 191 | |
| 192 | |
| 193 | |
| 194 data_path = '/data/lisa/data/nist/by_class/' | |
| 195 f = open(data_path+'all/all_train_data.ft') | |
| 196 g = open(data_path+'all/all_train_labels.ft') | |
| 197 h = open(data_path+'all/all_test_data.ft') | |
| 198 i = open(data_path+'all/all_test_labels.ft') | |
| 199 | |
| 200 train_set_x = ft.read(f) | |
| 201 train_set_y = ft.read(g) | |
| 202 test_set_x = ft.read(h) | |
| 203 test_set_y = ft.read(i) | |
| 204 | |
| 205 f.close() | |
| 206 g.close() | |
| 207 i.close() | |
| 208 h.close() | |
| 209 | |
| 210 # make minibatches of size 20 | |
| 211 batch_size = 20 # sized of the minibatch | |
| 212 | |
| 213 #create a validation set the same size as the test size | |
| 214 #use the end of the training array for this purpose | |
| 215 #discard the last remaining so we get a %batch_size number | |
| 216 test_size=len(test_set_y) | |
| 217 test_size = int(test_size/batch_size) | |
| 218 test_size*=batch_size | |
| 219 train_size = len(train_set_x) | |
| 220 train_size = int(train_size/batch_size) | |
| 221 train_size*=batch_size | |
| 222 validation_size =test_size | |
| 223 offset = train_size-test_size | |
| 224 if True: | |
| 225 print 'train size = %d' %train_size | |
| 226 print 'test size = %d' %test_size | |
| 227 print 'valid size = %d' %validation_size | |
| 228 print 'offset = %d' %offset | |
| 229 | |
| 230 | |
| 231 #train_set = (train_set_x,train_set_y) | |
| 232 train_batches = [] | |
| 233 for i in xrange(0, train_size-test_size, batch_size): | |
| 234 train_batches = train_batches + \ | |
| 235 [(train_set_x[i:i+batch_size], train_set_y[i:i+batch_size])] | |
| 236 | |
| 237 test_batches = [] | |
| 238 for i in xrange(0, test_size, batch_size): | |
| 239 test_batches = test_batches + \ | |
| 240 [(test_set_x[i:i+batch_size], test_set_y[i:i+batch_size])] | |
| 241 | |
| 242 valid_batches = [] | |
| 243 for i in xrange(0, test_size, batch_size): | |
| 244 valid_batches = valid_batches + \ | |
| 245 [(train_set_x[offset+i:offset+i+batch_size], \ | |
| 246 train_set_y[offset+i:offset+i+batch_size])] | |
| 247 | |
| 248 | |
| 249 ishape = (32,32) # this is the size of NIST images | |
| 250 | |
| 251 # allocate symbolic variables for the data | |
| 252 x = T.fmatrix() # the data is presented as rasterized images | |
| 253 y = T.lvector() # the labels are presented as 1D vector of | |
| 254 # [long int] labels | |
| 255 | |
| 256 # construct the denoising autoencoder class | |
| 257 n_ins = 32*32 | |
|
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258 encoder = dA(n_ins, n_code_layer, complexity, input = x.reshape((batch_size,n_ins))) |
| 190 | 259 |
| 260 # Train autoencoder | |
| 261 | |
| 262 # compute gradients of the layer parameters | |
| 263 gW = T.grad(encoder.cost, encoder.W) | |
| 264 gb = T.grad(encoder.cost, encoder.b) | |
| 265 gb_prime = T.grad(encoder.cost, encoder.b_prime) | |
| 266 # compute the updated value of the parameters after one step | |
| 267 updated_W = encoder.W - gW * learning_rate | |
| 268 updated_b = encoder.b - gb * learning_rate | |
| 269 updated_b_prime = encoder.b_prime - gb_prime * learning_rate | |
| 270 | |
| 271 # defining the function that evaluate the symbolic description of | |
| 272 # one update step | |
| 273 train_model = theano.function([x], encoder.cost, updates=\ | |
| 274 { encoder.W : updated_W, \ | |
| 275 encoder.b : updated_b, \ | |
| 276 encoder.b_prime : updated_b_prime } ) | |
| 277 | |
| 278 | |
| 279 | |
| 280 | |
| 281 # compiling a theano function that computes the mistakes that are made | |
| 282 # by the model on a minibatch | |
| 283 test_model = theano.function([x], encoder.cost) | |
| 284 | |
| 285 normalize = numpy.asarray(255, dtype=theano.config.floatX) | |
| 286 | |
| 287 | |
| 288 n_minibatches = len(train_batches) | |
| 289 | |
| 290 # early-stopping parameters | |
| 291 patience = 10000000 / batch_size # look as this many examples regardless | |
| 292 patience_increase = 2 # wait this much longer when a new best is | |
| 293 # found | |
| 294 improvement_threshold = 0.995 # a relative improvement of this much is | |
| 295 # considered significant | |
| 296 validation_frequency = n_minibatches # go through this many | |
| 297 # minibatche before checking the network | |
| 298 # on the validation set; in this case we | |
| 299 # check every epoch | |
| 300 | |
| 301 | |
| 302 best_params = None | |
| 303 best_validation_loss = float('inf') | |
| 304 best_iter = 0 | |
| 305 test_score = 0. | |
| 306 start_time = time.clock() | |
| 307 # have a maximum of `n_iter` iterations through the entire dataset | |
| 308 for iter in xrange(n_iter* n_minibatches): | |
| 309 | |
| 310 # get epoch and minibatch index | |
| 311 epoch = iter / n_minibatches | |
| 312 minibatch_index = iter % n_minibatches | |
| 313 | |
| 314 # get the minibatches corresponding to `iter` modulo | |
| 315 # `len(train_batches)` | |
| 316 x,y = train_batches[ minibatch_index ] | |
| 317 ''' | |
| 318 if iter == 0: | |
| 319 b = numpy.asarray(255, dtype=theano.config.floatX) | |
| 320 x = x / b | |
| 321 print x | |
| 322 print y | |
| 323 print x.__class__ | |
| 324 print x.shape | |
| 325 print x.dtype.name | |
| 326 print y.dtype.name | |
| 327 print x.min(), x.max() | |
| 328 ''' | |
| 329 | |
| 330 cost_ij = train_model(x/normalize) | |
| 331 | |
| 332 if (iter+1) % validation_frequency == 0: | |
| 333 # compute zero-one loss on validation set | |
| 334 this_validation_loss = 0. | |
| 335 for x,y in valid_batches: | |
| 336 # sum up the errors for each minibatch | |
| 337 this_validation_loss += test_model(x/normalize) | |
| 338 # get the average by dividing with the number of minibatches | |
| 339 this_validation_loss /= len(valid_batches) | |
| 340 | |
| 341 print('epoch %i, minibatch %i/%i, validation error %f ' % \ | |
| 342 (epoch, minibatch_index+1, n_minibatches, \ | |
| 343 this_validation_loss)) | |
| 344 | |
| 345 # save value in file | |
| 346 result_file.write(str(epoch) + ' ' + str(this_validation_loss)+ '\n') | |
| 347 | |
| 348 | |
| 349 # if we got the best validation score until now | |
| 350 if this_validation_loss < best_validation_loss: | |
| 351 | |
| 352 #improve patience if loss improvement is good enough | |
| 353 if this_validation_loss < best_validation_loss * \ | |
| 354 improvement_threshold : | |
| 355 patience = max(patience, iter * patience_increase) | |
| 356 | |
| 357 best_validation_loss = this_validation_loss | |
| 358 best_iter = iter | |
| 359 # test it on the test set | |
| 360 | |
| 361 test_score = 0. | |
| 362 for x,y in test_batches: | |
| 363 test_score += test_model(x/normalize) | |
| 364 test_score /= len(test_batches) | |
| 365 print((' epoch %i, minibatch %i/%i, test error of best ' | |
| 366 'model %f ') % | |
| 367 (epoch, minibatch_index+1, n_minibatches, | |
| 368 test_score)) | |
| 369 | |
| 370 if patience <= iter : | |
|
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371 print('iter (%i) is superior than patience(%i). break', (iter, patience)) |
| 190 | 372 break |
| 373 | |
| 374 | |
| 375 | |
| 376 end_time = time.clock() | |
| 377 print(('Optimization complete with best validation score of %f ,' | |
| 378 'with test performance %f ') % | |
| 379 (best_validation_loss, test_score)) | |
| 380 print ('The code ran for %f minutes' % ((end_time-start_time)/60.)) | |
| 381 | |
| 382 | |
| 383 result_file.close() | |
| 384 | |
| 385 return (best_validation_loss, test_score, (end_time-start_time)/60, best_iter) | |
| 386 | |
| 387 def sgd_optimization_mnist( learning_rate=0.01, \ | |
| 388 n_iter = 1, n_code_layer = 400, \ | |
| 389 complexity = 0.1): | |
| 390 """ | |
| 391 Demonstrate stochastic gradient descent optimization for a denoising autoencoder | |
| 392 | |
| 393 This is demonstrated on MNIST. | |
| 394 | |
| 395 :param learning_rate: learning rate used (factor for the stochastic | |
| 396 gradient | |
| 397 | |
| 398 :param pretraining_epochs: number of epoch to do pretraining | |
| 399 | |
| 400 :param pretrain_lr: learning rate to be used during pre-training | |
| 401 | |
| 402 :param n_iter: maximal number of iterations ot run the optimizer | |
| 403 | |
| 404 """ | |
| 405 #open file to save the validation and test curve | |
| 406 filename = 'lr_' + str(learning_rate) + 'ni_' + str(n_iter) + 'nc_' + str(n_code_layer) + \ | |
| 407 'c_' + str(complexity) + '.txt' | |
| 408 | |
| 409 result_file = open(filename, 'w') | |
| 410 | |
| 411 # Load the dataset | |
| 412 f = gzip.open('/u/lisa/HTML/deep/data/mnist/mnist.pkl.gz','rb') | |
| 413 train_set, valid_set, test_set = cPickle.load(f) | |
| 414 f.close() | |
| 415 | |
| 416 # make minibatches of size 20 | |
| 417 batch_size = 20 # sized of the minibatch | |
| 418 | |
| 419 # Dealing with the training set | |
| 420 # get the list of training images (x) and their labels (y) | |
| 421 (train_set_x, train_set_y) = train_set | |
| 422 # initialize the list of training minibatches with empty list | |
| 423 train_batches = [] | |
| 424 for i in xrange(0, len(train_set_x), batch_size): | |
| 425 # add to the list of minibatches the minibatch starting at | |
| 426 # position i, ending at position i+batch_size | |
| 427 # a minibatch is a pair ; the first element of the pair is a list | |
| 428 # of datapoints, the second element is the list of corresponding | |
| 429 # labels | |
| 430 train_batches = train_batches + \ | |
| 431 [(train_set_x[i:i+batch_size], train_set_y[i:i+batch_size])] | |
| 432 | |
| 433 # Dealing with the validation set | |
| 434 (valid_set_x, valid_set_y) = valid_set | |
| 435 # initialize the list of validation minibatches | |
| 436 valid_batches = [] | |
| 437 for i in xrange(0, len(valid_set_x), batch_size): | |
| 438 valid_batches = valid_batches + \ | |
| 439 [(valid_set_x[i:i+batch_size], valid_set_y[i:i+batch_size])] | |
| 440 | |
| 441 # Dealing with the testing set | |
| 442 (test_set_x, test_set_y) = test_set | |
| 443 # initialize the list of testing minibatches | |
| 444 test_batches = [] | |
| 445 for i in xrange(0, len(test_set_x), batch_size): | |
| 446 test_batches = test_batches + \ | |
| 447 [(test_set_x[i:i+batch_size], test_set_y[i:i+batch_size])] | |
| 448 | |
| 449 | |
| 450 ishape = (28,28) # this is the size of MNIST images | |
| 451 | |
| 452 # allocate symbolic variables for the data | |
| 453 x = T.fmatrix() # the data is presented as rasterized images | |
| 454 y = T.lvector() # the labels are presented as 1D vector of | |
| 455 # [long int] labels | |
| 456 | |
| 457 # construct the denoising autoencoder class | |
| 458 n_ins = 28*28 | |
|
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e12702b88a2d
removed one bug: the complexity value was not sent to the dA class
youssouf
parents:
190
diff
changeset
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459 encoder = dA(n_ins, n_code_layer, complexity, input = x.reshape((batch_size,n_ins))) |
| 190 | 460 |
| 461 # Train autoencoder | |
| 462 | |
| 463 # compute gradients of the layer parameters | |
| 464 gW = T.grad(encoder.cost, encoder.W) | |
| 465 gb = T.grad(encoder.cost, encoder.b) | |
| 466 gb_prime = T.grad(encoder.cost, encoder.b_prime) | |
| 467 # compute the updated value of the parameters after one step | |
| 468 updated_W = encoder.W - gW * learning_rate | |
| 469 updated_b = encoder.b - gb * learning_rate | |
| 470 updated_b_prime = encoder.b_prime - gb_prime * learning_rate | |
| 471 | |
| 472 # defining the function that evaluate the symbolic description of | |
| 473 # one update step | |
| 474 train_model = theano.function([x], encoder.cost, updates=\ | |
| 475 { encoder.W : updated_W, \ | |
| 476 encoder.b : updated_b, \ | |
| 477 encoder.b_prime : updated_b_prime } ) | |
| 478 | |
| 479 | |
| 480 | |
| 481 | |
| 482 # compiling a theano function that computes the mistakes that are made | |
| 483 # by the model on a minibatch | |
| 484 test_model = theano.function([x], encoder.cost) | |
| 485 | |
| 486 | |
| 487 | |
| 488 | |
| 489 n_minibatches = len(train_batches) | |
| 490 | |
| 491 # early-stopping parameters | |
| 492 patience = 10000# look as this many examples regardless | |
| 493 patience_increase = 2 # wait this much longer when a new best is | |
| 494 # found | |
| 495 improvement_threshold = 0.995 # a relative improvement of this much is | |
| 496 # considered significant | |
| 497 validation_frequency = n_minibatches # go through this many | |
| 498 # minibatche before checking the network | |
| 499 # on the validation set; in this case we | |
| 500 # check every epoch | |
| 501 | |
| 502 | |
| 503 best_params = None | |
| 504 best_validation_loss = float('inf') | |
| 505 best_iter = 0 | |
| 506 test_score = 0. | |
| 507 start_time = time.clock() | |
| 508 # have a maximum of `n_iter` iterations through the entire dataset | |
| 509 for iter in xrange(n_iter* n_minibatches): | |
| 510 | |
| 511 # get epoch and minibatch index | |
| 512 epoch = iter / n_minibatches | |
| 513 minibatch_index = iter % n_minibatches | |
| 514 | |
| 515 # get the minibatches corresponding to `iter` modulo | |
| 516 # `len(train_batches)` | |
| 517 x,y = train_batches[ minibatch_index ] | |
| 518 cost_ij = train_model(x) | |
| 519 | |
| 520 if (iter+1) % validation_frequency == 0: | |
| 521 # compute zero-one loss on validation set | |
| 522 this_validation_loss = 0. | |
| 523 for x,y in valid_batches: | |
| 524 # sum up the errors for each minibatch | |
| 525 this_validation_loss += test_model(x) | |
| 526 # get the average by dividing with the number of minibatches | |
| 527 this_validation_loss /= len(valid_batches) | |
| 528 | |
| 529 print('epoch %i, minibatch %i/%i, validation error %f ' % \ | |
| 530 (epoch, minibatch_index+1, n_minibatches, \ | |
| 531 this_validation_loss)) | |
| 532 | |
| 533 # save value in file | |
| 534 result_file.write(str(epoch) + ' ' + str(this_validation_loss)+ '\n') | |
| 535 | |
| 536 | |
| 537 # if we got the best validation score until now | |
| 538 if this_validation_loss < best_validation_loss: | |
| 539 | |
| 540 #improve patience if loss improvement is good enough | |
| 541 if this_validation_loss < best_validation_loss * \ | |
| 542 improvement_threshold : | |
| 543 patience = max(patience, iter * patience_increase) | |
| 544 | |
| 545 best_validation_loss = this_validation_loss | |
| 546 best_iter = iter | |
| 547 # test it on the test set | |
| 548 | |
| 549 test_score = 0. | |
| 550 for x,y in test_batches: | |
| 551 test_score += test_model(x) | |
| 552 test_score /= len(test_batches) | |
| 553 print((' epoch %i, minibatch %i/%i, test error of best ' | |
| 554 'model %f ') % | |
| 555 (epoch, minibatch_index+1, n_minibatches, | |
| 556 test_score)) | |
| 557 | |
| 558 if patience <= iter : | |
| 559 print('iter (%i) is superior than patience(%i). break', iter, patience) | |
| 560 break | |
| 561 | |
| 562 | |
| 563 end_time = time.clock() | |
| 564 print(('Optimization complete with best validation score of %f ,' | |
| 565 'with test performance %f ') % | |
| 566 (best_validation_loss, test_score)) | |
| 567 print ('The code ran for %f minutes' % ((end_time-start_time)/60.)) | |
| 568 | |
| 569 | |
| 570 result_file.close() | |
| 571 | |
| 572 return (best_validation_loss, test_score, (end_time-start_time)/60, best_iter) | |
| 573 | |
| 574 | |
| 575 def experiment(state,channel): | |
| 576 | |
| 577 (best_validation_loss, test_score, minutes_trained, iter) = \ | |
| 578 sgd_optimization_mnist(state.learning_rate, state.n_iter, state.n_code_layer, | |
| 579 state.complexity) | |
| 580 | |
| 581 state.best_validation_loss = best_validation_loss | |
| 582 state.test_score = test_score | |
| 583 state.minutes_trained = minutes_trained | |
| 584 state.iter = iter | |
| 585 | |
| 586 return channel.COMPLETE | |
| 587 | |
| 588 def experiment_nist(state,channel): | |
| 589 | |
| 590 (best_validation_loss, test_score, minutes_trained, iter) = \ | |
| 591 sgd_optimization_nist(state.learning_rate, state.n_iter, state.n_code_layer, | |
| 592 state.complexity) | |
| 593 | |
| 594 state.best_validation_loss = best_validation_loss | |
| 595 state.test_score = test_score | |
| 596 state.minutes_trained = minutes_trained | |
| 597 state.iter = iter | |
| 598 | |
| 599 return channel.COMPLETE | |
| 600 | |
| 601 | |
| 602 if __name__ == '__main__': | |
| 603 | |
| 604 sgd_optimization_nist() | |
| 605 | |
| 606 |