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comparison pylearn/algorithms/mcRBM.py @ 1270:d38cb039c662

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debugging mcRBM
author James Bergstra <bergstrj@iro.umontreal.ca>
date Fri, 03 Sep 2010 15:05:31 -0400
parents 075c193afd1b
children ba25c6e4f55d
comparison
equal deleted inserted replaced
1269:c24c98da5615 1270:d38cb039c662
326 """ 326 """
327 if not hasattr(rng, 'randn'): 327 if not hasattr(rng, 'randn'):
328 rng = np.random.RandomState(rng) 328 rng = np.random.RandomState(rng)
329 if n_visible is None: 329 if n_visible is None:
330 n_visible = self.n_visible_units() 330 n_visible = self.n_visible_units()
331 rval = HMC_sampler( 331 rval = HMC_sampler.new_from_shared_positions(
332 positions = [shared( 332 shared_positions = shared(
333 rng.randn( 333 rng.randn(
334 n_particles, 334 n_particles,
335 n_visible).astype(floatX), 335 n_visible).astype(floatX),
336 name='particles')], 336 name='particles'),
337 energy_fn=self.free_energy_given_v, 337 energy_fn=self.free_energy_given_v,
338 seed=int(rng.randint(2**30))) 338 seed=int(rng.randint(2**30)))
339 return rval 339 return rval
340
341 def as_feedforward_layer(self, v):
342 return dict(
343 outputs = self.expected_h_g_given_v(v),
344 params = [self.U, self.W, self.b, self.c],
345 )
340 346
341 @classmethod 347 @classmethod
342 def alloc(cls, n_I, n_K, n_J, rng = 8923402190): 348 def alloc(cls, n_I, n_K, n_J, rng = 8923402190):
343 """ 349 """
344 Return a MeanCovRBM instance with randomly-initialized parameters. 350 Return a MeanCovRBM instance with randomly-initialized parameters.
381 387
382 def normalize_U(self, new_U): 388 def normalize_U(self, new_U):
383 #TODO: write the docstring 389 #TODO: write the docstring
384 U_norms = TT.sqrt((new_U**2).sum(axis=0)) 390 U_norms = TT.sqrt((new_U**2).sum(axis=0))
385 new_normVF = .95 * self.normVF + .05 * TT.mean(U_norms) 391 new_normVF = .95 * self.normVF + .05 * TT.mean(U_norms)
386 return new_U * this_normVF / U_norms), new_normVF 392 return (new_U * this_normVF / U_norms), new_normVF
387 393
388 def contrastive_grads(self, visible_batch, params=None): 394 def contrastive_grads(self, visible_batch, params=None):
389 if params is not None: 395 if params is not None:
390 params = self.rbm.params 396 params = self.rbm.params
391 return contrastive_grad( 397 return contrastive_grad(
392 free_energy_fn=rbm.free_energy_given_v, 398 free_energy_fn=self.rbm.free_energy_given_v,
393 pos_v=visible_batch, 399 pos_v=visible_batch,
394 neg_v=self.sampler.positions, 400 neg_v=self.sampler.positions,
395 params=params, 401 params=params,
396 other_cost=(l1(self.rbm.U)+l1(self.rbm.W)) * self.l1_penalty) 402 other_cost=(l1(self.rbm.U)+l1(self.rbm.W)) * self.l1_penalty)
397 403