Mercurial > pylearn
changeset 1447:fbe470217937
Use .get_value() and .set_value() of shared instead of the .value property
| author | Pascal Lamblin <lamblinp@iro.umontreal.ca> |
|---|---|
| date | Wed, 16 Mar 2011 20:20:02 -0400 |
| parents | 6e50d209b5f1 |
| children | 8110ca3cec3f |
| files | pylearn/sampling/hmc.py pylearn/sampling/mcmc.py pylearn/sampling/tests/test_hmc.py pylearn/sampling/tests/test_mcmc.py pylearn/shared/layers/tests/test_kouh2008.py pylearn/shared/layers/tests/test_sigmoidal_layer.py |
| diffstat | 6 files changed, 23 insertions(+), 21 deletions(-) [+] |
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--- a/pylearn/sampling/hmc.py Tue Mar 08 12:50:37 2011 -0500 +++ b/pylearn/sampling/hmc.py Wed Mar 16 20:20:02 2011 -0400 @@ -237,7 +237,7 @@ # allocate shared vars if shared_positions_shape==None: - shared_positions_shape = shared_positions.value.shape + shared_positions_shape = shared_positions.get_value(borrow=True).shape batchsize = shared_positions_shape[0] stepsize = shared(numpy.asarray(initial_stepsize).astype(theano.config.floatX), 'hmc_stepsize') @@ -289,7 +289,7 @@ `borrow=True`. """ self.simulate() - return self.positions.value.copy() + return self.positions.get_value(borrow=False) def updates(self): """Returns the update expressions required to simulate the Markov Chain
--- a/pylearn/sampling/mcmc.py Tue Mar 08 12:50:37 2011 -0500 +++ b/pylearn/sampling/mcmc.py Wed Mar 16 20:20:02 2011 -0400 @@ -55,7 +55,7 @@ The len of this vector is the batchsize. """ - batchsize = positions[0].value.shape[0] + batchsize = positions[0].get_value(borrow=True).shape[0] self.s_rng = TT.shared_randomstreams.RandomStreams(seed) self.positions = positions self.prev_energy = shared(np.zeros(batchsize) + float('inf')) @@ -64,7 +64,7 @@ s_stepsize = TT.scalar('stepsize') - new_positions = [p + s_stepsize * self.s_rng.normal(size=p.value.shape) + new_positions = [p + s_stepsize * self.s_rng.normal(size=p.get_value(borrow=True).shape) for p in self.positions] # accept-reject according to Metropolis-Hastings @@ -90,7 +90,7 @@ self.stepsize = min(self.stepsize*self.stepsize_inc,self.stepsize_max) def get_position(self): - return [q.value for q in self.positions] + return [q.get_value(borrow=True) for q in self.positions] def draw(self, n_steps=None): """Return the current sample in the Markov chain as a list of numpy arrays
--- a/pylearn/sampling/tests/test_hmc.py Tue Mar 08 12:50:37 2011 -0500 +++ b/pylearn/sampling/tests/test_hmc.py Wed Mar 16 20:20:02 2011 -0400 @@ -22,16 +22,16 @@ position = shared(rng.randn(batchsize, 2).astype(theano.config.floatX)) sampler = sampler_cls(position, gaussian_energy) - print 'initial position', position.value - print 'initial stepsize', sampler.stepsize.value + print 'initial position', position.get_value(borrow=True) + print 'initial stepsize', sampler.stepsize.get_value(borrow=True) # DRAW SAMPLES samples = [sampler.draw() for r in xrange(burnin)] #burn-in samples = np.asarray([sampler.draw() for r in xrange(n_samples)]) - assert sampler.avg_acceptance_rate.value > 0 - assert sampler.avg_acceptance_rate.value < 1 + assert sampler.avg_acceptance_rate.get_value() > 0 + assert sampler.avg_acceptance_rate.get_value() < 1 # TEST THAT THEY ARE FROM THE RIGHT DISTRIBUTION @@ -42,8 +42,8 @@ #assert np.all(abs(mu - samples.mean(axis=0)) < 1) - print 'final stepsize', sampler.stepsize.value - print 'final acceptance_rate', sampler.avg_acceptance_rate.value + print 'final stepsize', sampler.stepsize.get_value() + print 'final acceptance_rate', sampler.avg_acceptance_rate.get_value() print 'target cov', cov s = samples[:,0,:] @@ -59,7 +59,7 @@ def test_hmc(): print ('HMC') sampler = _sampler_on_2d_gaussian(HMC_sampler.new_from_shared_positions, burnin=3000/20, n_samples=90000/20) - assert abs(sampler.avg_acceptance_rate.value - sampler.target_acceptance_rate) < .1 - assert sampler.stepsize.value >= sampler.stepsize_min - assert sampler.stepsize.value <= sampler.stepsize_max + assert abs(sampler.avg_acceptance_rate.get_value() - sampler.target_acceptance_rate) < .1 + assert sampler.stepsize.get_value() >= sampler.stepsize_min + assert sampler.stepsize.get_value() <= sampler.stepsize_max
--- a/pylearn/sampling/tests/test_mcmc.py Tue Mar 08 12:50:37 2011 -0500 +++ b/pylearn/sampling/tests/test_mcmc.py Wed Mar 16 20:20:02 2011 -0400 @@ -22,7 +22,7 @@ position = shared(rng.randn(batchsize, 2).astype(theano.config.floatX)) sampler = sampler_cls([position], gaussian_energy) - print 'initial position', position.value + print 'initial position', position.get_value(borrow=True) print 'initial stepsize', sampler.stepsize # DRAW SAMPLES
--- a/pylearn/shared/layers/tests/test_kouh2008.py Tue Mar 08 12:50:37 2011 -0500 +++ b/pylearn/shared/layers/tests/test_kouh2008.py Wed Mar 16 20:20:02 2011 -0400 @@ -60,7 +60,9 @@ out = LogisticRegression.new(layer.output, n_out, 2) cost = out.nll(y).sum() #joint optimization except for one of the linear filters - out.w.value += 0.1 * rng.rand(*out.w.value.shape) + out.w.set_value((out.w.get_value(borrow=True) + + 0.1 * rng.rand(*out.w.get_value(borrow=True).shape)), + borrow=True) params = layer.params[:-2] mode = None updates = [(p, p - numpy.asarray(0.001, dtype=dtype)*gp) for p,gp in zip(params, tensor.grad(cost, params)) ]
--- a/pylearn/shared/layers/tests/test_sigmoidal_layer.py Tue Mar 08 12:50:37 2011 -0500 +++ b/pylearn/shared/layers/tests/test_sigmoidal_layer.py Wed Mar 16 20:20:02 2011 -0400 @@ -24,8 +24,8 @@ updates = [(p, p - numpy.asarray(0.01, dtype=dtype)*gp) for p,gp in zip(params, tensor.grad(cost, params)) ] f = pfunc([x, y], cost, updates=updates) - w0 = layer.w.value.copy() - b0 = layer.b.value.copy() + w0 = layer.w.get_value(borrow=False) + b0 = layer.b.get_value(borrow=False) xval = numpy.asarray(rng.rand(bsize, n_in), dtype=dtype) yval = numpy.asarray(rng.randint(0,2,bsize), dtype='int64') @@ -35,7 +35,7 @@ print i, 'rval', fN assert f0 > 6 - assert fN < 2 + assert fN < 2 - assert numpy.all(w0 != layer.w.value) - assert numpy.all(b0 != layer.b.value) + assert numpy.all(w0 != layer.w.get_value(borrow=True)) + assert numpy.all(b0 != layer.b.get_value(borrow=True))