def sample(self, state_below=None, state_above=None, layer_above=None, theano_rng=None): if theano_rng is None: raise ValueError( "theano_rng is required; it just defaults to None so that it may appear after layer_above / state_above in the list." ) if state_above is not None: msg = layer_above.downward_message(state_above) else: msg = None if self.requires_reformat: state_below = self.input_space.format_as(state_below, self.desired_space) z = self.transformer.lmul(state_below) + self.b p, h, p_sample, h_sample = max_pool_channels(z, self.pool_size, msg, theano_rng) return p_sample, h_sample
def mf_update(self, state_below, state_above, layer_above = None, double_weights = False, iter_name = None): self.input_space.validate(state_below) if self.requires_reformat: if not isinstance(state_below, tuple): for sb in get_debug_values(state_below): if sb.shape[0] != self.dbm.batch_size: raise ValueError("self.dbm.batch_size is %d but got shape of %d" % (self.dbm.batch_size, sb.shape[0])) assert reduce(lambda x,y: x * y, sb.shape[1:]) == self.input_dim state_below = self.input_space.format_as(state_below, self.desired_space) if iter_name is None: iter_name = 'anon' if state_above is not None: assert layer_above is not None msg = layer_above.downward_message(state_above) msg.name = 'msg_from_'+layer_above.layer_name+'_to_'+self.layer_name+'['+iter_name+']' else: msg = None if double_weights: state_below = 2. * state_below state_below.name = self.layer_name + '_'+iter_name + '_2state' z = self.transformer.lmul(state_below) + self.b if self.layer_name is not None and iter_name is not None: z.name = self.layer_name + '_' + iter_name + '_z' p,h = max_pool_channels(z, self.pool_size, msg) p.name = self.layer_name + '_p_' + iter_name h.name = self.layer_name + '_h_' + iter_name return p, h
def init_mf_state(self): raise NotImplementedError("This is just a copy-paste of BVMP") # work around theano bug with broadcasted vectors z = T.alloc(0., self.dbm.batch_size, self.detector_layer_dim).astype(self.b.dtype) + \ self.b.dimshuffle('x', 0) rval = max_pool_channels(z = z, pool_size = self.pool_size) return rval
def make_state(self, num_examples, numpy_rng): """ Returns a shared variable containing an actual state (not a mean field state) for this variable. """ t1 = time.time() empty_input = self.h_space.get_origin_batch(num_examples) h_state = sharedX(empty_input) default_z = T.zeros_like(h_state) + self.b theano_rng = MRG_RandomStreams(numpy_rng.randint(2 ** 16)) p_exp, h_exp, p_sample, h_sample = max_pool_channels( z = default_z, pool_size = self.pool_size, theano_rng = theano_rng) assert h_sample.dtype == default_z.dtype p_state = sharedX( self.output_space.get_origin_batch( num_examples)) t2 = time.time() f = function([], updates = { p_state : p_sample, h_state : h_sample }) t3 = time.time() f() t4 = time.time() print str(self)+'.make_state took',t4-t1 print '\tcompose time:',t2-t1 print '\tcompile time:',t3-t2 print '\texecute time:',t4-t3 p_state.name = 'p_sample_shared' h_state.name = 'h_sample_shared' return p_state, h_state
def mf_update(self, state_below, state_above, layer_above=None, double_weights=False, iter_name=None): self.input_space.validate(state_below) if self.requires_reformat: if not isinstance(state_below, tuple): for sb in get_debug_values(state_below): if sb.shape[0] != self.dbm.batch_size: raise ValueError( "self.dbm.batch_size is %d but got shape of %d" % (self.dbm.batch_size, sb.shape[0])) assert reduce(lambda x, y: x * y, sb.shape[1:]) == self.input_dim state_below = self.input_space.format_as(state_below, self.desired_space) if iter_name is None: iter_name = 'anon' if state_above is not None: assert layer_above is not None msg = layer_above.downward_message(state_above) msg.name = 'msg_from_' + layer_above.layer_name + '_to_' + self.layer_name + '[' + iter_name + ']' else: msg = None if double_weights: state_below = 2. * state_below state_below.name = self.layer_name + '_' + iter_name + '_2state' z = self.transformer.lmul(state_below) + self.b if self.layer_name is not None and iter_name is not None: z.name = self.layer_name + '_' + iter_name + '_z' p, h = max_pool_channels(z, self.pool_size, msg) p.name = self.layer_name + '_p_' + iter_name h.name = self.layer_name + '_h_' + iter_name return p, h
def sample(self, state_below = None, state_above = None, layer_above = None, theano_rng = None): if theano_rng is None: raise ValueError("theano_rng is required; it just defaults to None so that it may appear after layer_above / state_above in the list.") if state_above is not None: msg = layer_above.downward_message(state_above) else: msg = None if self.requires_reformat: state_below = self.input_space.format_as(state_below, self.desired_space) z = self.transformer.lmul(state_below) + self.b p, h, p_sample, h_sample = max_pool_channels(z, self.pool_size, msg, theano_rng) return p_sample, h_sample
def fprop(self, state_below): self.input_space.validate(state_below) if self.requires_reformat: if not isinstance(state_below, tuple): for sb in get_debug_values(state_below): if sb.shape[0] != self.dbm.batch_size: raise ValueError("self.dbm.batch_size is %d but got shape of %d" % (self.dbm.batch_size, sb.shape[0])) assert reduce(lambda x,y: x * y, sb.shape[1:]) == self.input_dim state_below = self.input_space.format_as(state_below, self.desired_space) z = self.transformer.lmul(state_below) + self.b if self.layer_name is not None: z.name = self.layer_name + '_z' p,h = max_pool_channels(z, self.pool_size) p.name = self.layer_name + '_p_' return p
def make_state(self, num_examples, numpy_rng): """ Returns a shared variable containing an actual state (not a mean field state) for this variable. """ t1 = time.time() empty_input = self.h_space.get_origin_batch(num_examples) h_state = sharedX(empty_input) default_z = T.zeros_like(h_state) + self.b theano_rng = MRG_RandomStreams(numpy_rng.randint(2**16)) p_exp, h_exp, p_sample, h_sample = max_pool_channels( z=default_z, pool_size=self.pool_size, theano_rng=theano_rng) assert h_sample.dtype == default_z.dtype p_state = sharedX(self.output_space.get_origin_batch(num_examples)) t2 = time.time() f = function([], updates={p_state: p_sample, h_state: h_sample}) t3 = time.time() f() t4 = time.time() print str(self) + '.make_state took', t4 - t1 print '\tcompose time:', t2 - t1 print '\tcompile time:', t3 - t2 print '\texecute time:', t4 - t3 p_state.name = 'p_sample_shared' h_state.name = 'h_sample_shared' return p_state, h_state