taxi

Winning entry to the Kaggle taxi competition
git clone https://esimon.eu/repos/taxi.git
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commit e88014a6fa95eaa9bac6094a89ac5179776afe74
parent a6fdddce3f94913a0f8fadfcf8c74005e76c192e
Author: √Čtienne Simon <esimon@esimon.eu>
Date:   Fri, 24 Jul 2015 16:15:17 -0400

Use SegregatedBidirectional for bidirectional memory network

Diffstat:
Mmodel/memory_network_bidir.py | 27+++++++++++++++++----------
1 file changed, 17 insertions(+), 10 deletions(-)

diff --git a/model/memory_network_bidir.py b/model/memory_network_bidir.py @@ -13,6 +13,8 @@ from model import ContextEmbedder from memory_network import StreamRecurrent as Stream from memory_network import MemoryNetworkBase +from bidirectional import SegregatedBidirectional + class RecurrentEncoder(Initializable): def __init__(self, config, output_dim, activation, **kwargs): @@ -21,11 +23,12 @@ class RecurrentEncoder(Initializable): self.config = config self.context_embedder = ContextEmbedder(config) - self.rec = Bidirectional(LSTM(dim=config.rec_state_dim, name='encoder_recurrent')) - self.fork = Fork( - [name for name in self.rec.prototype.apply.sequences - if name != 'mask'], - prototype=Linear()) + self.rec = SegregatedBidirectional(LSTM(dim=config.rec_state_dim, name='encoder_recurrent')) + + self.fwd_fork = Fork([name for name in self.rec.prototype.apply.sequences if name!='mask'], + prototype=Linear(), name='fwd_fork') + self.bkwd_fork = Fork([name for name in self.rec.prototype.apply.sequences if name!='mask'], + prototype=Linear(), name='bkwd_fork') rto_in = config.rec_state_dim * 2 + sum(x[2] for x in config.dim_embeddings) self.rec_to_output = MLP( @@ -33,15 +36,16 @@ class RecurrentEncoder(Initializable): dims=[rto_in] + config.dim_hidden + [output_dim], name='encoder_rto') - self.children = [self.context_embedder, self.rec, self.fork, self.rec_to_output] + self.children = [self.context_embedder, self.rec, self.fwd_fork, self.bkwd_fork, self.rec_to_output] self.rec_inputs = ['latitude', 'longitude', 'latitude_mask'] self.inputs = self.context_embedder.inputs + self.rec_inputs def _push_allocation_config(self): - self.fork.input_dim = 2 - self.fork.output_dims = [ self.rec.children[0].get_dim(name) - for name in self.fork.output_names ] + for i, fork in enumerate([self.fwd_fork, self.bkwd_fork]): + fork.input_dim = 2 + fork.output_dims = [ self.rec.children[i].get_dim(name) + for name in fork.output_names ] def _push_initialization_config(self): for brick in self.children: @@ -56,7 +60,10 @@ class RecurrentEncoder(Initializable): rec_in = tensor.concatenate((latitude[:, :, None], longitude[:, :, None]), axis=2) - path = self.rec.apply(self.fork.apply(rec_in), mask=latitude_mask)[0] + path = self.rec.apply(merge(self.fwd_fork.apply(rec_in, as_dict=True), + {'mask': latitude_mask}), + merge(self.bkwd_fork.apply(rec_in, as_dict=True), + {'mask': latitude_mask}))[0] last_id = tensor.cast(latitude_mask.sum(axis=0) - 1, dtype='int64')