diff options
| -rw-r--r-- | data/transformers.py | 1 | ||||
| -rw-r--r-- | model/mlp.py | 5 | ||||
| -rwxr-xr-x | train.py | 18 |
3 files changed, 17 insertions, 7 deletions
diff --git a/data/transformers.py b/data/transformers.py index 239d957..f994488 100644 --- a/data/transformers.py +++ b/data/transformers.py @@ -10,6 +10,7 @@ from fuel.transformers import Batch, Mapping, SortMapping, Transformer, Unpack import data +fuel.config.default_seed = 123 def at_least_k(k, v, pad_at_begin, is_longitude): if len(v) == 0: diff --git a/model/mlp.py b/model/mlp.py index a6f3991..7e7d092 100644 --- a/model/mlp.py +++ b/model/mlp.py @@ -1,5 +1,8 @@ from theano import tensor +import fuel +import blocks + from fuel.transformers import Batch, MultiProcessing from fuel.streams import DataStream from fuel.schemes import ConstantScheme, ShuffledExampleScheme @@ -11,6 +14,8 @@ from data.hdf5 import TaxiDataset, TaxiStream from data.cut import TaxiTimeCutScheme from model import ContextEmbedder +blocks.config.default_seed = 123 +fuel.config.default_seed = 123 class FFMLP(Initializable): def __init__(self, config, output_layer=None, **kwargs): @@ -87,6 +87,9 @@ if __name__ == "__main__": else: step_rule = AdaDelta() + logger.info("Fuel seed: %d" % fuel.config.default_seed) + logger.info("Blocks seed: %d" % blocks.config.default_seed) + params = cg.parameters algorithm = GradientDescent( cost=cost, @@ -107,13 +110,14 @@ if __name__ == "__main__": prefix='valid', every_n_batches=1000), Printing(every_n_batches=1000), - - # SaveLoadParams(dump_path, cg, - # before_training=True, # before training -> load params - # every_n_batches=1000, # every N batches -> save params - # after_epoch=True, # after epoch -> save params - # after_training=True, # after training -> save params - # ), + # FinishAfter(every_n_batches=10), + + SaveLoadParams(dump_path, cg, + before_training=True, # before training -> load params + every_n_batches=1000, # every N batches -> save params + after_epoch=True, # after epoch -> save params + after_training=True, # after training -> save params + ), RunOnTest(model_name, model, |