diff options
Diffstat (limited to 'model.py')
| -rw-r--r-- | model.py | 33 |
1 files changed, 20 insertions, 13 deletions
@@ -23,6 +23,7 @@ from blocks.extensions.saveload import Dump, LoadFromDump from blocks.extensions.monitoring import DataStreamMonitoring import data +import transformers n_dow = 7 # number of division for dayofweek/dayofmonth/hourofday n_dom = 31 @@ -30,9 +31,8 @@ n_hour = 24 n_clients = 57106 n_stands = 63 -n_embed = n_clients + n_stands # embeddings capturing local parameters -n_begin_pts = 5 # how many points we consider at the beginning and end of the known trajectory +n_begin_end_pts = 5 # how many points we consider at the beginning and end of the known trajectory n_end_pts = 5 dim_embed = 50 @@ -45,19 +45,22 @@ def main(): # The input and the targets x_firstk = tensor.matrix('first_k') x_lastk = tensor.matrix('last_k') - x_client = tensor.lmatrix('client') - y = tensor.vector('targets') + x_client = tensor.lmatrix('origin_call') + x_stand = tensor.lmatrix('origin_stand') + y = tensor.vector('destination') # Define the model - client_embed_table = LookupTable(length=n_clients, dim=dim_embed, name='lookup') + client_embed_table = LookupTable(length=n_clients+1, dim=dim_embed, name='client_lookup') + stand_embed_table = LookupTable(length=n_stands+1, dim=dim_embed, name='stand_lookup') hidden_layer = MLP(activations=[Rectifier()], - dims=[(n_begin_pts + n_end_pts) * 2 + dim_embed, dim_hidden]) + dims=[n_begin_end_pts * 2 * 2 + dim_embed + dim_embed, dim_hidden]) output_layer = Linear(input_dim=dim_hidden, output_dim=2) # Create the Theano variables client_embed = client_embed_table.apply(x_client).flatten(ndim=2) - inputs = tensor.concatenate([x_firstk, x_lastk, client_embed], axis=1) + stand_embed = stand_embed_table.apply(x_stand).flatten(ndim=2) + inputs = tensor.concatenate([x_firstk, x_lastk, client_embed, stand_embed], axis=1) hidden = hidden_layer.apply(inputs) outputs = output_layer.apply(hidden) @@ -77,13 +80,17 @@ def main(): # Load the training and test data train = data.train_data - stream = DataStream(train) - train_stream = Batch(stream, iteration_scheme=ConstantScheme(batch_size)) + train = DataStream(train) + train = transformers.add_extremities(train, n_begin_end_pts) + train = transformers.add_destination(train) + train_stream = Batch(train, iteration_scheme=ConstantScheme(batch_size)) + + valid = data.valid_data + valid = DataStream(valid) + valid = transformers.add_extremities(valid, n_begin_end_pts) + valid = transformers.add_destination(valid) + valid_stream = Batch(valid, iteration_scheme=ConstantScheme(batch_size)) - # valid = data.valid_data - # stream = DataStream(valid) - # valid_stream = Batch(stream, iteration_scheme=ConstantScheme(batch_size)) - valid_stream = train_stream # Training cg = ComputationGraph(cost) |