diff options
Diffstat (limited to 'model.py')
| -rw-r--r-- | model.py | 27 |
1 files changed, 19 insertions, 8 deletions
@@ -46,20 +46,30 @@ if __name__ == "__main__": def setup_stream(): # Load the training and test data - train = H5PYDataset('/data/lisatmp3/simonet/taxi/data.hdf5', which_set='train', subset=slice(0, config.train_size - config.n_valid), load_in_memory=True) + train = H5PYDataset('/data/lisatmp3/simonet/taxi/data.hdf5', + which_set='train', + subset=slice(0, config.train_size - config.n_valid), + load_in_memory=True) train = DataStream(train, iteration_scheme=SequentialExampleScheme(config.train_size - config.n_valid)) train = transformers.add_first_k(config.n_begin_end_pts, train) train = transformers.add_random_k(config.n_begin_end_pts, train) train = transformers.add_destination(train) - train = transformers.Select(train, ('origin_stand', 'origin_call', 'first_k_latitude', 'last_k_latitude', 'first_k_longitude', 'last_k_longitude', 'destination_latitude', 'destination_longitude')) + train = transformers.Select(train, ('origin_stand', 'origin_call', 'first_k_latitude', + 'last_k_latitude', 'first_k_longitude', 'last_k_longitude', + 'destination_latitude', 'destination_longitude')) train_stream = Batch(train, iteration_scheme=ConstantScheme(config.batch_size)) - valid = H5PYDataset('/data/lisatmp3/simonet/taxi/data.hdf5', which_set='train', subset=slice(config.train_size - config.n_valid, config.train_size), load_in_memory=True) + valid = H5PYDataset('/data/lisatmp3/simonet/taxi/data.hdf5', + which_set='train', + subset=slice(config.train_size - config.n_valid, config.train_size), + load_in_memory=True) valid = DataStream(valid, iteration_scheme=SequentialExampleScheme(config.n_valid)) valid = transformers.add_first_k(config.n_begin_end_pts, valid) valid = transformers.add_last_k(config.n_begin_end_pts, valid) valid = transformers.add_destination(valid) - valid = transformers.Select(valid, ('origin_stand', 'origin_call', 'first_k_latitude', 'last_k_latitude', 'first_k_longitude', 'last_k_longitude', 'destination_latitude', 'destination_longitude')) + valid = transformers.Select(valid, ('origin_stand', 'origin_call', 'first_k_latitude', + 'last_k_latitude', 'first_k_longitude', 'last_k_longitude', + 'destination_latitude', 'destination_longitude')) valid_stream = Batch(valid, iteration_scheme=ConstantScheme(1000)) return (train_stream, valid_stream) @@ -68,15 +78,15 @@ def main(): # The input and the targets x_firstk_latitude = (tensor.matrix('first_k_latitude') - data.porto_center[0]) / data.data_std[0] x_firstk_longitude = (tensor.matrix('first_k_longitude') - data.porto_center[1]) / data.data_std[1] - x_firstk = tensor.concatenate((x_firstk_latitude, x_firstk_longitude), axis=1) x_lastk_latitude = (tensor.matrix('last_k_latitude') - data.porto_center[0]) / data.data_std[0] x_lastk_longitude = (tensor.matrix('last_k_longitude') - data.porto_center[1]) / data.data_std[1] - x_lastk = tensor.concatenate((x_lastk_latitude, x_lastk_longitude), axis=1) x_client = tensor.lvector('origin_call') x_stand = tensor.lvector('origin_stand') - y = tensor.concatenate((tensor.vector('destination_latitude')[:, None], tensor.vector('destination_longitude')[:, None]), axis=1) + + y = tensor.concatenate((tensor.vector('destination_latitude')[:, None], + tensor.vector('destination_longitude')[:, None]), axis=1) # Define the model client_embed_table = LookupTable(length=config.n_clients+1, dim=config.dim_embed, name='client_lookup') @@ -87,7 +97,8 @@ def main(): # Create the Theano variables client_embed = client_embed_table.apply(x_client).flatten(ndim=2) stand_embed = stand_embed_table.apply(x_stand).flatten(ndim=2) - inputs = tensor.concatenate([x_firstk, x_lastk, client_embed, stand_embed], + inputs = tensor.concatenate([x_firstk_latitude, x_firstk_longitude, + x_lastk_latitude, x_lastk_longitude, client_embed, stand_embed], axis=1) # inputs = theano.printing.Print("inputs")(inputs) outputs = mlp.apply(inputs) |