diff options
| -rw-r--r-- | config/simple_mlp_0.py (renamed from config/model_0.py) | 2 | ||||
| -rw-r--r-- | model/__init__.py | 0 | ||||
| -rw-r--r-- | model/simple_mlp.py | 69 | ||||
| -rw-r--r-- | train.py (renamed from model.py) | 67 |
4 files changed, 76 insertions, 62 deletions
diff --git a/config/model_0.py b/config/simple_mlp_0.py index c4985b2..61ddbfd 100644 --- a/config/model_0.py +++ b/config/simple_mlp_0.py @@ -1,3 +1,5 @@ +import model.simple_mlp as model + n_dow = 7 # number of division for dayofweek/dayofmonth/hourofday n_dom = 31 n_hour = 24 diff --git a/model/__init__.py b/model/__init__.py new file mode 100644 index 0000000..e69de29 --- /dev/null +++ b/model/__init__.py diff --git a/model/simple_mlp.py b/model/simple_mlp.py new file mode 100644 index 0000000..896ccd3 --- /dev/null +++ b/model/simple_mlp.py @@ -0,0 +1,69 @@ +from blocks.bricks import MLP, Rectifier, Linear, Sigmoid, Identity +from blocks.bricks.lookup import LookupTable + +from blocks.initialization import IsotropicGaussian, Constant + +from theano import tensor + +import data +import hdist + +class Model(object): + def __init__(self, config): + # 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_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_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) + + # Define the model + client_embed_table = LookupTable(length=data.n_train_clients+1, dim=config.dim_embed, name='client_lookup') + stand_embed_table = LookupTable(length=data.n_stands+1, dim=config.dim_embed, name='stand_lookup') + mlp = MLP(activations=[Rectifier() for _ in config.dim_hidden] + [Identity()], + dims=[config.dim_input] + config.dim_hidden + [config.dim_output]) + + # Create the Theano variables + client_embed = client_embed_table.apply(x_client) + stand_embed = stand_embed_table.apply(x_stand) + 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) + + # Normalize & Center + # outputs = theano.printing.Print("normal_outputs")(outputs) + outputs = data.data_std * outputs + data.porto_center + + # outputs = theano.printing.Print("outputs")(outputs) + # y = theano.printing.Print("y")(y) + + outputs.name = 'outputs' + + # Calculate the cost + cost = hdist.erdist(outputs, y).mean() + cost.name = 'cost' + hcost = hdist.hdist(outputs, y).mean() + hcost.name = 'hcost' + + # Initialization + client_embed_table.weights_init = IsotropicGaussian(0.001) + stand_embed_table.weights_init = IsotropicGaussian(0.001) + mlp.weights_init = IsotropicGaussian(0.01) + mlp.biases_init = Constant(0.001) + + client_embed_table.initialize() + stand_embed_table.initialize() + mlp.initialize() + + self.cost = cost + self.hcost = hcost + self.outputs = outputs @@ -15,10 +15,6 @@ from theano.ifelse import ifelse from blocks.filter import VariableFilter -from blocks.bricks import MLP, Rectifier, Linear, Sigmoid, Identity -from blocks.bricks.lookup import LookupTable - -from blocks.initialization import IsotropicGaussian, Constant from blocks.model import Model from fuel.datasets.hdf5 import H5PYDataset @@ -88,64 +84,11 @@ def setup_test_stream(): 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_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_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) - - # x_firstk_latitude = theano.printing.Print("x_firstk_latitude")(x_firstk_latitude) - # x_firstk_longitude = theano.printing.Print("x_firstk_longitude")(x_firstk_longitude) - # x_lastk_latitude = theano.printing.Print("x_lastk_latitude")(x_lastk_latitude) - # x_lastk_longitude = theano.printing.Print("x_lastk_longitude")(x_lastk_longitude) - - # Define the model - client_embed_table = LookupTable(length=data.n_train_clients+1, dim=config.dim_embed, name='client_lookup') - stand_embed_table = LookupTable(length=data.n_stands+1, dim=config.dim_embed, name='stand_lookup') - mlp = MLP(activations=[Rectifier() for _ in config.dim_hidden] + [Identity()], - dims=[config.dim_input] + config.dim_hidden + [config.dim_output]) - - # Create the Theano variables - client_embed = client_embed_table.apply(x_client) - stand_embed = stand_embed_table.apply(x_stand) - 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) - - # Normalize & Center - # outputs = theano.printing.Print("normal_outputs")(outputs) - outputs = data.data_std * outputs + data.porto_center - - # outputs = theano.printing.Print("outputs")(outputs) - # y = theano.printing.Print("y")(y) - - outputs.name = 'outputs' - - # Calculate the cost - cost = (outputs - y).norm(2, axis=1).mean() - cost.name = 'cost' - hcost = hdist.hdist(outputs, y).mean() - hcost.name = 'hcost' - - # Initialization - client_embed_table.weights_init = IsotropicGaussian(0.001) - stand_embed_table.weights_init = IsotropicGaussian(0.001) - mlp.weights_init = IsotropicGaussian(0.01) - mlp.biases_init = Constant(0.001) - - client_embed_table.initialize() - stand_embed_table.initialize() - mlp.initialize() + model = config.model.Model(config) + + cost = model.cost + hcost = model.hcost + outputs = model.outputs train_stream = setup_train_stream() valid_stream = setup_valid_stream() |