diff options
Diffstat (limited to 'model')
| -rw-r--r-- | model/simple_mlp.py | 30 | ||||
| -rw-r--r-- | model/simple_mlp_tgtcls.py | 31 |
2 files changed, 33 insertions, 28 deletions
diff --git a/model/simple_mlp.py b/model/simple_mlp.py index 896ccd3..fc065f7 100644 --- a/model/simple_mlp.py +++ b/model/simple_mlp.py @@ -17,25 +17,27 @@ class Model(object): 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') + input_list = [x_firstk_latitude, x_firstk_longitude, x_lastk_latitude, x_lastk_longitude] + embed_tables = [] + + self.require_inputs = ['first_k_latitude', 'first_k_longitude', 'last_k_latitude', 'last_k_longitude'] + + for (varname, num, dim) in config.dim_embeddings: + self.require_inputs.append(varname) + vardata = tensor.lvector(varname) + tbl = LookupTable(length=num, dim=dim, name='%s_lookup'%varname) + embed_tables.append(tbl) + input_list.append(tbl.apply(vardata)) 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 = tensor.concatenate(input_list, axis=1) # inputs = theano.printing.Print("inputs")(inputs) outputs = mlp.apply(inputs) @@ -55,13 +57,13 @@ class Model(object): hcost.name = 'hcost' # Initialization - client_embed_table.weights_init = IsotropicGaussian(0.001) - stand_embed_table.weights_init = IsotropicGaussian(0.001) + for tbl in embed_tables: + tbl.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() + for tbl in embed_tables: + tbl.initialize() mlp.initialize() self.cost = cost diff --git a/model/simple_mlp_tgtcls.py b/model/simple_mlp_tgtcls.py index fecd900..b2a1a6e 100644 --- a/model/simple_mlp_tgtcls.py +++ b/model/simple_mlp_tgtcls.py @@ -20,26 +20,29 @@ class Model(object): 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') + input_list = [x_firstk_latitude, x_firstk_longitude, x_lastk_latitude, x_lastk_longitude] + embed_tables = [] + + self.require_inputs = ['first_k_latitude', 'first_k_longitude', 'last_k_latitude', 'last_k_longitude'] + + for (varname, num, dim) in config.dim_embeddings: + self.require_inputs.append(varname) + vardata = tensor.lvector(varname) + tbl = LookupTable(length=num, dim=dim, name='%s_lookup'%varname) + embed_tables.append(tbl) + input_list.append(tbl.apply(vardata)) 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] + [Softmax()], dims=[config.dim_input] + config.dim_hidden + [config.dim_output]) classes = theano.shared(numpy.array(config.tgtcls, dtype=theano.config.floatX), name='classes') # 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 = tensor.concatenate(input_list, axis=1) + # inputs = theano.printing.Print("inputs")(inputs) cls_probas = mlp.apply(inputs) outputs = tensor.dot(cls_probas, classes) @@ -56,13 +59,13 @@ class Model(object): hcost.name = 'hcost' # Initialization - client_embed_table.weights_init = IsotropicGaussian(0.001) - stand_embed_table.weights_init = IsotropicGaussian(0.001) + for tbl in embed_tables: + tbl.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() + for tbl in embed_tables: + tbl.initialize() mlp.initialize() self.cost = cost |