from blocks.bricks import MLP, Rectifier, Linear, Sigmoid, Identity from blocks.bricks.lookup import LookupTable from theano import tensor import data import error class Model(object): def __init__(self, config): # The input and the targets x_firstk_latitude = (tensor.matrix('first_k_latitude') - data.train_gps_mean[0]) / data.train_gps_std[0] x_firstk_longitude = (tensor.matrix('first_k_longitude') - data.train_gps_mean[1]) / data.train_gps_std[1] x_lastk_latitude = (tensor.matrix('last_k_latitude') - data.train_gps_mean[0]) / data.train_gps_std[0] x_lastk_longitude = (tensor.matrix('last_k_longitude') - data.train_gps_mean[1]) / data.train_gps_std[1] 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.lvector('travel_time') # Define the model mlp = MLP(activations=[Rectifier() for _ in config.dim_hidden] + [Identity()], dims=[config.dim_input] + config.dim_hidden + [config.dim_output]) # Create the Theano variables inputs = tensor.concatenate(input_list, axis=1) # inputs = theano.printing.Print("inputs")(inputs) outputs = config.exp_base ** mlp.apply(inputs) # outputs = theano.printing.Print("outputs")(outputs) # y = theano.printing.Print("y")(y) outputs.name = 'outputs' # Calculate the cost cost = error.rmsle(outputs.flatten(), y.flatten()) cost.name = 'cost' # Initialization for tbl in embed_tables: tbl.weights_init = config.embed_weights_init mlp.weights_init = config.mlp_weights_init mlp.biases_init = config.mlp_biases_init for tbl in embed_tables: tbl.initialize() mlp.initialize() self.cost = cost self.monitor = [cost] self.outputs = outputs self.pred_vars = ['travel_time']