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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
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