diff options
| -rw-r--r-- | data.py | 4 | ||||
| -rw-r--r-- | model.py | 24 |
2 files changed, 21 insertions, 7 deletions
@@ -1,6 +1,7 @@ import ast, csv import socket import fuel +import numpy from enum import Enum from fuel.datasets import Dataset from fuel.streams import DataStream @@ -13,6 +14,9 @@ else: client_ids = {int(x): y+1 for y, x in enumerate(open(DATA_PATH+"/client_ids.txt"))} +porto_center = numpy.array([[ -8.61612, 41.1573]], dtype='float32') +data_std = numpy.sqrt(numpy.array([[ 0.00333233, 0.00549598]], dtype='float32')) + class CallType(Enum): CENTRAL = 0 STAND = 1 @@ -21,7 +21,7 @@ from fuel.transformers import Batch from fuel.streams import DataStream from fuel.schemes import ConstantScheme -from blocks.algorithms import GradientDescent, Scale, AdaDelta +from blocks.algorithms import GradientDescent, Scale, AdaDelta, Momentum from blocks.graph import ComputationGraph from blocks.main_loop import MainLoop from blocks.extensions import Printing @@ -45,13 +45,22 @@ n_end_pts = 5 dim_embed = 50 dim_hidden = 200 -learning_rate = 0.01 +learning_rate = 0.002 +momentum = 0.9 batch_size = 32 def main(): # The input and the targets x_firstk = tensor.matrix('first_k') + n = x_firstk.shape[0] + x_firstk = (x_firstk.reshape((n, n_begin_end_pts, 2)) - data.porto_center[None, None, :]) / data.data_std[None, None, :] + x_firstk = x_firstk.reshape((n, 2 * n_begin_end_pts)) + x_lastk = tensor.matrix('last_k') + n = x_lastk.shape[0] + x_lastk = (x_lastk.reshape((n, n_begin_end_pts, 2)) - data.porto_center[None, None, :]) / data.data_std[None, None, :] + x_lastk = x_lastk.reshape((n, 2 * n_begin_end_pts)) + x_client = tensor.lvector('origin_call') x_stand = tensor.lvector('origin_stand') y = tensor.matrix('destination') @@ -75,9 +84,10 @@ def main(): # hidden = theano.printing.Print("hidden")(hidden) outputs = output_layer.apply(hidden) + # Normalize & Center + outputs = data.data_std * outputs + data.porto_center + # Calculate the cost - # cost = (outputs - y).norm(2, axis=1).mean() - # outputs = numpy.array([[ -8.621953, 41.162142]], dtype='float32') + 0 * outputs cost = (outputs - y).norm(2, axis=1).mean() cost.name = 'cost' hcost = hdist.hdist(outputs, y).mean() @@ -88,7 +98,7 @@ def main(): stand_embed_table.weights_init = IsotropicGaussian(0.001) hidden_layer.weights_init = IsotropicGaussian(0.01) hidden_layer.biases_init = Constant(0.001) - output_layer.weights_init = IsotropicGaussian(0.001) + output_layer.weights_init = IsotropicGaussian(0.01) output_layer.biases_init = Constant(0.001) client_embed_table.initialize() @@ -119,8 +129,8 @@ def main(): params = VariableFilter(bricks=[Linear])(cg.parameters) algorithm = GradientDescent( cost=cost, - step_rule=AdaDelta(decay_rate=0.5), - # step_rule=Scale(learning_rate=learning_rate), + # step_rule=AdaDelta(decay_rate=0.5), + step_rule=Momentum(learning_rate=learning_rate, momentum=momentum), params=params) extensions=[DataStreamMonitoring([cost, hcost], valid_stream, |