def BuildNetwork(self):
ActVar = T.reshape(self.ActVar,(-1,1,10,12))
ActInputLayer = lasagne.layers.InputLayer(shape=(None,1,10,12),input_var=ActVar,name='0')
StateInputLayer = lasagne.layers.InputLayer(shape=(None,10,10,12),input_var=self.StateVar,name='0')
network = lasagne.layers.ConcatLayer([ActInputLayer,StateInputLayer],axis=1)
network = lasagne.layers.DenseLayer(network,num_units=2000,W=lasagne.init.GlorotUniform(gain='relu'),nonlinearity=lasagne.nonlinearities.leaky_rectify,name = '1')
network = BN.batch_norm(network,name = '1')
#network = lasagne.layers.DropoutLayer(network)
network = lasagne.layers.DenseLayer(network,num_units=1000,W=lasagne.init.GlorotUniform(gain='relu'),nonlinearity=lasagne.nonlinearities.leaky_rectify,name = '2')
network = BN.batch_norm(network,name = '2')
#network = lasagne.layers.DropoutLayer(network)
network = lasagne.layers.DenseLayer(network,num_units=500,W=lasagne.init.GlorotUniform(gain='relu'),nonlinearity=lasagne.nonlinearities.leaky_rectify,name = '3')
network = BN.batch_norm(network,name = '3')
#network = lasagne.layers.DropoutLayer(network)
network = lasagne.layers.DenseLayer(network,num_units=40,W=lasagne.init.GlorotUniform(gain='relu'),nonlinearity=lasagne.nonlinearities.leaky_rectify,name = '4')
network = BN.batch_norm(network,name = '4')
#network = lasagne.layers.DropoutLayer(network)
network = lasagne.layers.DenseLayer(network,num_units=5,W=lasagne.init.GlorotUniform(gain='relu'),nonlinearity=lasagne.nonlinearities.leaky_rectify,name = '5')
network = BN.batch_norm(network,name = '5')
#network = lasagne.layers.DropoutLayer(network)
network = lasagne.layers.DenseLayer(network,num_units=1,W=lasagne.init.GlorotUniform(gain='relu'),nonlinearity=lasagne.nonlinearities.leaky_rectify,name = '6')
network = BN.batch_norm(network,name = '6')
return network
def BuildNetwork(self):
network = lasagne.layers.InputLayer(shape=(None,10,10,12),input_var=self.InputVar,name='0')
network = lasagne.layers.DenseLayer(network,num_units=1500,W=lasagne.init.GlorotUniform(gain='relu'),nonlinearity=lasagne.nonlinearities.leaky_rectify,name = '1')
network = BN.batch_norm(network,name = '1')
#network = lasagne.layers.DropoutLayer(network,0.8)
network = lasagne.layers.DenseLayer(network,num_units=1000,W=lasagne.init.GlorotUniform(gain='relu'),nonlinearity=lasagne.nonlinearities.leaky_rectify,name = '2')
network = BN.batch_norm(network,name = '2')
#network = lasagne.layers.DropoutLayer(network)
network = lasagne.layers.DenseLayer(network,num_units=500,W=lasagne.init.GlorotUniform(gain='relu'),nonlinearity=lasagne.nonlinearities.leaky_rectify,name = '3')
network = BN.batch_norm(network,name = '3')
#network = lasagne.layers.DropoutLayer(network)
network = lasagne.layers.DenseLayer(network,num_units=300,W=lasagne.init.GlorotUniform(gain='relu'),nonlinearity=lasagne.nonlinearities.leaky_rectify,name = '4')
network = BN.batch_norm(network,name = '4')
#network = lasagne.layers.DropoutLayer(network)
network = lasagne.layers.DenseLayer(network,num_units=120,W=lasagne.init.GlorotUniform(gain=1.0),nonlinearity=lasagne.nonlinearities.sigmoid,name = '5')
network = BN.batch_norm(network,name = '5')
network = lasagne.layers.ReshapeLayer(network,shape=([0],10,12),name='5')
return network
