skeletonClassifier_lasagne.py 文件源码

def build_network(self, input_var=None, batch_size = None):

        print "build_network() in SkeletonClassifier invoked"
        print self.sinputs

        if not input_var is None: self.sinputs = input_var
        if batch_size: self.batch_size = batch_size



        if not input_var is None: self.sinputs = input_var
        if not batch_size is None:
            self.batch_size = batch_size

        self.network['input'] = lasagne.layers.InputLayer(shape=(self.batch_size,self.nframes,1,self.dlength), input_var=self.sinputs[0])

        self.network['FC_1'] = batch_norm(lasagne.layers.DenseLayer( lasagne.layers.dropout(self.network['input'], p=self.dropout_rates[1]),
                    num_units=self.fc_layers[0],nonlinearity=lasagne.nonlinearities.tanh))

        self.network['FC_2'] = batch_norm(lasagne.layers.DenseLayer(
                    lasagne.layers.dropout(self.network['FC_1'], p=self.dropout_rates[2]),
                    num_units=self.fc_layers[1],
                    nonlinearity=lasagne.nonlinearities.tanh))

        self.network['FC_3'] = batch_norm(lasagne.layers.DenseLayer(
                    lasagne.layers.dropout(self.network['FC_2'], p=self.dropout_rates[3]),
                    num_units=self.fc_layers[2],
                    nonlinearity=lasagne.nonlinearities.tanh))

        self.network['prob'] = lasagne.layers.DenseLayer(
                    lasagne.layers.dropout(self.network['FC_3'], p=.2),
                    num_units=self.nclasses,
                    nonlinearity=lasagne.nonlinearities.softmax)

        return self.network