def buildNetwork(rnn_size): """ Build an RNN with softmax output """ net = RecurrentNeuralNetwork() input_layer = InputLayer(27) # Input is 27 unit vector recurrent_layer = RecurrentLayer(rnn_size) output_layer = SoftmaxLayer(34) input_to_recurrent = FullConnection(input_layer.output, recurrent_layer.input) recurrent_to_output = FullConnection(recurrent_layer.output, output_layer.input) recurrent_bias = Bias(recurrent_layer.input) output_bias = Bias(output_layer.input) target_layer = InputLayer(34) # 34 classes! objective_layer = CrossEntropyObjective(output_layer.output, target_layer.output) net.addLayer(input_layer) net.addLayer(target_layer) net.addConnection(input_to_recurrent) net.addConnection(recurrent_bias) net.addRecurrentLayer(recurrent_layer) net.addConnection(recurrent_to_output) net.addConnection(output_bias) net.addLayer(output_layer) net.addObjective(objective_layer) net.setInputLayer(input_layer) net.setTargetLayer(target_layer) net.setOutputLayer(output_layer) net.setObjective(objective_layer) net.randomize() return net
