def get_conv_dense_model():
graph = Graph((32, 32, 3), False)
output_node_id = 0
output_node_id = graph.add_layer(StubReLU(), output_node_id)
output_node_id = graph.add_layer(StubConv2d(3, 3, 3), output_node_id)
output_node_id = graph.add_layer(StubBatchNormalization2d(3),
output_node_id)
output_node_id = graph.add_layer(StubReLU(), output_node_id)
output_node_id = graph.add_layer(StubConv2d(3, 3, 3), output_node_id)
output_node_id = graph.add_layer(StubBatchNormalization2d(3),
output_node_id)
output_node_id = graph.add_layer(StubFlatten(), output_node_id)
output_node_id = graph.add_layer(
StubDropout2d(Constant.DENSE_DROPOUT_RATE), output_node_id)
output_node_id = graph.add_layer(StubReLU(), output_node_id)
output_node_id = graph.add_layer(
StubDense(graph.node_list[output_node_id].shape[0], 5), output_node_id)
output_node_id = graph.add_layer(StubReLU(), output_node_id)
output_node_id = graph.add_layer(StubDense(5, 5), output_node_id)
graph.add_layer(StubSoftmax(), output_node_id)
graph.produce_model().set_weight_to_graph()
return graph
def get_concat_skip_model():
graph = Graph((32, 32, 3), False)
output_node_id = 0
output_node_id = graph.add_layer(StubReLU(), output_node_id)
output_node_id = graph.add_layer(StubConv(3, 3, 3), output_node_id)
output_node_id = graph.add_layer(StubBatchNormalization(3), output_node_id)
output_node_id = graph.add_layer(StubReLU(), output_node_id)
output_node_id = graph.add_layer(StubConv(3, 3, 3), output_node_id)
output_node_id = graph.add_layer(StubBatchNormalization(3), output_node_id)
temp_node_id = output_node_id
output_node_id = graph.add_layer(StubReLU(), output_node_id)
output_node_id = graph.add_layer(StubConv(3, 3, 3), output_node_id)
output_node_id = graph.add_layer(StubBatchNormalization(3), output_node_id)
output_node_id = graph.add_layer(StubConcatenate(),
[output_node_id, temp_node_id])
output_node_id = graph.add_layer(StubReLU(), output_node_id)
output_node_id = graph.add_layer(StubConv(6, 3, 1), output_node_id)
output_node_id = graph.add_layer(StubBatchNormalization(3), output_node_id)
temp_node_id = output_node_id
output_node_id = graph.add_layer(StubReLU(), output_node_id)
output_node_id = graph.add_layer(StubConv(3, 3, 3), output_node_id)
output_node_id = graph.add_layer(StubBatchNormalization(3), output_node_id)
output_node_id = graph.add_layer(StubConcatenate(),
[output_node_id, temp_node_id])
output_node_id = graph.add_layer(StubReLU(), output_node_id)
output_node_id = graph.add_layer(StubConv(6, 3, 1), output_node_id)
output_node_id = graph.add_layer(StubBatchNormalization(3), output_node_id)
output_node_id = graph.add_layer(StubReLU(), output_node_id)
output_node_id = graph.add_layer(StubConv(3, 3, 3), output_node_id)
output_node_id = graph.add_layer(StubBatchNormalization(3), output_node_id)
output_node_id = graph.add_layer(StubFlatten(), output_node_id)
output_node_id = graph.add_layer(StubDropout(Constant.CONV_DROPOUT_RATE),
output_node_id)
output_node_id = graph.add_layer(StubReLU(), output_node_id)
output_node_id = graph.add_layer(
StubDense(graph.node_list[output_node_id].shape[0], 5), output_node_id)
output_node_id = graph.add_layer(StubReLU(), output_node_id)
output_node_id = graph.add_layer(StubDense(5, 5), output_node_id)
graph.add_layer(StubSoftmax(), output_node_id)
graph.produce_model().set_weight_to_graph()
return graph
def generate(self, model_len=Constant.MODEL_LEN, model_width=Constant.MODEL_WIDTH):
pooling_len = int(model_len / 4)
graph = Graph(self.input_shape, False)
temp_input_channel = self.input_shape[-1]
output_node_id = 0
for i in range(model_len):
output_node_id = graph.add_layer(StubReLU(), output_node_id)
output_node_id = graph.add_layer(StubConv(temp_input_channel, model_width, kernel_size=3), output_node_id)
output_node_id = graph.add_layer(StubBatchNormalization(model_width), output_node_id)
temp_input_channel = model_width
if pooling_len == 0 or ((i + 1) % pooling_len == 0 and i != model_len - 1):
output_node_id = graph.add_layer(StubPooling(), output_node_id)
output_node_id = graph.add_layer(StubFlatten(), output_node_id)
output_node_id = graph.add_layer(StubDropout(Constant.CONV_DROPOUT_RATE), output_node_id)
output_node_id = graph.add_layer(StubDense(graph.node_list[output_node_id].shape[0], model_width),
output_node_id)
output_node_id = graph.add_layer(StubReLU(), output_node_id)
graph.add_layer(StubDense(model_width, self.n_output_node), output_node_id)
return graph
