emotion.xml

<?xml version="1.0" ?>
<net name="output_graph_Emotion" version="11">
	<layers>
		<layer id="0" name="conv2d_1_input" type="Parameter" version="opset1">
			<data element_type="f32" shape="1,48,48,1"/>
			<rt_info>
				<attribute name="fused_names" value="conv2d_1_input" version="0"/>
				<attribute name="old_api_map_element_type" value="f16" version="0"/>
				<attribute name="old_api_map_order" value="0, 2, 3, 1" version="0"/>
			</rt_info>
			<output>
				<port id="0" names="conv2d_1_input,conv2d_1_input:0" precision="FP32">
					<dim>1</dim>
					<dim>48</dim>
					<dim>48</dim>
					<dim>1</dim>
				</port>
			</output>
		</layer>
		<layer id="1" name="conv2d_1_input/Transpose([0 3 1 2])/value28294075" type="Const" version="opset1">
			<data element_type="i64" offset="0" shape="4" size="32"/>
			<rt_info>
				<attribute name="fused_names" value="conv2d_1_input/Transpose([0 3 1 2])/value28294075" version="0"/>
			</rt_info>
			<output>
				<port id="0" precision="I64">
					<dim>4</dim>
				</port>
			</output>
		</layer>
		<layer id="2" name="conv2d_1_input/Transpose([0 3 1 2])" type="Transpose" version="opset1">
			<rt_info>
				<attribute name="fused_names" value="conv2d_1_input/Transpose([0 3 1 2])" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP32">
					<dim>1</dim>
					<dim>48</dim>
					<dim>48</dim>
					<dim>1</dim>
				</port>
				<port id="1" precision="I64">
					<dim>4</dim>
				</port>
			</input>
			<output>
				<port id="2" precision="FP32">
					<dim>1</dim>
					<dim>1</dim>
					<dim>48</dim>
					<dim>48</dim>
				</port>
			</output>
		</layer>
		<layer id="3" name="conv2d_1/kernel_compressed" type="Const" version="opset1">
			<data element_type="f16" offset="32" shape="32, 1, 3, 3" size="576"/>
			<output>
				<port id="0" precision="FP16">
					<dim>32</dim>
					<dim>1</dim>
					<dim>3</dim>
					<dim>3</dim>
				</port>
			</output>
		</layer>
		<layer id="4" name="conv2d_1/kernel" type="Convert" version="opset1">
			<data destination_type="f32"/>
			<rt_info>
				<attribute name="decompression" version="0"/>
				<attribute name="fused_names" value="conv2d_1/kernel" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP16">
					<dim>32</dim>
					<dim>1</dim>
					<dim>3</dim>
					<dim>3</dim>
				</port>
			</input>
			<output>
				<port id="1" names="conv2d_1/kernel:0" precision="FP32">
					<dim>32</dim>
					<dim>1</dim>
					<dim>3</dim>
					<dim>3</dim>
				</port>
			</output>
		</layer>
		<layer id="5" name="conv2d_1/Conv2D" type="Convolution" version="opset1">
			<data auto_pad="same_upper" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
			<rt_info>
				<attribute name="fused_names" value="conv2d_1/Conv2D" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP32">
					<dim>1</dim>
					<dim>1</dim>
					<dim>48</dim>
					<dim>48</dim>
				</port>
				<port id="1" precision="FP32">
					<dim>32</dim>
					<dim>1</dim>
					<dim>3</dim>
					<dim>3</dim>
				</port>
			</input>
			<output>
				<port id="2" names="conv2d_1/Conv2D:0" precision="FP32">
					<dim>1</dim>
					<dim>32</dim>
					<dim>48</dim>
					<dim>48</dim>
				</port>
			</output>
		</layer>
		<layer id="6" name="conv2d_1/bias_compressed" type="Const" version="opset1">
			<data element_type="f16" offset="608" shape="1, 32, 1, 1" size="64"/>
			<output>
				<port id="0" precision="FP16">
					<dim>1</dim>
					<dim>32</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</output>
		</layer>
		<layer id="7" name="conv2d_1/bias" type="Convert" version="opset1">
			<data destination_type="f32"/>
			<rt_info>
				<attribute name="decompression" version="0"/>
				<attribute name="fused_names" value="conv2d_1/bias" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP16">
					<dim>1</dim>
					<dim>32</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</input>
			<output>
				<port id="1" names="conv2d_1/bias:0" precision="FP32">
					<dim>1</dim>
					<dim>32</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</output>
		</layer>
		<layer id="8" name="conv2d_1/BiasAdd/Add" type="Add" version="opset1">
			<data auto_broadcast="numpy"/>
			<rt_info>
				<attribute name="fused_names" value="conv2d_1/BiasAdd/Add" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP32">
					<dim>1</dim>
					<dim>32</dim>
					<dim>48</dim>
					<dim>48</dim>
				</port>
				<port id="1" precision="FP32">
					<dim>1</dim>
					<dim>32</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</input>
			<output>
				<port id="2" names="conv2d_1/BiasAdd:0" precision="FP32">
					<dim>1</dim>
					<dim>32</dim>
					<dim>48</dim>
					<dim>48</dim>
				</port>
			</output>
		</layer>
		<layer id="9" name="activation_1/Elu" type="Elu" version="opset1">
			<data alpha="1"/>
			<rt_info>
				<attribute name="fused_names" value="activation_1/Elu" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP32">
					<dim>1</dim>
					<dim>32</dim>
					<dim>48</dim>
					<dim>48</dim>
				</port>
			</input>
			<output>
				<port id="1" names="activation_1/Elu:0" precision="FP32">
					<dim>1</dim>
					<dim>32</dim>
					<dim>48</dim>
					<dim>48</dim>
				</port>
			</output>
		</layer>
		<layer id="10" name="data_mul_2035_compressed" type="Const" version="opset1">
			<data element_type="f16" offset="672" shape="1, 32, 1, 1" size="64"/>
			<output>
				<port id="0" precision="FP16">
					<dim>1</dim>
					<dim>32</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</output>
		</layer>
		<layer id="11" name="data_mul_2035" type="Convert" version="opset1">
			<data destination_type="f32"/>
			<rt_info>
				<attribute name="decompression" version="0"/>
				<attribute name="fused_names" value="data_mul_2035" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP16">
					<dim>1</dim>
					<dim>32</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</input>
			<output>
				<port id="1" precision="FP32">
					<dim>1</dim>
					<dim>32</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</output>
		</layer>
		<layer id="12" name="batch_normalization_1/FusedBatchNormV3/mean/Fused_Mul_" type="Multiply" version="opset1">
			<data auto_broadcast="numpy"/>
			<rt_info>
				<attribute name="fused_names" value="batch_normalization_1/FusedBatchNormV3/mean/Fused_Mul_" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP32">
					<dim>1</dim>
					<dim>32</dim>
					<dim>48</dim>
					<dim>48</dim>
				</port>
				<port id="1" precision="FP32">
					<dim>1</dim>
					<dim>32</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</input>
			<output>
				<port id="2" precision="FP32">
					<dim>1</dim>
					<dim>32</dim>
					<dim>48</dim>
					<dim>48</dim>
				</port>
			</output>
		</layer>
		<layer id="13" name="data_add_2037_compressed" type="Const" version="opset1">
			<data element_type="f16" offset="736" shape="1, 32, 1, 1" size="64"/>
			<output>
				<port id="0" precision="FP16">
					<dim>1</dim>
					<dim>32</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</output>
		</layer>
		<layer id="14" name="data_add_2037" type="Convert" version="opset1">
			<data destination_type="f32"/>
			<rt_info>
				<attribute name="decompression" version="0"/>
				<attribute name="fused_names" value="data_add_2037" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP16">
					<dim>1</dim>
					<dim>32</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</input>
			<output>
				<port id="1" precision="FP32">
					<dim>1</dim>
					<dim>32</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</output>
		</layer>
		<layer id="15" name="batch_normalization_1/FusedBatchNormV3/variance/Fused_Add_" type="Add" version="opset1">
			<data auto_broadcast="numpy"/>
			<rt_info>
				<attribute name="fused_names" value="batch_normalization_1/FusedBatchNormV3/variance/Fused_Add_" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP32">
					<dim>1</dim>
					<dim>32</dim>
					<dim>48</dim>
					<dim>48</dim>
				</port>
				<port id="1" precision="FP32">
					<dim>1</dim>
					<dim>32</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</input>
			<output>
				<port id="2" names="batch_normalization_1/FusedBatchNormV3:0" precision="FP32">
					<dim>1</dim>
					<dim>32</dim>
					<dim>48</dim>
					<dim>48</dim>
				</port>
			</output>
		</layer>
		<layer id="16" name="conv2d_2/kernel_compressed" type="Const" version="opset1">
			<data element_type="f16" offset="800" shape="32, 32, 3, 3" size="18432"/>
			<output>
				<port id="0" precision="FP16">
					<dim>32</dim>
					<dim>32</dim>
					<dim>3</dim>
					<dim>3</dim>
				</port>
			</output>
		</layer>
		<layer id="17" name="conv2d_2/kernel" type="Convert" version="opset1">
			<data destination_type="f32"/>
			<rt_info>
				<attribute name="decompression" version="0"/>
				<attribute name="fused_names" value="conv2d_2/kernel" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP16">
					<dim>32</dim>
					<dim>32</dim>
					<dim>3</dim>
					<dim>3</dim>
				</port>
			</input>
			<output>
				<port id="1" names="conv2d_2/kernel:0" precision="FP32">
					<dim>32</dim>
					<dim>32</dim>
					<dim>3</dim>
					<dim>3</dim>
				</port>
			</output>
		</layer>
		<layer id="18" name="conv2d_2/Conv2D" type="Convolution" version="opset1">
			<data auto_pad="same_upper" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
			<rt_info>
				<attribute name="fused_names" value="conv2d_2/Conv2D" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP32">
					<dim>1</dim>
					<dim>32</dim>
					<dim>48</dim>
					<dim>48</dim>
				</port>
				<port id="1" precision="FP32">
					<dim>32</dim>
					<dim>32</dim>
					<dim>3</dim>
					<dim>3</dim>
				</port>
			</input>
			<output>
				<port id="2" names="conv2d_2/Conv2D:0" precision="FP32">
					<dim>1</dim>
					<dim>32</dim>
					<dim>48</dim>
					<dim>48</dim>
				</port>
			</output>
		</layer>
		<layer id="19" name="conv2d_2/bias_compressed" type="Const" version="opset1">
			<data element_type="f16" offset="19232" shape="1, 32, 1, 1" size="64"/>
			<output>
				<port id="0" precision="FP16">
					<dim>1</dim>
					<dim>32</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</output>
		</layer>
		<layer id="20" name="conv2d_2/bias" type="Convert" version="opset1">
			<data destination_type="f32"/>
			<rt_info>
				<attribute name="decompression" version="0"/>
				<attribute name="fused_names" value="conv2d_2/bias" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP16">
					<dim>1</dim>
					<dim>32</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</input>
			<output>
				<port id="1" names="conv2d_2/bias:0" precision="FP32">
					<dim>1</dim>
					<dim>32</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</output>
		</layer>
		<layer id="21" name="conv2d_2/BiasAdd/Add" type="Add" version="opset1">
			<data auto_broadcast="numpy"/>
			<rt_info>
				<attribute name="fused_names" value="conv2d_2/BiasAdd/Add" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP32">
					<dim>1</dim>
					<dim>32</dim>
					<dim>48</dim>
					<dim>48</dim>
				</port>
				<port id="1" precision="FP32">
					<dim>1</dim>
					<dim>32</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</input>
			<output>
				<port id="2" names="conv2d_2/BiasAdd:0" precision="FP32">
					<dim>1</dim>
					<dim>32</dim>
					<dim>48</dim>
					<dim>48</dim>
				</port>
			</output>
		</layer>
		<layer id="22" name="activation_2/Elu" type="Elu" version="opset1">
			<data alpha="1"/>
			<rt_info>
				<attribute name="fused_names" value="activation_2/Elu" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP32">
					<dim>1</dim>
					<dim>32</dim>
					<dim>48</dim>
					<dim>48</dim>
				</port>
			</input>
			<output>
				<port id="1" names="activation_2/Elu:0" precision="FP32">
					<dim>1</dim>
					<dim>32</dim>
					<dim>48</dim>
					<dim>48</dim>
				</port>
			</output>
		</layer>
		<layer id="23" name="data_mul_20392043_compressed" type="Const" version="opset1">
			<data element_type="f16" offset="19296" shape="1, 32, 1, 1" size="64"/>
			<output>
				<port id="0" precision="FP16">
					<dim>1</dim>
					<dim>32</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</output>
		</layer>
		<layer id="24" name="data_mul_20392043" type="Convert" version="opset1">
			<data destination_type="f32"/>
			<rt_info>
				<attribute name="decompression" version="0"/>
				<attribute name="fused_names" value="data_mul_20392043" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP16">
					<dim>1</dim>
					<dim>32</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</input>
			<output>
				<port id="1" precision="FP32">
					<dim>1</dim>
					<dim>32</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</output>
		</layer>
		<layer id="25" name="batch_normalization_2/FusedBatchNormV3/mean/Fused_Mul_" type="Multiply" version="opset1">
			<data auto_broadcast="numpy"/>
			<rt_info>
				<attribute name="fused_names" value="batch_normalization_2/FusedBatchNormV3/mean/Fused_Mul_" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP32">
					<dim>1</dim>
					<dim>32</dim>
					<dim>48</dim>
					<dim>48</dim>
				</port>
				<port id="1" precision="FP32">
					<dim>1</dim>
					<dim>32</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</input>
			<output>
				<port id="2" precision="FP32">
					<dim>1</dim>
					<dim>32</dim>
					<dim>48</dim>
					<dim>48</dim>
				</port>
			</output>
		</layer>
		<layer id="26" name="data_add_20402045_compressed" type="Const" version="opset1">
			<data element_type="f16" offset="19360" shape="1, 32, 1, 1" size="64"/>
			<output>
				<port id="0" precision="FP16">
					<dim>1</dim>
					<dim>32</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</output>
		</layer>
		<layer id="27" name="data_add_20402045" type="Convert" version="opset1">
			<data destination_type="f32"/>
			<rt_info>
				<attribute name="decompression" version="0"/>
				<attribute name="fused_names" value="data_add_20402045" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP16">
					<dim>1</dim>
					<dim>32</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</input>
			<output>
				<port id="1" precision="FP32">
					<dim>1</dim>
					<dim>32</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</output>
		</layer>
		<layer id="28" name="batch_normalization_2/FusedBatchNormV3/variance/Fused_Add_" type="Add" version="opset1">
			<data auto_broadcast="numpy"/>
			<rt_info>
				<attribute name="fused_names" value="batch_normalization_2/FusedBatchNormV3/variance/Fused_Add_" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP32">
					<dim>1</dim>
					<dim>32</dim>
					<dim>48</dim>
					<dim>48</dim>
				</port>
				<port id="1" precision="FP32">
					<dim>1</dim>
					<dim>32</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</input>
			<output>
				<port id="2" names="batch_normalization_2/FusedBatchNormV3:0" precision="FP32">
					<dim>1</dim>
					<dim>32</dim>
					<dim>48</dim>
					<dim>48</dim>
				</port>
			</output>
		</layer>
		<layer id="29" name="max_pooling2d_1/MaxPool" type="MaxPool" version="opset8">
			<data auto_pad="valid" axis="0" dilations="1, 1" index_element_type="i64" kernel="2, 2" pads_begin="0, 0" pads_end="0, 0" rounding_type="floor" strides="2, 2"/>
			<rt_info>
				<attribute name="fused_names" value="max_pooling2d_1/MaxPool" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP32">
					<dim>1</dim>
					<dim>32</dim>
					<dim>48</dim>
					<dim>48</dim>
				</port>
			</input>
			<output>
				<port id="1" names="max_pooling2d_1/MaxPool:0" precision="FP32">
					<dim>1</dim>
					<dim>32</dim>
					<dim>24</dim>
					<dim>24</dim>
				</port>
				<port id="2" precision="I64">
					<dim>1</dim>
					<dim>32</dim>
					<dim>24</dim>
					<dim>24</dim>
				</port>
			</output>
		</layer>
		<layer id="30" name="conv2d_3/kernel_compressed" type="Const" version="opset1">
			<data element_type="f16" offset="19424" shape="64, 32, 3, 3" size="36864"/>
			<output>
				<port id="0" precision="FP16">
					<dim>64</dim>
					<dim>32</dim>
					<dim>3</dim>
					<dim>3</dim>
				</port>
			</output>
		</layer>
		<layer id="31" name="conv2d_3/kernel" type="Convert" version="opset1">
			<data destination_type="f32"/>
			<rt_info>
				<attribute name="decompression" version="0"/>
				<attribute name="fused_names" value="conv2d_3/kernel" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP16">
					<dim>64</dim>
					<dim>32</dim>
					<dim>3</dim>
					<dim>3</dim>
				</port>
			</input>
			<output>
				<port id="1" names="conv2d_3/kernel:0" precision="FP32">
					<dim>64</dim>
					<dim>32</dim>
					<dim>3</dim>
					<dim>3</dim>
				</port>
			</output>
		</layer>
		<layer id="32" name="conv2d_3/Conv2D" type="Convolution" version="opset1">
			<data auto_pad="same_upper" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
			<rt_info>
				<attribute name="fused_names" value="conv2d_3/Conv2D" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP32">
					<dim>1</dim>
					<dim>32</dim>
					<dim>24</dim>
					<dim>24</dim>
				</port>
				<port id="1" precision="FP32">
					<dim>64</dim>
					<dim>32</dim>
					<dim>3</dim>
					<dim>3</dim>
				</port>
			</input>
			<output>
				<port id="2" names="conv2d_3/Conv2D:0" precision="FP32">
					<dim>1</dim>
					<dim>64</dim>
					<dim>24</dim>
					<dim>24</dim>
				</port>
			</output>
		</layer>
		<layer id="33" name="conv2d_3/bias_compressed" type="Const" version="opset1">
			<data element_type="f16" offset="56288" shape="1, 64, 1, 1" size="128"/>
			<output>
				<port id="0" precision="FP16">
					<dim>1</dim>
					<dim>64</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</output>
		</layer>
		<layer id="34" name="conv2d_3/bias" type="Convert" version="opset1">
			<data destination_type="f32"/>
			<rt_info>
				<attribute name="decompression" version="0"/>
				<attribute name="fused_names" value="conv2d_3/bias" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP16">
					<dim>1</dim>
					<dim>64</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</input>
			<output>
				<port id="1" names="conv2d_3/bias:0" precision="FP32">
					<dim>1</dim>
					<dim>64</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</output>
		</layer>
		<layer id="35" name="conv2d_3/BiasAdd/Add" type="Add" version="opset1">
			<data auto_broadcast="numpy"/>
			<rt_info>
				<attribute name="fused_names" value="conv2d_3/BiasAdd/Add" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP32">
					<dim>1</dim>
					<dim>64</dim>
					<dim>24</dim>
					<dim>24</dim>
				</port>
				<port id="1" precision="FP32">
					<dim>1</dim>
					<dim>64</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</input>
			<output>
				<port id="2" names="conv2d_3/BiasAdd:0" precision="FP32">
					<dim>1</dim>
					<dim>64</dim>
					<dim>24</dim>
					<dim>24</dim>
				</port>
			</output>
		</layer>
		<layer id="36" name="activation_3/Elu" type="Elu" version="opset1">
			<data alpha="1"/>
			<rt_info>
				<attribute name="fused_names" value="activation_3/Elu" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP32">
					<dim>1</dim>
					<dim>64</dim>
					<dim>24</dim>
					<dim>24</dim>
				</port>
			</input>
			<output>
				<port id="1" names="activation_3/Elu:0" precision="FP32">
					<dim>1</dim>
					<dim>64</dim>
					<dim>24</dim>
					<dim>24</dim>
				</port>
			</output>
		</layer>
		<layer id="37" name="data_mul_20472051_compressed" type="Const" version="opset1">
			<data element_type="f16" offset="56416" shape="1, 64, 1, 1" size="128"/>
			<output>
				<port id="0" precision="FP16">
					<dim>1</dim>
					<dim>64</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</output>
		</layer>
		<layer id="38" name="data_mul_20472051" type="Convert" version="opset1">
			<data destination_type="f32"/>
			<rt_info>
				<attribute name="decompression" version="0"/>
				<attribute name="fused_names" value="data_mul_20472051" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP16">
					<dim>1</dim>
					<dim>64</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</input>
			<output>
				<port id="1" precision="FP32">
					<dim>1</dim>
					<dim>64</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</output>
		</layer>
		<layer id="39" name="batch_normalization_3/FusedBatchNormV3/mean/Fused_Mul_" type="Multiply" version="opset1">
			<data auto_broadcast="numpy"/>
			<rt_info>
				<attribute name="fused_names" value="batch_normalization_3/FusedBatchNormV3/mean/Fused_Mul_" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP32">
					<dim>1</dim>
					<dim>64</dim>
					<dim>24</dim>
					<dim>24</dim>
				</port>
				<port id="1" precision="FP32">
					<dim>1</dim>
					<dim>64</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</input>
			<output>
				<port id="2" precision="FP32">
					<dim>1</dim>
					<dim>64</dim>
					<dim>24</dim>
					<dim>24</dim>
				</port>
			</output>
		</layer>
		<layer id="40" name="data_add_20482053_compressed" type="Const" version="opset1">
			<data element_type="f16" offset="56544" shape="1, 64, 1, 1" size="128"/>
			<output>
				<port id="0" precision="FP16">
					<dim>1</dim>
					<dim>64</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</output>
		</layer>
		<layer id="41" name="data_add_20482053" type="Convert" version="opset1">
			<data destination_type="f32"/>
			<rt_info>
				<attribute name="decompression" version="0"/>
				<attribute name="fused_names" value="data_add_20482053" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP16">
					<dim>1</dim>
					<dim>64</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</input>
			<output>
				<port id="1" precision="FP32">
					<dim>1</dim>
					<dim>64</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</output>
		</layer>
		<layer id="42" name="batch_normalization_3/FusedBatchNormV3/variance/Fused_Add_" type="Add" version="opset1">
			<data auto_broadcast="numpy"/>
			<rt_info>
				<attribute name="fused_names" value="batch_normalization_3/FusedBatchNormV3/variance/Fused_Add_" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP32">
					<dim>1</dim>
					<dim>64</dim>
					<dim>24</dim>
					<dim>24</dim>
				</port>
				<port id="1" precision="FP32">
					<dim>1</dim>
					<dim>64</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</input>
			<output>
				<port id="2" names="batch_normalization_3/FusedBatchNormV3:0" precision="FP32">
					<dim>1</dim>
					<dim>64</dim>
					<dim>24</dim>
					<dim>24</dim>
				</port>
			</output>
		</layer>
		<layer id="43" name="conv2d_4/kernel_compressed" type="Const" version="opset1">
			<data element_type="f16" offset="56672" shape="64, 64, 3, 3" size="73728"/>
			<output>
				<port id="0" precision="FP16">
					<dim>64</dim>
					<dim>64</dim>
					<dim>3</dim>
					<dim>3</dim>
				</port>
			</output>
		</layer>
		<layer id="44" name="conv2d_4/kernel" type="Convert" version="opset1">
			<data destination_type="f32"/>
			<rt_info>
				<attribute name="decompression" version="0"/>
				<attribute name="fused_names" value="conv2d_4/kernel" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP16">
					<dim>64</dim>
					<dim>64</dim>
					<dim>3</dim>
					<dim>3</dim>
				</port>
			</input>
			<output>
				<port id="1" names="conv2d_4/kernel:0" precision="FP32">
					<dim>64</dim>
					<dim>64</dim>
					<dim>3</dim>
					<dim>3</dim>
				</port>
			</output>
		</layer>
		<layer id="45" name="conv2d_4/Conv2D" type="Convolution" version="opset1">
			<data auto_pad="same_upper" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
			<rt_info>
				<attribute name="fused_names" value="conv2d_4/Conv2D" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP32">
					<dim>1</dim>
					<dim>64</dim>
					<dim>24</dim>
					<dim>24</dim>
				</port>
				<port id="1" precision="FP32">
					<dim>64</dim>
					<dim>64</dim>
					<dim>3</dim>
					<dim>3</dim>
				</port>
			</input>
			<output>
				<port id="2" names="conv2d_4/Conv2D:0" precision="FP32">
					<dim>1</dim>
					<dim>64</dim>
					<dim>24</dim>
					<dim>24</dim>
				</port>
			</output>
		</layer>
		<layer id="46" name="conv2d_4/bias_compressed" type="Const" version="opset1">
			<data element_type="f16" offset="130400" shape="1, 64, 1, 1" size="128"/>
			<output>
				<port id="0" precision="FP16">
					<dim>1</dim>
					<dim>64</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</output>
		</layer>
		<layer id="47" name="conv2d_4/bias" type="Convert" version="opset1">
			<data destination_type="f32"/>
			<rt_info>
				<attribute name="decompression" version="0"/>
				<attribute name="fused_names" value="conv2d_4/bias" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP16">
					<dim>1</dim>
					<dim>64</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</input>
			<output>
				<port id="1" names="conv2d_4/bias:0" precision="FP32">
					<dim>1</dim>
					<dim>64</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</output>
		</layer>
		<layer id="48" name="conv2d_4/BiasAdd/Add" type="Add" version="opset1">
			<data auto_broadcast="numpy"/>
			<rt_info>
				<attribute name="fused_names" value="conv2d_4/BiasAdd/Add" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP32">
					<dim>1</dim>
					<dim>64</dim>
					<dim>24</dim>
					<dim>24</dim>
				</port>
				<port id="1" precision="FP32">
					<dim>1</dim>
					<dim>64</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</input>
			<output>
				<port id="2" names="conv2d_4/BiasAdd:0" precision="FP32">
					<dim>1</dim>
					<dim>64</dim>
					<dim>24</dim>
					<dim>24</dim>
				</port>
			</output>
		</layer>
		<layer id="49" name="activation_4/Elu" type="Elu" version="opset1">
			<data alpha="1"/>
			<rt_info>
				<attribute name="fused_names" value="activation_4/Elu" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP32">
					<dim>1</dim>
					<dim>64</dim>
					<dim>24</dim>
					<dim>24</dim>
				</port>
			</input>
			<output>
				<port id="1" names="activation_4/Elu:0" precision="FP32">
					<dim>1</dim>
					<dim>64</dim>
					<dim>24</dim>
					<dim>24</dim>
				</port>
			</output>
		</layer>
		<layer id="50" name="data_mul_20552059_compressed" type="Const" version="opset1">
			<data element_type="f16" offset="130528" shape="1, 64, 1, 1" size="128"/>
			<output>
				<port id="0" precision="FP16">
					<dim>1</dim>
					<dim>64</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</output>
		</layer>
		<layer id="51" name="data_mul_20552059" type="Convert" version="opset1">
			<data destination_type="f32"/>
			<rt_info>
				<attribute name="decompression" version="0"/>
				<attribute name="fused_names" value="data_mul_20552059" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP16">
					<dim>1</dim>
					<dim>64</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</input>
			<output>
				<port id="1" precision="FP32">
					<dim>1</dim>
					<dim>64</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</output>
		</layer>
		<layer id="52" name="batch_normalization_4/FusedBatchNormV3/mean/Fused_Mul_" type="Multiply" version="opset1">
			<data auto_broadcast="numpy"/>
			<rt_info>
				<attribute name="fused_names" value="batch_normalization_4/FusedBatchNormV3/mean/Fused_Mul_" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP32">
					<dim>1</dim>
					<dim>64</dim>
					<dim>24</dim>
					<dim>24</dim>
				</port>
				<port id="1" precision="FP32">
					<dim>1</dim>
					<dim>64</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</input>
			<output>
				<port id="2" precision="FP32">
					<dim>1</dim>
					<dim>64</dim>
					<dim>24</dim>
					<dim>24</dim>
				</port>
			</output>
		</layer>
		<layer id="53" name="data_add_20562061_compressed" type="Const" version="opset1">
			<data element_type="f16" offset="130656" shape="1, 64, 1, 1" size="128"/>
			<output>
				<port id="0" precision="FP16">
					<dim>1</dim>
					<dim>64</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</output>
		</layer>
		<layer id="54" name="data_add_20562061" type="Convert" version="opset1">
			<data destination_type="f32"/>
			<rt_info>
				<attribute name="decompression" version="0"/>
				<attribute name="fused_names" value="data_add_20562061" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP16">
					<dim>1</dim>
					<dim>64</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</input>
			<output>
				<port id="1" precision="FP32">
					<dim>1</dim>
					<dim>64</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</output>
		</layer>
		<layer id="55" name="batch_normalization_4/FusedBatchNormV3/variance/Fused_Add_" type="Add" version="opset1">
			<data auto_broadcast="numpy"/>
			<rt_info>
				<attribute name="fused_names" value="batch_normalization_4/FusedBatchNormV3/variance/Fused_Add_" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP32">
					<dim>1</dim>
					<dim>64</dim>
					<dim>24</dim>
					<dim>24</dim>
				</port>
				<port id="1" precision="FP32">
					<dim>1</dim>
					<dim>64</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</input>
			<output>
				<port id="2" names="batch_normalization_4/FusedBatchNormV3:0" precision="FP32">
					<dim>1</dim>
					<dim>64</dim>
					<dim>24</dim>
					<dim>24</dim>
				</port>
			</output>
		</layer>
		<layer id="56" name="max_pooling2d_2/MaxPool" type="MaxPool" version="opset8">
			<data auto_pad="valid" axis="0" dilations="1, 1" index_element_type="i64" kernel="2, 2" pads_begin="0, 0" pads_end="0, 0" rounding_type="floor" strides="2, 2"/>
			<rt_info>
				<attribute name="fused_names" value="max_pooling2d_2/MaxPool" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP32">
					<dim>1</dim>
					<dim>64</dim>
					<dim>24</dim>
					<dim>24</dim>
				</port>
			</input>
			<output>
				<port id="1" names="max_pooling2d_2/MaxPool:0" precision="FP32">
					<dim>1</dim>
					<dim>64</dim>
					<dim>12</dim>
					<dim>12</dim>
				</port>
				<port id="2" precision="I64">
					<dim>1</dim>
					<dim>64</dim>
					<dim>12</dim>
					<dim>12</dim>
				</port>
			</output>
		</layer>
		<layer id="57" name="conv2d_5/kernel_compressed" type="Const" version="opset1">
			<data element_type="f16" offset="130784" shape="128, 64, 3, 3" size="147456"/>
			<output>
				<port id="0" precision="FP16">
					<dim>128</dim>
					<dim>64</dim>
					<dim>3</dim>
					<dim>3</dim>
				</port>
			</output>
		</layer>
		<layer id="58" name="conv2d_5/kernel" type="Convert" version="opset1">
			<data destination_type="f32"/>
			<rt_info>
				<attribute name="decompression" version="0"/>
				<attribute name="fused_names" value="conv2d_5/kernel" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP16">
					<dim>128</dim>
					<dim>64</dim>
					<dim>3</dim>
					<dim>3</dim>
				</port>
			</input>
			<output>
				<port id="1" names="conv2d_5/kernel:0" precision="FP32">
					<dim>128</dim>
					<dim>64</dim>
					<dim>3</dim>
					<dim>3</dim>
				</port>
			</output>
		</layer>
		<layer id="59" name="conv2d_5/Conv2D" type="Convolution" version="opset1">
			<data auto_pad="same_upper" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
			<rt_info>
				<attribute name="fused_names" value="conv2d_5/Conv2D" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP32">
					<dim>1</dim>
					<dim>64</dim>
					<dim>12</dim>
					<dim>12</dim>
				</port>
				<port id="1" precision="FP32">
					<dim>128</dim>
					<dim>64</dim>
					<dim>3</dim>
					<dim>3</dim>
				</port>
			</input>
			<output>
				<port id="2" names="conv2d_5/Conv2D:0" precision="FP32">
					<dim>1</dim>
					<dim>128</dim>
					<dim>12</dim>
					<dim>12</dim>
				</port>
			</output>
		</layer>
		<layer id="60" name="conv2d_5/bias_compressed" type="Const" version="opset1">
			<data element_type="f16" offset="278240" shape="1, 128, 1, 1" size="256"/>
			<output>
				<port id="0" precision="FP16">
					<dim>1</dim>
					<dim>128</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</output>
		</layer>
		<layer id="61" name="conv2d_5/bias" type="Convert" version="opset1">
			<data destination_type="f32"/>
			<rt_info>
				<attribute name="decompression" version="0"/>
				<attribute name="fused_names" value="conv2d_5/bias" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP16">
					<dim>1</dim>
					<dim>128</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</input>
			<output>
				<port id="1" names="conv2d_5/bias:0" precision="FP32">
					<dim>1</dim>
					<dim>128</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</output>
		</layer>
		<layer id="62" name="conv2d_5/BiasAdd/Add" type="Add" version="opset1">
			<data auto_broadcast="numpy"/>
			<rt_info>
				<attribute name="fused_names" value="conv2d_5/BiasAdd/Add" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP32">
					<dim>1</dim>
					<dim>128</dim>
					<dim>12</dim>
					<dim>12</dim>
				</port>
				<port id="1" precision="FP32">
					<dim>1</dim>
					<dim>128</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</input>
			<output>
				<port id="2" names="conv2d_5/BiasAdd:0" precision="FP32">
					<dim>1</dim>
					<dim>128</dim>
					<dim>12</dim>
					<dim>12</dim>
				</port>
			</output>
		</layer>
		<layer id="63" name="activation_5/Elu" type="Elu" version="opset1">
			<data alpha="1"/>
			<rt_info>
				<attribute name="fused_names" value="activation_5/Elu" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP32">
					<dim>1</dim>
					<dim>128</dim>
					<dim>12</dim>
					<dim>12</dim>
				</port>
			</input>
			<output>
				<port id="1" names="activation_5/Elu:0" precision="FP32">
					<dim>1</dim>
					<dim>128</dim>
					<dim>12</dim>
					<dim>12</dim>
				</port>
			</output>
		</layer>
		<layer id="64" name="data_mul_20632067_compressed" type="Const" version="opset1">
			<data element_type="f16" offset="278496" shape="1, 128, 1, 1" size="256"/>
			<output>
				<port id="0" precision="FP16">
					<dim>1</dim>
					<dim>128</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</output>
		</layer>
		<layer id="65" name="data_mul_20632067" type="Convert" version="opset1">
			<data destination_type="f32"/>
			<rt_info>
				<attribute name="decompression" version="0"/>
				<attribute name="fused_names" value="data_mul_20632067" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP16">
					<dim>1</dim>
					<dim>128</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</input>
			<output>
				<port id="1" precision="FP32">
					<dim>1</dim>
					<dim>128</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</output>
		</layer>
		<layer id="66" name="batch_normalization_5/FusedBatchNormV3/mean/Fused_Mul_" type="Multiply" version="opset1">
			<data auto_broadcast="numpy"/>
			<rt_info>
				<attribute name="fused_names" value="batch_normalization_5/FusedBatchNormV3/mean/Fused_Mul_" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP32">
					<dim>1</dim>
					<dim>128</dim>
					<dim>12</dim>
					<dim>12</dim>
				</port>
				<port id="1" precision="FP32">
					<dim>1</dim>
					<dim>128</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</input>
			<output>
				<port id="2" precision="FP32">
					<dim>1</dim>
					<dim>128</dim>
					<dim>12</dim>
					<dim>12</dim>
				</port>
			</output>
		</layer>
		<layer id="67" name="data_add_20642069_compressed" type="Const" version="opset1">
			<data element_type="f16" offset="278752" shape="1, 128, 1, 1" size="256"/>
			<output>
				<port id="0" precision="FP16">
					<dim>1</dim>
					<dim>128</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</output>
		</layer>
		<layer id="68" name="data_add_20642069" type="Convert" version="opset1">
			<data destination_type="f32"/>
			<rt_info>
				<attribute name="decompression" version="0"/>
				<attribute name="fused_names" value="data_add_20642069" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP16">
					<dim>1</dim>
					<dim>128</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</input>
			<output>
				<port id="1" precision="FP32">
					<dim>1</dim>
					<dim>128</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</output>
		</layer>
		<layer id="69" name="batch_normalization_5/FusedBatchNormV3/variance/Fused_Add_" type="Add" version="opset1">
			<data auto_broadcast="numpy"/>
			<rt_info>
				<attribute name="fused_names" value="batch_normalization_5/FusedBatchNormV3/variance/Fused_Add_" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP32">
					<dim>1</dim>
					<dim>128</dim>
					<dim>12</dim>
					<dim>12</dim>
				</port>
				<port id="1" precision="FP32">
					<dim>1</dim>
					<dim>128</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</input>
			<output>
				<port id="2" names="batch_normalization_5/FusedBatchNormV3:0" precision="FP32">
					<dim>1</dim>
					<dim>128</dim>
					<dim>12</dim>
					<dim>12</dim>
				</port>
			</output>
		</layer>
		<layer id="70" name="conv2d_6/kernel_compressed" type="Const" version="opset1">
			<data element_type="f16" offset="279008" shape="128, 128, 3, 3" size="294912"/>
			<output>
				<port id="0" precision="FP16">
					<dim>128</dim>
					<dim>128</dim>
					<dim>3</dim>
					<dim>3</dim>
				</port>
			</output>
		</layer>
		<layer id="71" name="conv2d_6/kernel" type="Convert" version="opset1">
			<data destination_type="f32"/>
			<rt_info>
				<attribute name="decompression" version="0"/>
				<attribute name="fused_names" value="conv2d_6/kernel" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP16">
					<dim>128</dim>
					<dim>128</dim>
					<dim>3</dim>
					<dim>3</dim>
				</port>
			</input>
			<output>
				<port id="1" names="conv2d_6/kernel:0" precision="FP32">
					<dim>128</dim>
					<dim>128</dim>
					<dim>3</dim>
					<dim>3</dim>
				</port>
			</output>
		</layer>
		<layer id="72" name="conv2d_6/Conv2D" type="Convolution" version="opset1">
			<data auto_pad="same_upper" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
			<rt_info>
				<attribute name="fused_names" value="conv2d_6/Conv2D" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP32">
					<dim>1</dim>
					<dim>128</dim>
					<dim>12</dim>
					<dim>12</dim>
				</port>
				<port id="1" precision="FP32">
					<dim>128</dim>
					<dim>128</dim>
					<dim>3</dim>
					<dim>3</dim>
				</port>
			</input>
			<output>
				<port id="2" names="conv2d_6/Conv2D:0" precision="FP32">
					<dim>1</dim>
					<dim>128</dim>
					<dim>12</dim>
					<dim>12</dim>
				</port>
			</output>
		</layer>
		<layer id="73" name="conv2d_6/bias_compressed" type="Const" version="opset1">
			<data element_type="f16" offset="573920" shape="1, 128, 1, 1" size="256"/>
			<output>
				<port id="0" precision="FP16">
					<dim>1</dim>
					<dim>128</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</output>
		</layer>
		<layer id="74" name="conv2d_6/bias" type="Convert" version="opset1">
			<data destination_type="f32"/>
			<rt_info>
				<attribute name="decompression" version="0"/>
				<attribute name="fused_names" value="conv2d_6/bias" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP16">
					<dim>1</dim>
					<dim>128</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</input>
			<output>
				<port id="1" names="conv2d_6/bias:0" precision="FP32">
					<dim>1</dim>
					<dim>128</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</output>
		</layer>
		<layer id="75" name="conv2d_6/BiasAdd/Add" type="Add" version="opset1">
			<data auto_broadcast="numpy"/>
			<rt_info>
				<attribute name="fused_names" value="conv2d_6/BiasAdd/Add" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP32">
					<dim>1</dim>
					<dim>128</dim>
					<dim>12</dim>
					<dim>12</dim>
				</port>
				<port id="1" precision="FP32">
					<dim>1</dim>
					<dim>128</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</input>
			<output>
				<port id="2" names="conv2d_6/BiasAdd:0" precision="FP32">
					<dim>1</dim>
					<dim>128</dim>
					<dim>12</dim>
					<dim>12</dim>
				</port>
			</output>
		</layer>
		<layer id="76" name="activation_6/Elu" type="Elu" version="opset1">
			<data alpha="1"/>
			<rt_info>
				<attribute name="fused_names" value="activation_6/Elu" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP32">
					<dim>1</dim>
					<dim>128</dim>
					<dim>12</dim>
					<dim>12</dim>
				</port>
			</input>
			<output>
				<port id="1" names="activation_6/Elu:0" precision="FP32">
					<dim>1</dim>
					<dim>128</dim>
					<dim>12</dim>
					<dim>12</dim>
				</port>
			</output>
		</layer>
		<layer id="77" name="data_mul_20712075_compressed" type="Const" version="opset1">
			<data element_type="f16" offset="574176" shape="1, 128, 1, 1" size="256"/>
			<output>
				<port id="0" precision="FP16">
					<dim>1</dim>
					<dim>128</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</output>
		</layer>
		<layer id="78" name="data_mul_20712075" type="Convert" version="opset1">
			<data destination_type="f32"/>
			<rt_info>
				<attribute name="decompression" version="0"/>
				<attribute name="fused_names" value="data_mul_20712075" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP16">
					<dim>1</dim>
					<dim>128</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</input>
			<output>
				<port id="1" precision="FP32">
					<dim>1</dim>
					<dim>128</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</output>
		</layer>
		<layer id="79" name="batch_normalization_6/FusedBatchNormV3/mean/Fused_Mul_" type="Multiply" version="opset1">
			<data auto_broadcast="numpy"/>
			<rt_info>
				<attribute name="fused_names" value="batch_normalization_6/FusedBatchNormV3/mean/Fused_Mul_" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP32">
					<dim>1</dim>
					<dim>128</dim>
					<dim>12</dim>
					<dim>12</dim>
				</port>
				<port id="1" precision="FP32">
					<dim>1</dim>
					<dim>128</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</input>
			<output>
				<port id="2" precision="FP32">
					<dim>1</dim>
					<dim>128</dim>
					<dim>12</dim>
					<dim>12</dim>
				</port>
			</output>
		</layer>
		<layer id="80" name="data_add_20722077_compressed" type="Const" version="opset1">
			<data element_type="f16" offset="574432" shape="1, 128, 1, 1" size="256"/>
			<output>
				<port id="0" precision="FP16">
					<dim>1</dim>
					<dim>128</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</output>
		</layer>
		<layer id="81" name="data_add_20722077" type="Convert" version="opset1">
			<data destination_type="f32"/>
			<rt_info>
				<attribute name="decompression" version="0"/>
				<attribute name="fused_names" value="data_add_20722077" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP16">
					<dim>1</dim>
					<dim>128</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</input>
			<output>
				<port id="1" precision="FP32">
					<dim>1</dim>
					<dim>128</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</output>
		</layer>
		<layer id="82" name="batch_normalization_6/FusedBatchNormV3/variance/Fused_Add_" type="Add" version="opset1">
			<data auto_broadcast="numpy"/>
			<rt_info>
				<attribute name="fused_names" value="batch_normalization_6/FusedBatchNormV3/variance/Fused_Add_" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP32">
					<dim>1</dim>
					<dim>128</dim>
					<dim>12</dim>
					<dim>12</dim>
				</port>
				<port id="1" precision="FP32">
					<dim>1</dim>
					<dim>128</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</input>
			<output>
				<port id="2" names="batch_normalization_6/FusedBatchNormV3:0" precision="FP32">
					<dim>1</dim>
					<dim>128</dim>
					<dim>12</dim>
					<dim>12</dim>
				</port>
			</output>
		</layer>
		<layer id="83" name="max_pooling2d_3/MaxPool" type="MaxPool" version="opset8">
			<data auto_pad="valid" axis="0" dilations="1, 1" index_element_type="i64" kernel="2, 2" pads_begin="0, 0" pads_end="0, 0" rounding_type="floor" strides="2, 2"/>
			<rt_info>
				<attribute name="fused_names" value="max_pooling2d_3/MaxPool" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP32">
					<dim>1</dim>
					<dim>128</dim>
					<dim>12</dim>
					<dim>12</dim>
				</port>
			</input>
			<output>
				<port id="1" names="max_pooling2d_3/MaxPool:0" precision="FP32">
					<dim>1</dim>
					<dim>128</dim>
					<dim>6</dim>
					<dim>6</dim>
				</port>
				<port id="2" precision="I64">
					<dim>1</dim>
					<dim>128</dim>
					<dim>6</dim>
					<dim>6</dim>
				</port>
			</output>
		</layer>
		<layer id="84" name="conv2d_7/kernel_compressed" type="Const" version="opset1">
			<data element_type="f16" offset="574688" shape="256, 128, 3, 3" size="589824"/>
			<output>
				<port id="0" precision="FP16">
					<dim>256</dim>
					<dim>128</dim>
					<dim>3</dim>
					<dim>3</dim>
				</port>
			</output>
		</layer>
		<layer id="85" name="conv2d_7/kernel" type="Convert" version="opset1">
			<data destination_type="f32"/>
			<rt_info>
				<attribute name="decompression" version="0"/>
				<attribute name="fused_names" value="conv2d_7/kernel" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP16">
					<dim>256</dim>
					<dim>128</dim>
					<dim>3</dim>
					<dim>3</dim>
				</port>
			</input>
			<output>
				<port id="1" names="conv2d_7/kernel:0" precision="FP32">
					<dim>256</dim>
					<dim>128</dim>
					<dim>3</dim>
					<dim>3</dim>
				</port>
			</output>
		</layer>
		<layer id="86" name="conv2d_7/Conv2D" type="Convolution" version="opset1">
			<data auto_pad="same_upper" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
			<rt_info>
				<attribute name="fused_names" value="conv2d_7/Conv2D" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP32">
					<dim>1</dim>
					<dim>128</dim>
					<dim>6</dim>
					<dim>6</dim>
				</port>
				<port id="1" precision="FP32">
					<dim>256</dim>
					<dim>128</dim>
					<dim>3</dim>
					<dim>3</dim>
				</port>
			</input>
			<output>
				<port id="2" names="conv2d_7/Conv2D:0" precision="FP32">
					<dim>1</dim>
					<dim>256</dim>
					<dim>6</dim>
					<dim>6</dim>
				</port>
			</output>
		</layer>
		<layer id="87" name="conv2d_7/bias_compressed" type="Const" version="opset1">
			<data element_type="f16" offset="1164512" shape="1, 256, 1, 1" size="512"/>
			<output>
				<port id="0" precision="FP16">
					<dim>1</dim>
					<dim>256</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</output>
		</layer>
		<layer id="88" name="conv2d_7/bias" type="Convert" version="opset1">
			<data destination_type="f32"/>
			<rt_info>
				<attribute name="decompression" version="0"/>
				<attribute name="fused_names" value="conv2d_7/bias" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP16">
					<dim>1</dim>
					<dim>256</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</input>
			<output>
				<port id="1" names="conv2d_7/bias:0" precision="FP32">
					<dim>1</dim>
					<dim>256</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</output>
		</layer>
		<layer id="89" name="conv2d_7/BiasAdd/Add" type="Add" version="opset1">
			<data auto_broadcast="numpy"/>
			<rt_info>
				<attribute name="fused_names" value="conv2d_7/BiasAdd/Add" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP32">
					<dim>1</dim>
					<dim>256</dim>
					<dim>6</dim>
					<dim>6</dim>
				</port>
				<port id="1" precision="FP32">
					<dim>1</dim>
					<dim>256</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</input>
			<output>
				<port id="2" names="conv2d_7/BiasAdd:0" precision="FP32">
					<dim>1</dim>
					<dim>256</dim>
					<dim>6</dim>
					<dim>6</dim>
				</port>
			</output>
		</layer>
		<layer id="90" name="activation_7/Elu" type="Elu" version="opset1">
			<data alpha="1"/>
			<rt_info>
				<attribute name="fused_names" value="activation_7/Elu" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP32">
					<dim>1</dim>
					<dim>256</dim>
					<dim>6</dim>
					<dim>6</dim>
				</port>
			</input>
			<output>
				<port id="1" names="activation_7/Elu:0" precision="FP32">
					<dim>1</dim>
					<dim>256</dim>
					<dim>6</dim>
					<dim>6</dim>
				</port>
			</output>
		</layer>
		<layer id="91" name="data_mul_20792083_compressed" type="Const" version="opset1">
			<data element_type="f16" offset="1165024" shape="1, 256, 1, 1" size="512"/>
			<output>
				<port id="0" precision="FP16">
					<dim>1</dim>
					<dim>256</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</output>
		</layer>
		<layer id="92" name="data_mul_20792083" type="Convert" version="opset1">
			<data destination_type="f32"/>
			<rt_info>
				<attribute name="decompression" version="0"/>
				<attribute name="fused_names" value="data_mul_20792083" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP16">
					<dim>1</dim>
					<dim>256</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</input>
			<output>
				<port id="1" precision="FP32">
					<dim>1</dim>
					<dim>256</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</output>
		</layer>
		<layer id="93" name="batch_normalization_7/FusedBatchNormV3/mean/Fused_Mul_" type="Multiply" version="opset1">
			<data auto_broadcast="numpy"/>
			<rt_info>
				<attribute name="fused_names" value="batch_normalization_7/FusedBatchNormV3/mean/Fused_Mul_" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP32">
					<dim>1</dim>
					<dim>256</dim>
					<dim>6</dim>
					<dim>6</dim>
				</port>
				<port id="1" precision="FP32">
					<dim>1</dim>
					<dim>256</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</input>
			<output>
				<port id="2" precision="FP32">
					<dim>1</dim>
					<dim>256</dim>
					<dim>6</dim>
					<dim>6</dim>
				</port>
			</output>
		</layer>
		<layer id="94" name="data_add_20802085_compressed" type="Const" version="opset1">
			<data element_type="f16" offset="1165536" shape="1, 256, 1, 1" size="512"/>
			<output>
				<port id="0" precision="FP16">
					<dim>1</dim>
					<dim>256</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</output>
		</layer>
		<layer id="95" name="data_add_20802085" type="Convert" version="opset1">
			<data destination_type="f32"/>
			<rt_info>
				<attribute name="decompression" version="0"/>
				<attribute name="fused_names" value="data_add_20802085" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP16">
					<dim>1</dim>
					<dim>256</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</input>
			<output>
				<port id="1" precision="FP32">
					<dim>1</dim>
					<dim>256</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</output>
		</layer>
		<layer id="96" name="batch_normalization_7/FusedBatchNormV3/variance/Fused_Add_" type="Add" version="opset1">
			<data auto_broadcast="numpy"/>
			<rt_info>
				<attribute name="fused_names" value="batch_normalization_7/FusedBatchNormV3/variance/Fused_Add_" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP32">
					<dim>1</dim>
					<dim>256</dim>
					<dim>6</dim>
					<dim>6</dim>
				</port>
				<port id="1" precision="FP32">
					<dim>1</dim>
					<dim>256</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</input>
			<output>
				<port id="2" names="batch_normalization_7/FusedBatchNormV3:0" precision="FP32">
					<dim>1</dim>
					<dim>256</dim>
					<dim>6</dim>
					<dim>6</dim>
				</port>
			</output>
		</layer>
		<layer id="97" name="conv2d_8/kernel_compressed" type="Const" version="opset1">
			<data element_type="f16" offset="1166048" shape="256, 256, 3, 3" size="1179648"/>
			<output>
				<port id="0" precision="FP16">
					<dim>256</dim>
					<dim>256</dim>
					<dim>3</dim>
					<dim>3</dim>
				</port>
			</output>
		</layer>
		<layer id="98" name="conv2d_8/kernel" type="Convert" version="opset1">
			<data destination_type="f32"/>
			<rt_info>
				<attribute name="decompression" version="0"/>
				<attribute name="fused_names" value="conv2d_8/kernel" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP16">
					<dim>256</dim>
					<dim>256</dim>
					<dim>3</dim>
					<dim>3</dim>
				</port>
			</input>
			<output>
				<port id="1" names="conv2d_8/kernel:0" precision="FP32">
					<dim>256</dim>
					<dim>256</dim>
					<dim>3</dim>
					<dim>3</dim>
				</port>
			</output>
		</layer>
		<layer id="99" name="conv2d_8/Conv2D" type="Convolution" version="opset1">
			<data auto_pad="same_upper" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
			<rt_info>
				<attribute name="fused_names" value="conv2d_8/Conv2D" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP32">
					<dim>1</dim>
					<dim>256</dim>
					<dim>6</dim>
					<dim>6</dim>
				</port>
				<port id="1" precision="FP32">
					<dim>256</dim>
					<dim>256</dim>
					<dim>3</dim>
					<dim>3</dim>
				</port>
			</input>
			<output>
				<port id="2" names="conv2d_8/Conv2D:0" precision="FP32">
					<dim>1</dim>
					<dim>256</dim>
					<dim>6</dim>
					<dim>6</dim>
				</port>
			</output>
		</layer>
		<layer id="100" name="conv2d_8/bias_compressed" type="Const" version="opset1">
			<data element_type="f16" offset="2345696" shape="1, 256, 1, 1" size="512"/>
			<output>
				<port id="0" precision="FP16">
					<dim>1</dim>
					<dim>256</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</output>
		</layer>
		<layer id="101" name="conv2d_8/bias" type="Convert" version="opset1">
			<data destination_type="f32"/>
			<rt_info>
				<attribute name="decompression" version="0"/>
				<attribute name="fused_names" value="conv2d_8/bias" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP16">
					<dim>1</dim>
					<dim>256</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</input>
			<output>
				<port id="1" names="conv2d_8/bias:0" precision="FP32">
					<dim>1</dim>
					<dim>256</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</output>
		</layer>
		<layer id="102" name="conv2d_8/BiasAdd/Add" type="Add" version="opset1">
			<data auto_broadcast="numpy"/>
			<rt_info>
				<attribute name="fused_names" value="conv2d_8/BiasAdd/Add" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP32">
					<dim>1</dim>
					<dim>256</dim>
					<dim>6</dim>
					<dim>6</dim>
				</port>
				<port id="1" precision="FP32">
					<dim>1</dim>
					<dim>256</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</input>
			<output>
				<port id="2" names="conv2d_8/BiasAdd:0" precision="FP32">
					<dim>1</dim>
					<dim>256</dim>
					<dim>6</dim>
					<dim>6</dim>
				</port>
			</output>
		</layer>
		<layer id="103" name="activation_8/Elu" type="Elu" version="opset1">
			<data alpha="1"/>
			<rt_info>
				<attribute name="fused_names" value="activation_8/Elu" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP32">
					<dim>1</dim>
					<dim>256</dim>
					<dim>6</dim>
					<dim>6</dim>
				</port>
			</input>
			<output>
				<port id="1" names="activation_8/Elu:0" precision="FP32">
					<dim>1</dim>
					<dim>256</dim>
					<dim>6</dim>
					<dim>6</dim>
				</port>
			</output>
		</layer>
		<layer id="104" name="data_mul_20872091_compressed" type="Const" version="opset1">
			<data element_type="f16" offset="2346208" shape="1, 256, 1, 1" size="512"/>
			<output>
				<port id="0" precision="FP16">
					<dim>1</dim>
					<dim>256</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</output>
		</layer>
		<layer id="105" name="data_mul_20872091" type="Convert" version="opset1">
			<data destination_type="f32"/>
			<rt_info>
				<attribute name="decompression" version="0"/>
				<attribute name="fused_names" value="data_mul_20872091" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP16">
					<dim>1</dim>
					<dim>256</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</input>
			<output>
				<port id="1" precision="FP32">
					<dim>1</dim>
					<dim>256</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</output>
		</layer>
		<layer id="106" name="batch_normalization_8/FusedBatchNormV3/mean/Fused_Mul_" type="Multiply" version="opset1">
			<data auto_broadcast="numpy"/>
			<rt_info>
				<attribute name="fused_names" value="batch_normalization_8/FusedBatchNormV3/mean/Fused_Mul_" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP32">
					<dim>1</dim>
					<dim>256</dim>
					<dim>6</dim>
					<dim>6</dim>
				</port>
				<port id="1" precision="FP32">
					<dim>1</dim>
					<dim>256</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</input>
			<output>
				<port id="2" precision="FP32">
					<dim>1</dim>
					<dim>256</dim>
					<dim>6</dim>
					<dim>6</dim>
				</port>
			</output>
		</layer>
		<layer id="107" name="data_add_20882093_compressed" type="Const" version="opset1">
			<data element_type="f16" offset="2346720" shape="1, 256, 1, 1" size="512"/>
			<output>
				<port id="0" precision="FP16">
					<dim>1</dim>
					<dim>256</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</output>
		</layer>
		<layer id="108" name="data_add_20882093" type="Convert" version="opset1">
			<data destination_type="f32"/>
			<rt_info>
				<attribute name="decompression" version="0"/>
				<attribute name="fused_names" value="data_add_20882093" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP16">
					<dim>1</dim>
					<dim>256</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</input>
			<output>
				<port id="1" precision="FP32">
					<dim>1</dim>
					<dim>256</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</output>
		</layer>
		<layer id="109" name="batch_normalization_8/FusedBatchNormV3/variance/Fused_Add_" type="Add" version="opset1">
			<data auto_broadcast="numpy"/>
			<rt_info>
				<attribute name="fused_names" value="batch_normalization_8/FusedBatchNormV3/variance/Fused_Add_" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP32">
					<dim>1</dim>
					<dim>256</dim>
					<dim>6</dim>
					<dim>6</dim>
				</port>
				<port id="1" precision="FP32">
					<dim>1</dim>
					<dim>256</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</input>
			<output>
				<port id="2" names="batch_normalization_8/FusedBatchNormV3:0" precision="FP32">
					<dim>1</dim>
					<dim>256</dim>
					<dim>6</dim>
					<dim>6</dim>
				</port>
			</output>
		</layer>
		<layer id="110" name="max_pooling2d_4/MaxPool" type="MaxPool" version="opset8">
			<data auto_pad="valid" axis="0" dilations="1, 1" index_element_type="i64" kernel="2, 2" pads_begin="0, 0" pads_end="0, 0" rounding_type="floor" strides="2, 2"/>
			<rt_info>
				<attribute name="fused_names" value="max_pooling2d_4/MaxPool" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP32">
					<dim>1</dim>
					<dim>256</dim>
					<dim>6</dim>
					<dim>6</dim>
				</port>
			</input>
			<output>
				<port id="1" precision="FP32">
					<dim>1</dim>
					<dim>256</dim>
					<dim>3</dim>
					<dim>3</dim>
				</port>
				<port id="2" precision="I64">
					<dim>1</dim>
					<dim>256</dim>
					<dim>3</dim>
					<dim>3</dim>
				</port>
			</output>
		</layer>
		<layer id="111" name="max_pooling2d_4/MaxPool/Transpose/value27374039" type="Const" version="opset1">
			<data element_type="i64" offset="2347232" shape="4" size="32"/>
			<rt_info>
				<attribute name="fused_names" value="max_pooling2d_4/MaxPool/Transpose/value27374039" version="0"/>
			</rt_info>
			<output>
				<port id="0" precision="I64">
					<dim>4</dim>
				</port>
			</output>
		</layer>
		<layer id="112" name="max_pooling2d_4/MaxPool/Transpose" type="Transpose" version="opset1">
			<rt_info>
				<attribute name="fused_names" value="max_pooling2d_4/MaxPool/Transpose" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP32">
					<dim>1</dim>
					<dim>256</dim>
					<dim>3</dim>
					<dim>3</dim>
				</port>
				<port id="1" precision="I64">
					<dim>4</dim>
				</port>
			</input>
			<output>
				<port id="2" names="max_pooling2d_4/MaxPool:0" precision="FP32">
					<dim>1</dim>
					<dim>3</dim>
					<dim>3</dim>
					<dim>256</dim>
				</port>
			</output>
		</layer>
		<layer id="113" name="flatten_1/Const" type="Const" version="opset1">
			<data element_type="i64" offset="2347264" shape="2" size="16"/>
			<rt_info>
				<attribute name="fused_names" value="flatten_1/Const" version="0"/>
			</rt_info>
			<output>
				<port id="0" names="flatten_1/Const:0" precision="I64">
					<dim>2</dim>
				</port>
			</output>
		</layer>
		<layer id="114" name="flatten_1/Reshape" type="Reshape" version="opset1">
			<data special_zero="false"/>
			<rt_info>
				<attribute name="fused_names" value="flatten_1/Reshape" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP32">
					<dim>1</dim>
					<dim>3</dim>
					<dim>3</dim>
					<dim>256</dim>
				</port>
				<port id="1" precision="I64">
					<dim>2</dim>
				</port>
			</input>
			<output>
				<port id="2" names="flatten_1/Reshape:0" precision="FP32">
					<dim>1</dim>
					<dim>2304</dim>
				</port>
			</output>
		</layer>
		<layer id="115" name="dense_1/kernel_compressed" type="Const" version="opset1">
			<data element_type="f16" offset="2347280" shape="64, 2304" size="294912"/>
			<output>
				<port id="0" precision="FP16">
					<dim>64</dim>
					<dim>2304</dim>
				</port>
			</output>
		</layer>
		<layer id="116" name="dense_1/kernel" type="Convert" version="opset1">
			<data destination_type="f32"/>
			<rt_info>
				<attribute name="decompression" version="0"/>
				<attribute name="fused_names" value="dense_1/kernel" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP16">
					<dim>64</dim>
					<dim>2304</dim>
				</port>
			</input>
			<output>
				<port id="1" names="dense_1/kernel:0" precision="FP32">
					<dim>64</dim>
					<dim>2304</dim>
				</port>
			</output>
		</layer>
		<layer id="117" name="dense_1/MatMul" type="MatMul" version="opset1">
			<data transpose_a="false" transpose_b="true"/>
			<rt_info>
				<attribute name="fused_names" value="dense_1/MatMul" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP32">
					<dim>1</dim>
					<dim>2304</dim>
				</port>
				<port id="1" precision="FP32">
					<dim>64</dim>
					<dim>2304</dim>
				</port>
			</input>
			<output>
				<port id="2" names="dense_1/MatMul:0" precision="FP32">
					<dim>1</dim>
					<dim>64</dim>
				</port>
			</output>
		</layer>
		<layer id="118" name="dense_1/bias_compressed" type="Const" version="opset1">
			<data element_type="f16" offset="2642192" shape="1, 64" size="128"/>
			<output>
				<port id="0" precision="FP16">
					<dim>1</dim>
					<dim>64</dim>
				</port>
			</output>
		</layer>
		<layer id="119" name="dense_1/bias" type="Convert" version="opset1">
			<data destination_type="f32"/>
			<rt_info>
				<attribute name="decompression" version="0"/>
				<attribute name="fused_names" value="dense_1/bias" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP16">
					<dim>1</dim>
					<dim>64</dim>
				</port>
			</input>
			<output>
				<port id="1" names="dense_1/bias:0" precision="FP32">
					<dim>1</dim>
					<dim>64</dim>
				</port>
			</output>
		</layer>
		<layer id="120" name="dense_1/BiasAdd/Add" type="Add" version="opset1">
			<data auto_broadcast="numpy"/>
			<rt_info>
				<attribute name="fused_names" value="dense_1/BiasAdd/Add" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP32">
					<dim>1</dim>
					<dim>64</dim>
				</port>
				<port id="1" precision="FP32">
					<dim>1</dim>
					<dim>64</dim>
				</port>
			</input>
			<output>
				<port id="2" names="dense_1/BiasAdd:0" precision="FP32">
					<dim>1</dim>
					<dim>64</dim>
				</port>
			</output>
		</layer>
		<layer id="121" name="activation_9/Elu" type="Elu" version="opset1">
			<data alpha="1"/>
			<rt_info>
				<attribute name="fused_names" value="activation_9/Elu" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP32">
					<dim>1</dim>
					<dim>64</dim>
				</port>
			</input>
			<output>
				<port id="1" names="activation_9/Elu:0" precision="FP32">
					<dim>1</dim>
					<dim>64</dim>
				</port>
			</output>
		</layer>
		<layer id="122" name="batch_normalization_9/batchnorm/mul_compressed" type="Const" version="opset1">
			<data element_type="f16" offset="2642320" shape="1, 64" size="128"/>
			<output>
				<port id="0" precision="FP16">
					<dim>1</dim>
					<dim>64</dim>
				</port>
			</output>
		</layer>
		<layer id="123" name="batch_normalization_9/batchnorm/mul" type="Convert" version="opset1">
			<data destination_type="f32"/>
			<rt_info>
				<attribute name="decompression" version="0"/>
				<attribute name="fused_names" value="batch_normalization_9/batchnorm/mul" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP16">
					<dim>1</dim>
					<dim>64</dim>
				</port>
			</input>
			<output>
				<port id="1" names="batch_normalization_9/batchnorm/mul:0" precision="FP32">
					<dim>1</dim>
					<dim>64</dim>
				</port>
			</output>
		</layer>
		<layer id="124" name="batch_normalization_9/batchnorm/mul_1" type="Multiply" version="opset1">
			<data auto_broadcast="numpy"/>
			<rt_info>
				<attribute name="fused_names" value="batch_normalization_9/batchnorm/mul_1" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP32">
					<dim>1</dim>
					<dim>64</dim>
				</port>
				<port id="1" precision="FP32">
					<dim>1</dim>
					<dim>64</dim>
				</port>
			</input>
			<output>
				<port id="2" names="batch_normalization_9/batchnorm/mul_1:0" precision="FP32">
					<dim>1</dim>
					<dim>64</dim>
				</port>
			</output>
		</layer>
		<layer id="125" name="batch_normalization_9/batchnorm/sub_compressed" type="Const" version="opset1">
			<data element_type="f16" offset="2642448" shape="1, 64" size="128"/>
			<output>
				<port id="0" precision="FP16">
					<dim>1</dim>
					<dim>64</dim>
				</port>
			</output>
		</layer>
		<layer id="126" name="batch_normalization_9/batchnorm/sub" type="Convert" version="opset1">
			<data destination_type="f32"/>
			<rt_info>
				<attribute name="decompression" version="0"/>
				<attribute name="fused_names" value="batch_normalization_9/batchnorm/sub" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP16">
					<dim>1</dim>
					<dim>64</dim>
				</port>
			</input>
			<output>
				<port id="1" names="batch_normalization_9/batchnorm/sub:0" precision="FP32">
					<dim>1</dim>
					<dim>64</dim>
				</port>
			</output>
		</layer>
		<layer id="127" name="batch_normalization_9/batchnorm/add_1" type="Add" version="opset1">
			<data auto_broadcast="numpy"/>
			<rt_info>
				<attribute name="fused_names" value="batch_normalization_9/batchnorm/add_1" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP32">
					<dim>1</dim>
					<dim>64</dim>
				</port>
				<port id="1" precision="FP32">
					<dim>1</dim>
					<dim>64</dim>
				</port>
			</input>
			<output>
				<port id="2" names="batch_normalization_9/batchnorm/add_1:0" precision="FP32">
					<dim>1</dim>
					<dim>64</dim>
				</port>
			</output>
		</layer>
		<layer id="128" name="dense_2/kernel_compressed" type="Const" version="opset1">
			<data element_type="f16" offset="2642576" shape="64, 64" size="8192"/>
			<output>
				<port id="0" precision="FP16">
					<dim>64</dim>
					<dim>64</dim>
				</port>
			</output>
		</layer>
		<layer id="129" name="dense_2/kernel" type="Convert" version="opset1">
			<data destination_type="f32"/>
			<rt_info>
				<attribute name="decompression" version="0"/>
				<attribute name="fused_names" value="dense_2/kernel" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP16">
					<dim>64</dim>
					<dim>64</dim>
				</port>
			</input>
			<output>
				<port id="1" names="dense_2/kernel:0" precision="FP32">
					<dim>64</dim>
					<dim>64</dim>
				</port>
			</output>
		</layer>
		<layer id="130" name="dense_2/MatMul" type="MatMul" version="opset1">
			<data transpose_a="false" transpose_b="true"/>
			<rt_info>
				<attribute name="fused_names" value="dense_2/MatMul" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP32">
					<dim>1</dim>
					<dim>64</dim>
				</port>
				<port id="1" precision="FP32">
					<dim>64</dim>
					<dim>64</dim>
				</port>
			</input>
			<output>
				<port id="2" names="dense_2/MatMul:0" precision="FP32">
					<dim>1</dim>
					<dim>64</dim>
				</port>
			</output>
		</layer>
		<layer id="131" name="dense_2/bias_compressed" type="Const" version="opset1">
			<data element_type="f16" offset="2650768" shape="1, 64" size="128"/>
			<output>
				<port id="0" precision="FP16">
					<dim>1</dim>
					<dim>64</dim>
				</port>
			</output>
		</layer>
		<layer id="132" name="dense_2/bias" type="Convert" version="opset1">
			<data destination_type="f32"/>
			<rt_info>
				<attribute name="decompression" version="0"/>
				<attribute name="fused_names" value="dense_2/bias" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP16">
					<dim>1</dim>
					<dim>64</dim>
				</port>
			</input>
			<output>
				<port id="1" names="dense_2/bias:0" precision="FP32">
					<dim>1</dim>
					<dim>64</dim>
				</port>
			</output>
		</layer>
		<layer id="133" name="dense_2/BiasAdd/Add" type="Add" version="opset1">
			<data auto_broadcast="numpy"/>
			<rt_info>
				<attribute name="fused_names" value="dense_2/BiasAdd/Add" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP32">
					<dim>1</dim>
					<dim>64</dim>
				</port>
				<port id="1" precision="FP32">
					<dim>1</dim>
					<dim>64</dim>
				</port>
			</input>
			<output>
				<port id="2" names="dense_2/BiasAdd:0" precision="FP32">
					<dim>1</dim>
					<dim>64</dim>
				</port>
			</output>
		</layer>
		<layer id="134" name="activation_10/Elu" type="Elu" version="opset1">
			<data alpha="1"/>
			<rt_info>
				<attribute name="fused_names" value="activation_10/Elu" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP32">
					<dim>1</dim>
					<dim>64</dim>
				</port>
			</input>
			<output>
				<port id="1" names="activation_10/Elu:0" precision="FP32">
					<dim>1</dim>
					<dim>64</dim>
				</port>
			</output>
		</layer>
		<layer id="135" name="batch_normalization_10/batchnorm/mul_compressed" type="Const" version="opset1">
			<data element_type="f16" offset="2650896" shape="1, 64" size="128"/>
			<output>
				<port id="0" precision="FP16">
					<dim>1</dim>
					<dim>64</dim>
				</port>
			</output>
		</layer>
		<layer id="136" name="batch_normalization_10/batchnorm/mul" type="Convert" version="opset1">
			<data destination_type="f32"/>
			<rt_info>
				<attribute name="decompression" version="0"/>
				<attribute name="fused_names" value="batch_normalization_10/batchnorm/mul" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP16">
					<dim>1</dim>
					<dim>64</dim>
				</port>
			</input>
			<output>
				<port id="1" names="batch_normalization_10/batchnorm/mul:0" precision="FP32">
					<dim>1</dim>
					<dim>64</dim>
				</port>
			</output>
		</layer>
		<layer id="137" name="batch_normalization_10/batchnorm/mul_1" type="Multiply" version="opset1">
			<data auto_broadcast="numpy"/>
			<rt_info>
				<attribute name="fused_names" value="batch_normalization_10/batchnorm/mul_1" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP32">
					<dim>1</dim>
					<dim>64</dim>
				</port>
				<port id="1" precision="FP32">
					<dim>1</dim>
					<dim>64</dim>
				</port>
			</input>
			<output>
				<port id="2" names="batch_normalization_10/batchnorm/mul_1:0" precision="FP32">
					<dim>1</dim>
					<dim>64</dim>
				</port>
			</output>
		</layer>
		<layer id="138" name="batch_normalization_10/batchnorm/sub_compressed" type="Const" version="opset1">
			<data element_type="f16" offset="2651024" shape="1, 64" size="128"/>
			<output>
				<port id="0" precision="FP16">
					<dim>1</dim>
					<dim>64</dim>
				</port>
			</output>
		</layer>
		<layer id="139" name="batch_normalization_10/batchnorm/sub" type="Convert" version="opset1">
			<data destination_type="f32"/>
			<rt_info>
				<attribute name="decompression" version="0"/>
				<attribute name="fused_names" value="batch_normalization_10/batchnorm/sub" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP16">
					<dim>1</dim>
					<dim>64</dim>
				</port>
			</input>
			<output>
				<port id="1" names="batch_normalization_10/batchnorm/sub:0" precision="FP32">
					<dim>1</dim>
					<dim>64</dim>
				</port>
			</output>
		</layer>
		<layer id="140" name="batch_normalization_10/batchnorm/add_1" type="Add" version="opset1">
			<data auto_broadcast="numpy"/>
			<rt_info>
				<attribute name="fused_names" value="batch_normalization_10/batchnorm/add_1" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP32">
					<dim>1</dim>
					<dim>64</dim>
				</port>
				<port id="1" precision="FP32">
					<dim>1</dim>
					<dim>64</dim>
				</port>
			</input>
			<output>
				<port id="2" names="batch_normalization_10/batchnorm/add_1:0" precision="FP32">
					<dim>1</dim>
					<dim>64</dim>
				</port>
			</output>
		</layer>
		<layer id="141" name="dense_3/kernel_compressed" type="Const" version="opset1">
			<data element_type="f16" offset="2651152" shape="5, 64" size="640"/>
			<output>
				<port id="0" precision="FP16">
					<dim>5</dim>
					<dim>64</dim>
				</port>
			</output>
		</layer>
		<layer id="142" name="dense_3/kernel" type="Convert" version="opset1">
			<data destination_type="f32"/>
			<rt_info>
				<attribute name="decompression" version="0"/>
				<attribute name="fused_names" value="dense_3/kernel" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP16">
					<dim>5</dim>
					<dim>64</dim>
				</port>
			</input>
			<output>
				<port id="1" names="dense_3/kernel:0" precision="FP32">
					<dim>5</dim>
					<dim>64</dim>
				</port>
			</output>
		</layer>
		<layer id="143" name="dense_3/MatMul" type="MatMul" version="opset1">
			<data transpose_a="false" transpose_b="true"/>
			<rt_info>
				<attribute name="fused_names" value="dense_3/MatMul" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP32">
					<dim>1</dim>
					<dim>64</dim>
				</port>
				<port id="1" precision="FP32">
					<dim>5</dim>
					<dim>64</dim>
				</port>
			</input>
			<output>
				<port id="2" names="dense_3/MatMul:0" precision="FP32">
					<dim>1</dim>
					<dim>5</dim>
				</port>
			</output>
		</layer>
		<layer id="144" name="dense_3/bias_compressed" type="Const" version="opset1">
			<data element_type="f16" offset="2651792" shape="1, 5" size="10"/>
			<output>
				<port id="0" precision="FP16">
					<dim>1</dim>
					<dim>5</dim>
				</port>
			</output>
		</layer>
		<layer id="145" name="dense_3/bias" type="Convert" version="opset1">
			<data destination_type="f32"/>
			<rt_info>
				<attribute name="decompression" version="0"/>
				<attribute name="fused_names" value="dense_3/bias" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP16">
					<dim>1</dim>
					<dim>5</dim>
				</port>
			</input>
			<output>
				<port id="1" names="dense_3/bias:0" precision="FP32">
					<dim>1</dim>
					<dim>5</dim>
				</port>
			</output>
		</layer>
		<layer id="146" name="dense_3/BiasAdd/Add" type="Add" version="opset1">
			<data auto_broadcast="numpy"/>
			<rt_info>
				<attribute name="fused_names" value="dense_3/BiasAdd/Add" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP32">
					<dim>1</dim>
					<dim>5</dim>
				</port>
				<port id="1" precision="FP32">
					<dim>1</dim>
					<dim>5</dim>
				</port>
			</input>
			<output>
				<port id="2" names="dense_3/BiasAdd:0" precision="FP32">
					<dim>1</dim>
					<dim>5</dim>
				</port>
			</output>
		</layer>
		<layer id="147" name="activation_11/Softmax" type="SoftMax" version="opset8">
			<data axis="1"/>
			<rt_info>
				<attribute name="fused_names" value="activation_11/Softmax" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP32">
					<dim>1</dim>
					<dim>5</dim>
				</port>
			</input>
			<output>
				<port id="1" names="activation_11/Softmax:0" precision="FP32">
					<dim>1</dim>
					<dim>5</dim>
				</port>
			</output>
		</layer>
		<layer id="148" name="activation_11/Softmax:0" type="Result" version="opset1">
			<rt_info>
				<attribute name="fused_names" value="activation_11/Softmax:0" version="0"/>
			</rt_info>
			<input>
				<port id="0" precision="FP32">
					<dim>1</dim>
					<dim>5</dim>
				</port>
			</input>
		</layer>
	</layers>
	<edges>
		<edge from-layer="0" from-port="0" to-layer="2" to-port="0"/>
		<edge from-layer="1" from-port="0" to-layer="2" to-port="1"/>
		<edge from-layer="2" from-port="2" to-layer="5" to-port="0"/>
		<edge from-layer="3" from-port="0" to-layer="4" to-port="0"/>
		<edge from-layer="4" from-port="1" to-layer="5" to-port="1"/>
		<edge from-layer="5" from-port="2" to-layer="8" to-port="0"/>
		<edge from-layer="6" from-port="0" to-layer="7" to-port="0"/>
		<edge from-layer="7" from-port="1" to-layer="8" to-port="1"/>
		<edge from-layer="8" from-port="2" to-layer="9" to-port="0"/>
		<edge from-layer="9" from-port="1" to-layer="12" to-port="0"/>
		<edge from-layer="10" from-port="0" to-layer="11" to-port="0"/>
		<edge from-layer="11" from-port="1" to-layer="12" to-port="1"/>
		<edge from-layer="12" from-port="2" to-layer="15" to-port="0"/>
		<edge from-layer="13" from-port="0" to-layer="14" to-port="0"/>
		<edge from-layer="14" from-port="1" to-layer="15" to-port="1"/>
		<edge from-layer="15" from-port="2" to-layer="18" to-port="0"/>
		<edge from-layer="16" from-port="0" to-layer="17" to-port="0"/>
		<edge from-layer="17" from-port="1" to-layer="18" to-port="1"/>
		<edge from-layer="18" from-port="2" to-layer="21" to-port="0"/>
		<edge from-layer="19" from-port="0" to-layer="20" to-port="0"/>
		<edge from-layer="20" from-port="1" to-layer="21" to-port="1"/>
		<edge from-layer="21" from-port="2" to-layer="22" to-port="0"/>
		<edge from-layer="22" from-port="1" to-layer="25" to-port="0"/>
		<edge from-layer="23" from-port="0" to-layer="24" to-port="0"/>
		<edge from-layer="24" from-port="1" to-layer="25" to-port="1"/>
		<edge from-layer="25" from-port="2" to-layer="28" to-port="0"/>
		<edge from-layer="26" from-port="0" to-layer="27" to-port="0"/>
		<edge from-layer="27" from-port="1" to-layer="28" to-port="1"/>
		<edge from-layer="28" from-port="2" to-layer="29" to-port="0"/>
		<edge from-layer="29" from-port="1" to-layer="32" to-port="0"/>
		<edge from-layer="30" from-port="0" to-layer="31" to-port="0"/>
		<edge from-layer="31" from-port="1" to-layer="32" to-port="1"/>
		<edge from-layer="32" from-port="2" to-layer="35" to-port="0"/>
		<edge from-layer="33" from-port="0" to-layer="34" to-port="0"/>
		<edge from-layer="34" from-port="1" to-layer="35" to-port="1"/>
		<edge from-layer="35" from-port="2" to-layer="36" to-port="0"/>
		<edge from-layer="36" from-port="1" to-layer="39" to-port="0"/>
		<edge from-layer="37" from-port="0" to-layer="38" to-port="0"/>
		<edge from-layer="38" from-port="1" to-layer="39" to-port="1"/>
		<edge from-layer="39" from-port="2" to-layer="42" to-port="0"/>
		<edge from-layer="40" from-port="0" to-layer="41" to-port="0"/>
		<edge from-layer="41" from-port="1" to-layer="42" to-port="1"/>
		<edge from-layer="42" from-port="2" to-layer="45" to-port="0"/>
		<edge from-layer="43" from-port="0" to-layer="44" to-port="0"/>
		<edge from-layer="44" from-port="1" to-layer="45" to-port="1"/>
		<edge from-layer="45" from-port="2" to-layer="48" to-port="0"/>
		<edge from-layer="46" from-port="0" to-layer="47" to-port="0"/>
		<edge from-layer="47" from-port="1" to-layer="48" to-port="1"/>
		<edge from-layer="48" from-port="2" to-layer="49" to-port="0"/>
		<edge from-layer="49" from-port="1" to-layer="52" to-port="0"/>
		<edge from-layer="50" from-port="0" to-layer="51" to-port="0"/>
		<edge from-layer="51" from-port="1" to-layer="52" to-port="1"/>
		<edge from-layer="52" from-port="2" to-layer="55" to-port="0"/>
		<edge from-layer="53" from-port="0" to-layer="54" to-port="0"/>
		<edge from-layer="54" from-port="1" to-layer="55" to-port="1"/>
		<edge from-layer="55" from-port="2" to-layer="56" to-port="0"/>
		<edge from-layer="56" from-port="1" to-layer="59" to-port="0"/>
		<edge from-layer="57" from-port="0" to-layer="58" to-port="0"/>
		<edge from-layer="58" from-port="1" to-layer="59" to-port="1"/>
		<edge from-layer="59" from-port="2" to-layer="62" to-port="0"/>
		<edge from-layer="60" from-port="0" to-layer="61" to-port="0"/>
		<edge from-layer="61" from-port="1" to-layer="62" to-port="1"/>
		<edge from-layer="62" from-port="2" to-layer="63" to-port="0"/>
		<edge from-layer="63" from-port="1" to-layer="66" to-port="0"/>
		<edge from-layer="64" from-port="0" to-layer="65" to-port="0"/>
		<edge from-layer="65" from-port="1" to-layer="66" to-port="1"/>
		<edge from-layer="66" from-port="2" to-layer="69" to-port="0"/>
		<edge from-layer="67" from-port="0" to-layer="68" to-port="0"/>
		<edge from-layer="68" from-port="1" to-layer="69" to-port="1"/>
		<edge from-layer="69" from-port="2" to-layer="72" to-port="0"/>
		<edge from-layer="70" from-port="0" to-layer="71" to-port="0"/>
		<edge from-layer="71" from-port="1" to-layer="72" to-port="1"/>
		<edge from-layer="72" from-port="2" to-layer="75" to-port="0"/>
		<edge from-layer="73" from-port="0" to-layer="74" to-port="0"/>
		<edge from-layer="74" from-port="1" to-layer="75" to-port="1"/>
		<edge from-layer="75" from-port="2" to-layer="76" to-port="0"/>
		<edge from-layer="76" from-port="1" to-layer="79" to-port="0"/>
		<edge from-layer="77" from-port="0" to-layer="78" to-port="0"/>
		<edge from-layer="78" from-port="1" to-layer="79" to-port="1"/>
		<edge from-layer="79" from-port="2" to-layer="82" to-port="0"/>
		<edge from-layer="80" from-port="0" to-layer="81" to-port="0"/>
		<edge from-layer="81" from-port="1" to-layer="82" to-port="1"/>
		<edge from-layer="82" from-port="2" to-layer="83" to-port="0"/>
		<edge from-layer="83" from-port="1" to-layer="86" to-port="0"/>
		<edge from-layer="84" from-port="0" to-layer="85" to-port="0"/>
		<edge from-layer="85" from-port="1" to-layer="86" to-port="1"/>
		<edge from-layer="86" from-port="2" to-layer="89" to-port="0"/>
		<edge from-layer="87" from-port="0" to-layer="88" to-port="0"/>
		<edge from-layer="88" from-port="1" to-layer="89" to-port="1"/>
		<edge from-layer="89" from-port="2" to-layer="90" to-port="0"/>
		<edge from-layer="90" from-port="1" to-layer="93" to-port="0"/>
		<edge from-layer="91" from-port="0" to-layer="92" to-port="0"/>
		<edge from-layer="92" from-port="1" to-layer="93" to-port="1"/>
		<edge from-layer="93" from-port="2" to-layer="96" to-port="0"/>
		<edge from-layer="94" from-port="0" to-layer="95" to-port="0"/>
		<edge from-layer="95" from-port="1" to-layer="96" to-port="1"/>
		<edge from-layer="96" from-port="2" to-layer="99" to-port="0"/>
		<edge from-layer="97" from-port="0" to-layer="98" to-port="0"/>
		<edge from-layer="98" from-port="1" to-layer="99" to-port="1"/>
		<edge from-layer="99" from-port="2" to-layer="102" to-port="0"/>
		<edge from-layer="100" from-port="0" to-layer="101" to-port="0"/>
		<edge from-layer="101" from-port="1" to-layer="102" to-port="1"/>
		<edge from-layer="102" from-port="2" to-layer="103" to-port="0"/>
		<edge from-layer="103" from-port="1" to-layer="106" to-port="0"/>
		<edge from-layer="104" from-port="0" to-layer="105" to-port="0"/>
		<edge from-layer="105" from-port="1" to-layer="106" to-port="1"/>
		<edge from-layer="106" from-port="2" to-layer="109" to-port="0"/>
		<edge from-layer="107" from-port="0" to-layer="108" to-port="0"/>
		<edge from-layer="108" from-port="1" to-layer="109" to-port="1"/>
		<edge from-layer="109" from-port="2" to-layer="110" to-port="0"/>
		<edge from-layer="110" from-port="1" to-layer="112" to-port="0"/>
		<edge from-layer="111" from-port="0" to-layer="112" to-port="1"/>
		<edge from-layer="112" from-port="2" to-layer="114" to-port="0"/>
		<edge from-layer="113" from-port="0" to-layer="114" to-port="1"/>
		<edge from-layer="114" from-port="2" to-layer="117" to-port="0"/>
		<edge from-layer="115" from-port="0" to-layer="116" to-port="0"/>
		<edge from-layer="116" from-port="1" to-layer="117" to-port="1"/>
		<edge from-layer="117" from-port="2" to-layer="120" to-port="0"/>
		<edge from-layer="118" from-port="0" to-layer="119" to-port="0"/>
		<edge from-layer="119" from-port="1" to-layer="120" to-port="1"/>
		<edge from-layer="120" from-port="2" to-layer="121" to-port="0"/>
		<edge from-layer="121" from-port="1" to-layer="124" to-port="0"/>
		<edge from-layer="122" from-port="0" to-layer="123" to-port="0"/>
		<edge from-layer="123" from-port="1" to-layer="124" to-port="1"/>
		<edge from-layer="124" from-port="2" to-layer="127" to-port="0"/>
		<edge from-layer="125" from-port="0" to-layer="126" to-port="0"/>
		<edge from-layer="126" from-port="1" to-layer="127" to-port="1"/>
		<edge from-layer="127" from-port="2" to-layer="130" to-port="0"/>
		<edge from-layer="128" from-port="0" to-layer="129" to-port="0"/>
		<edge from-layer="129" from-port="1" to-layer="130" to-port="1"/>
		<edge from-layer="130" from-port="2" to-layer="133" to-port="0"/>
		<edge from-layer="131" from-port="0" to-layer="132" to-port="0"/>
		<edge from-layer="132" from-port="1" to-layer="133" to-port="1"/>
		<edge from-layer="133" from-port="2" to-layer="134" to-port="0"/>
		<edge from-layer="134" from-port="1" to-layer="137" to-port="0"/>
		<edge from-layer="135" from-port="0" to-layer="136" to-port="0"/>
		<edge from-layer="136" from-port="1" to-layer="137" to-port="1"/>
		<edge from-layer="137" from-port="2" to-layer="140" to-port="0"/>
		<edge from-layer="138" from-port="0" to-layer="139" to-port="0"/>
		<edge from-layer="139" from-port="1" to-layer="140" to-port="1"/>
		<edge from-layer="140" from-port="2" to-layer="143" to-port="0"/>
		<edge from-layer="141" from-port="0" to-layer="142" to-port="0"/>
		<edge from-layer="142" from-port="1" to-layer="143" to-port="1"/>
		<edge from-layer="143" from-port="2" to-layer="146" to-port="0"/>
		<edge from-layer="144" from-port="0" to-layer="145" to-port="0"/>
		<edge from-layer="145" from-port="1" to-layer="146" to-port="1"/>
		<edge from-layer="146" from-port="2" to-layer="147" to-port="0"/>
		<edge from-layer="147" from-port="1" to-layer="148" to-port="0"/>
	</edges>
	<meta_data>
		<MO_version value="2022.1.0-7009-19bdd019745-refs/pull/1012/head"/>
		<Runtime_version value="2022.1.0-7009-19bdd019745-refs/pull/1012/head"/>
		<legacy_path value="True"/>
		<cli_parameters>
			<caffe_parser_path value="DIR"/>
			<compress_fp16 value="True"/>
			<data_type value="FP16"/>
			<disable_nhwc_to_nchw value="False"/>
			<disable_omitting_optional value="False"/>
			<disable_resnet_optimization value="False"/>
			<disable_weights_compression value="False"/>
			<enable_concat_optimization value="False"/>
			<enable_flattening_nested_params value="False"/>
			<enable_ssd_gluoncv value="False"/>
			<extensions value="DIR"/>
			<framework value="tf"/>
			<freeze_placeholder_with_value value="{}"/>
			<input_model value="DIR/output_graph_Emotion.pb"/>
			<input_model_is_text value="False"/>
			<input_shape value="[1,48,48,1]"/>
			<k value="DIR/CustomLayersMapping.xml"/>
			<layout value="()"/>
			<layout_values value="{}"/>
			<legacy_mxnet_model value="False"/>
			<log_level value="ERROR"/>
			<mean_scale_values value="{}"/>
			<mean_values value="()"/>
			<model_name value="output_graph_Emotion"/>
			<output_dir value="DIR"/>
			<packed_user_shapes value="defaultdict(&lt;class 'list'&gt;, {'conv2d_1_input': [{'shape': (1, 48, 48, 1), 'port': None, 'added': True}]})"/>
			<placeholder_data_types value="{}"/>
			<placeholder_shapes value="(1, 48, 48, 1)"/>
			<progress value="False"/>
			<remove_memory value="False"/>
			<remove_output_softmax value="False"/>
			<reverse_input_channels value="False"/>
			<save_params_from_nd value="False"/>
			<scale_values value="()"/>
			<silent value="False"/>
			<source_layout value="()"/>
			<static_shape value="False"/>
			<stream_output value="False"/>
			<target_layout value="()"/>
			<transform value=""/>
			<use_legacy_frontend value="False"/>
			<use_new_frontend value="False"/>
			<unset unset_cli_parameters="batch, counts, disable_fusing, finegrain_fusing, input, input_checkpoint, input_meta_graph, input_proto, input_symbol, mean_file, mean_file_offsets, nd_prefix_name, output, pretrained_model_name, saved_model_dir, saved_model_tags, scale, tensorboard_logdir, tensorflow_custom_layer_libraries, tensorflow_custom_operations_config_update, tensorflow_object_detection_api_pipeline_config, tensorflow_use_custom_operations_config, transformations_config"/>
		</cli_parameters>
	</meta_data>
</net>