Tensorflow 带\u未使用\u节点的正确参数

Tensorflow 带\u未使用\u节点的正确参数,tensorflow,machine-learning,keras,Tensorflow,Machine Learning,Keras,Tensorflow图形转换页面显示如何使用剥离未使用的节点。 但是,对于我的模型,如何知道在剥离未使用的节点(type=X,shape=“y0,y1,y3,3”)中X和Y的正确值 my MobileNet V2机型上的汇总图的输出: Found 1 possible inputs: (name=image_tensor, type=uint8(4), shape=[?,?,?,3]) No variables spotted. Found 4 possible outputs: (name=

Tensorflow图形转换页面显示如何使用
剥离未使用的节点
。
但是,对于我的模型,如何知道在
剥离未使用的节点(type=X,shape=“y0,y1,y3,3”)
中X和Y的正确值

my MobileNet V2机型上的
汇总图的输出:

Found 1 possible inputs: (name=image_tensor, type=uint8(4), shape=[?,?,?,3]) 
No variables spotted.
Found 4 possible outputs: (name=detection_boxes, op=Identity) (name=detection_scores, op=Identity) (name=detection_classes, op=Identity) (name=num_detections, op=Identity) 
Found 3457096 (3.46M) const parameters, 0 (0) variable parameters, and 623 control_edges
Op types used: 1707 Const, 525 Identity, 277 Mul, 194 Add, 170 Reshape, 147 GatherV2, 133 Sub, 117 Minimum, 98 Slice, 92 Maximum, 77 ConcatV2, 77 Cast, 64 Rsqrt, 60 StridedSlice, 59 Relu6, 55 Conv2D, 54 Pack, 52 Greater, 49 Shape, 46 Split, 46 Where, 45 ExpandDims, 40 Fill, 37 Tile, 33 RealDiv, 33 DepthwiseConv2dNative, 30 Range, 29 Switch, 27 Unpack, 26 Enter, 25 Squeeze, 25 ZerosLike, 23 NonMaxSuppressionV2, 14 Merge, 12 BiasAdd, 12 FusedBatchNorm, 11 TensorArrayV3, 8 NextIteration, 6 TensorArrayWriteV3, 6 TensorArraySizeV3, 6 Sqrt, 6 Exit, 6 TensorArrayGatherV3, 5 TensorArrayScatterV3, 5 TensorArrayReadV3, 3 Rank, 3 Equal, 3 Transpose, 3 Assert, 2 Exp, 2 Less, 2 LoopCond, 1 All, 1 TopKV2, 1 Size, 1 Sigmoid, 1 ResizeBilinear, 1 Placeholder
To use with tensorflow/tools/benchmark:benchmark_model try these arguments:
bazel run tensorflow/tools/benchmark:benchmark_model -- --graph=/home/ubuntu/model-optimization/frozen_inference_graph.pb --show_flops --input_layer=image_tensor --input_layer_type=uint8 --input_layer_shape=-1,-1,-1,3 --output_layer=detection_boxes,detection_scores,detection_classes,num_detections

我认为您应该复制输入层DIM,您可以在模型的.ascii文件中找到