Given a tensor flow model graph, how to find the input node and output node names

Try this:

run python

>>> import tensorflow as tf
>>> gf = tf.GraphDef()
>>> gf.ParseFromString(open('/your/path/to/graphname.pb','rb').read())

and then

>>> [n.name + '=>' +  n.op for n in gf.node if n.op in ( 'Softmax','Placeholder')]

Then, you can get result similar to this:

['Mul=>Placeholder', 'final_result=>Softmax']

But I'm not sure it's the problem of node names regarding the error messages. I guess you provided wrong arguements when loading the graph file or your generated graph file is something wrong?

Check this part:

E/AndroidRuntime(16821): java.lang.IllegalArgumentException: Incompatible 
shapes: [1,224,224,3] vs. [32,1,1,2048]

UPDATE: Sorry, if you're using (re)trained graph , then try this:

[n.name + '=>' +  n.op for n in gf.node if n.op in ( 'Softmax','Mul')]

It seems that (re)trained graph saves input/output op name as "Mul" and "Softmax", while optimized and/or quantized graph saves them as "Placeholder" and "Softmax".

BTW, using retrained graph in mobile environment is not recommended according to Peter Warden's post: https://petewarden.com/2016/09/27/tensorflow-for-mobile-poets/ . It's better to use quantized or memmapped graph due to performance and file size issue, I couldn't find out how to load memmapped graph in android though...:( (no problem loading optimized / quantized graph in android)

Recently I came across this option directly from tensorflow:

bazel build tensorflow/tools/graph_transforms:summarize_graph    
bazel-bin/tensorflow/tools/graph_transforms/summarize_graph
--in_graph=custom_graph_name.pb

I wrote a simple script to analyze the dependency relations in a computational graph (usually a DAG, directly acyclic graph). It's so obvious that the inputs are the nodes that lack a input. However, outputs can be defined as any nodes in a graph because, in the weirdest but still valid case, outputs can be inputs while the other nodes are all dummy. I still define the output operations as nodes without output in the code. You could neglect it at your willing.

import tensorflow as tf

def load_graph(frozen_graph_filename):
    with tf.io.gfile.GFile(frozen_graph_filename, "rb") as f:
        graph_def = tf.compat.v1.GraphDef()
        graph_def.ParseFromString(f.read())
    with tf.Graph().as_default() as graph:
        tf.import_graph_def(graph_def)
    return graph

def analyze_inputs_outputs(graph):
    ops = graph.get_operations()
    outputs_set = set(ops)
    inputs = []
    for op in ops:
        if len(op.inputs) == 0 and op.type != 'Const':
            inputs.append(op)
        else:
            for input_tensor in op.inputs:
                if input_tensor.op in outputs_set:
                    outputs_set.remove(input_tensor.op)
    outputs = list(outputs_set)
    return (inputs, outputs)