I use custom model for classification in Tensor flow Camera Demo.

I generated a .pb file (serialized protobuf file) and I could display the huge graph it contains.

To convert this graph to a optimized graph, as given in [https://www.oreilly.com/learning/tensorflow-on-android], the following procedure could be used:

```
$ bazel-bin/tensorflow/python/tools/optimize_for_inference \
--input=tf_files/retrained_graph.pb \
--output=tensorflow/examples/android/assets/retrained_graph.pb
--input_names=Mul \
--output_names=final_result
```

Here how to find the input_names and output_names from the graph display.

When I dont use proper names, I get device crash:

```
E/TensorFlowInferenceInterface(16821): Failed to run TensorFlow inference
with inputs:[AvgPool], outputs:[predictions]
E/AndroidRuntime(16821): FATAL EXCEPTION: inference
E/AndroidRuntime(16821): java.lang.IllegalArgumentException: Incompatible
shapes: [1,224,224,3] vs. [32,1,1,2048]
E/AndroidRuntime(16821): [[Node: dropout/dropout/mul = Mul[T=DT_FLOAT,
_device="/job:localhost/replica:0/task:0/cpu:0"](dropout/dropout/div,
dropout/dropout/Floor)]]
```

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)
```