CODEWITHSUNDEEP
androidpythontensorflowtensorboardtensorflow-datasets
Amit Prajapati
Amit Prajapati

Reputation: 14315

ValueError: Invalid tensors 'input' were found

I'm not able to convert .pb to tflite

Here is the command that I'm executing to generate .pb I am successful in generating it.

IMAGE_SIZE=224
ARCHITECTURE="mobilenet_1_1.0_${IMAGE_SIZE}"

python retrain.py  
 --bottleneck_dir=tf_files/bottlenecks   
 --how_many_training_steps=500   
 --model_dir=tf_files/models/   
 --summaries_dir=tf_files/training_summaries/"${ARCHITECTURE}"  
  --output_graph=tf_files/retrained_graph.pb   
  --output_labels=tf_files/retrained_labels.txt   
  --architecture="${ARCHITECTURE}"  
  --image_dir=tf_files/flower_photos

Once I am trying to create that .pb to .tflite get fail with same error "ValueError: Invalid tensors 'input' were found."

tflite_convert \
  --output_file=foo.tflite \
  --graph_def_file=retrained_graph.pb \
  --input_arrays=input \
  --output_arrays=MobilenetV1/Predictions/Reshape_1

Upvotes: 2

Views: 4139

Answers (2)

Amit Prajapati
Amit Prajapati

Reputation: 14315

I just follow this google code demo.

https://codelabs.developers.google.com/codelabs/tensorflow-for-poets/#0

Working fine 

IMAGE_SIZE=224
ARCHITECTURE="mobilenet_1.0_${IMAGE_SIZE}"

python -m scripts.retrain \
--bottleneck_dir=tf_files/bottlenecks \
--how_many_training_steps=500 \
--model_dir=tf_files/models/ \
--summaries_dir=tf_files/training_summaries/"${ARCHITECTURE}" \
--output_graph=tf_files/retrained_graph.pb \
--output_labels=tf_files/retrained_labels.txt \
--architecture="${ARCHITECTURE}" \
--image_dir=tf_files/flower_photos

tflite_convert   --graph_def_file=tf_files/retrained_graph.pb   --output_file=tf_files/optimized_graph.tflite   --input_format=TENSORFLOW_GRAPHDEF   --output_format=TFLITE   --input_shape=1,224,224,3   --input_array=input   --output_array=final_result   --inference_type=FLOAT   --input_data_type=FLOAT

I made one change for it simpley change mobilenet version.

Upvotes: 2

Jonathan.H
Jonathan.H

Reputation: 193

I got the same error as you with tflite converter python api.

This caused by the params we passed in input_arrays.

input_arrays need tensor_name defined in tf.placeholder(name="input") not proto map key string defined in build_signature_def(inputs={"input": tensor_info_proto},outputs...).

Here is a simple example.

x = tf.placeholder(tf.float32, [None], name="input_x")
...

builder = tf.saved_model.builder.SavedModelBuilder(saved_model_path)
input_tensor_info = {"input": tf.saved_model.build_tensor_info(x)}
output_tensor_info = ...
signature_def = tf.saved_model.build_signature_def(inputs=input_tensor_info,
                                                   outputs=...,
                                                   method_name=...)
builder.add_meta_graph_and_variables(...)
builder.save()

# convert saved_model to tflite format.
converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_path,
                                                     input_arrays=["input"],
                                                     ...)
...
...

Once you run a code like this will raise an error "ValueError: Invalid tensors 'input' were found."

If we make a small change as bellow, it will succeed.

# a small change when convert
converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_path,
                                                     input_arrays=["input_x"],
                                                     ...)

Upvotes: 0

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