tensorflow - Converting .tflite to .pb

Question

Problem: How can i convert a .tflite (serialised flat buffer) to .pb (frozen model)? The documentation only talks about one way conversion.

Use-case is: I have a model that is trained on converted to .tflite but unfortunately, i do not have details of the model and i would like to inspect the graph, how can i do that?

Answer 1

I found the answer here

We can use Interpreter to analysis the model and the same code looks like following:

import numpy as np
import tensorflow as tf

# Load TFLite model and allocate tensors.
interpreter = tf.lite.Interpreter(model_path="converted_model.tflite")
interpreter.allocate_tensors()

# Get input and output tensors.
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()

# Test model on random input data.
input_shape = input_details[0]['shape']
input_data = np.array(np.random.random_sample(input_shape), dtype=np.float32)
interpreter.set_tensor(input_details[0]['index'], input_data)

interpreter.invoke()
output_data = interpreter.get_tensor(output_details[0]['index'])
print(output_data)

Netron is the best analysis/visualising tool i found, it can understand lot of formats including .tflite.