I Use TFLiteConvert Post_training_quantize=True But My Model Is Still Too Big For Being Hosted In Firebase ML Kit's Custom Servers

I have written a TensorFlow / Keras Super-Resolution GAN. I've converted the resulting trained .h5 model to a .tflite model, using the below code, executed in Google Colab: import

Solution 1:

In general, quantization means, shifting from dtype float32 to uint8. So theoretically our model should reduce by the size of 4. This will be clearly visible in files of greater size.

Check whether your model has been quantized or not by using the tool "https://lutzroeder.github.io/netron/". Here you have to load the model and check the random layers having weight.The quantized graph contains the weights value in uint8 format In unquantized graph the weights value will be in float32 format.

Only setting "converter.post_training_quantize=True" is not enough to quantize your model. The other settings include:
converter.inference_type=tf.uint8
converter.default_ranges_stats=[min_value,max_value]
converter.quantized_input_stats={"name_of_the_input_layer_for_your_model":[mean,std]}

Hoping you are dealing with images.
min_value=0, max_value=255, mean=128(subjective) and std=128(subjective).
name_of_the_input_layer_for_your_model= first name of the graph when you load your model in the above mentioned link or you can get the name of the input layer through the code "model.input" will give the output "tf.Tensor 'input_1:0' shape=(?, 224, 224, 3) dtype=float32". Here the input_1 is the name of the input layer(NOTE: model must include the graph configuration and the weight.)