Simple use of tflite

Environmental Science

tensorflow=2.4.1

Practical examples

hold Tensorflow The model of is transformed into tflite

import tensorflow as tf
def convert_to_tflite(model):
    converter = tf.lite.TFLiteConverter.from_keras_model(model)
    tfmodel = converter.convert()
    file = open('yourmodel.tflite', 'wb')
    file.write(tfmodel)
    file.close()

function Tflite Model

import tensorflow as tf
def run_reference_by_tflite(input):
    interpreter = tf.lite.Interpreter(model_path="yourmodel.tflite")
    interpreter.allocate_tensors()
    # Get input and output tensors.
    input_details = interpreter.get_input_details()
    output_details = interpreter.get_output_details()
    # input details
    print(input_details, len(input_details))

    # output details
    print(output_details)

    # input_details[0]['index'] = the index which accepts the input
    interpreter.set_tensor(input_details[0]['index'], input)


    # run the inference
    interpreter.invoke()

    # output_details[0]['index'] = the index which provides the input
    output_data = interpreter.get_tensor(output_details[0]['index'])

    print('interpreter: ', output_data)

Related errors and solutions

1. ValueError: Cannot set tensor: Got value of type NOTYPE but expected type FLOAT32 for input 0,

or

ValueError: Cannot set tensor: Got value of type INT32 but expected type FLOAT32 for input 0,

or

ValueError: Cannot set tensor: Got value of type UINT8 but expected type FLOAT32 for input 0, name: input_1 

resolvent ：

under_exp = np.array(under_exp, dtype=np.float32)

Reference material

python - How to convert keras(h5) file to a tflite file? - Stack Overflow