How to invoke a trained TensorFlow model?

To call a pre-trained TensorFlow model, you need to follow these steps:

1. loading a saved model with tf.saved_model.load

import tensorflow as tf

loaded_model = tf.saved_model.load('path/to/saved/model')

1. The loaded model signatures

inference_fn = loaded_model.signatures['serving_default']

1. Prepare input data: Prepare input data according to the input requirements of the model. Typically, the input data is a Tensor or a dictionary containing Tensors. For example:

import numpy as np

input_data = np.array([[1, 2, 3, 4], [5, 6, 7, 8]], dtype=np.float32)

1. Perform reasoning: use the model’s inference function to reason. For example:

output = inference_fn(tf.constant(input_data))

1. the result converted to a numpy array
2. Convert the output with the name ‘output_name’ to a numpy array.

output_data = output['output_name'].numpy()

After completing the steps above, you have successfully called the pre-trained TensorFlow model and obtained the inference results. Please make adjustments according to your specific situation.