Introduction

TensorFlow Lite (TFLite) is an optimized framework designed to run TensorFlow models on edge devices like mobile, embedded systems, and other devices with computational constraints. Converting your existing TensorFlow model to TFLite format involves several steps, one of which is obtaining the input_array and output_array items. These arrays are crucial because they define the input and output data structures your model will use in its TFLite version.

Understanding input_array and output_array

input_array and output_array represent the input and output tensors of your model. They are integral to the graph transformation process during TensorFlow to TFLite conversion. Here's what they mean:

• input_array: It specifies the tensor(s) that will act as input(s) for your model. These tensors contain the data that will be fed into the model during inference.
• output_array: These are the tensor(s) representing the model's predictions. They hold the computed data when the model processes the input data.

Knowing the names and shapes of these tensors is critical in converting models and ensuring the model operates correctly after conversion.

Extracting input_array and output_array

Step 1: Understanding your Model's Architecture

Before extracting input_array and output_array , you should fully understand the model's architecture. Analyzing the computational graph can help you identify these arrays. Tools such as TensorBoard can be beneficial for visualizing the model structure.

Step 2: Accessing the Model

Load your TensorFlow model in its current form:

• Precision Loss: If using post-training quantization, be aware of potential precision losses.
• Custom Operations: Not all TensorFlow operations have direct equivalents in TFLite; custom operations might need additional processing.
• Batch Sizes: TFLite is designed for single batch processing. Adjust your input sizes accordingly.