如何解决INFO: Created TensorFlow Lite XNNPACK delegate for CPU.
时间: 2024-02-15 08:04:51 浏览: 98
这是 TensorFlow 在运行过程中输出的一条信息,提示使用了 TensorFlow Lite XNNPACK delegate 来为 CPU 提供加速。如果你想关闭这个信息的输出,可以在代码中添加以下语句:
```python
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
```
这样就可以将 TensorFlow 的日志级别设置为只输出 ERROR 级别及以上的信息,从而不再输出上述信息。但请注意,关闭这个信息的输出并不会影响 TensorFlow 的运行效果。
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Created TensorFlow Lite XNNPACK delegate for CPU
As an AI language model, I can provide you with information on the TensorFlow Lite XNNPACK delegate for CPU.
The TensorFlow Lite XNNPACK delegate is a delegate that allows TensorFlow Lite models to run on CPU using the XNNPACK library. The XNNPACK library is a highly optimized library for implementing neural network operations on CPUs. This delegate provides faster and more efficient execution of TensorFlow Lite models on devices that support the XNNPACK library.
The XNNPACK delegate is available in TensorFlow Lite for ARM CPUs, and it supports various types of operators such as convolution, depthwise convolution, fully connected, and pooling. The delegate also supports quantized and float models.
To use the XNNPACK delegate in your TensorFlow Lite model, you need to build TensorFlow Lite with the XNNPACK library and enable the delegate during runtime. You can do this using the following code:
```python
import tensorflow as tf
interpreter = tf.lite.Interpreter(model_path="model.tflite")
interpreter.allocate_tensors()
# Enable XNNPACK delegate
interpreter.set_tensor(
interpreter.get_input_details()[0]["index"],
np.zeros(interpreter.get_input_details()[0]["shape"], dtype=np.float32))
interpreter.invoke()
interpreter.set_delegate(tf.lite.experimental.load_delegate('libedgetpu.so.1'))
```
With the XNNPACK delegate, you can expect faster inference times and better performance on CPU devices.
INFO: Created TensorFlow Lite XNNPACK delegate for CPU.
这个信息表明你的TensorFlow Lite模型已经被成功地加载到你的设备中,并且已经使用了XNNPACK代理来优化模型在CPU上的执行。
XNNPACK是一个用于高效计算的TensorFlow Lite代理,它可以在CPU上加速模型的执行。当你的设备支持XNNPACK时,TensorFlow Lite会自动使用它来优化模型的执行。
总之,这个信息表明你的TensorFlow Lite模型已经成功地优化并准备好在CPU上执行了。