如何在TensorFlow 2.5.0中集成自定义的动态时间规整器?

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英文:

How to integrate custom dynamic-time-warper with TensorFlow 2.5.0?

问题

我目前正在进行一个项目,我在其中使用Python实现了一个自定义的动态时间规整(DTW)算法。现在,我想将这个自定义的DTW算法集成到TensorFlow 2.5.0中,具体来说是在一个用于RNN模型的自定义层中。TensorFlow文档没有涵盖这种特定情况,我也没有找到任何讨论这个问题的资源或示例。有谁能提供关于如何做到这一点的指导?

这是我自定义DTW算法的Python代码:

import numpy as np

def custom_dtw(seq1, seq2):
    # 在这里实现DTW算法...
    pass

我希望在一个自定义的TensorFlow层中使用它,如下所示:

import tensorflow as tf

class CustomDTWLayer(tf.keras.layers.Layer):
    def __init__(self):
        super(CustomDTWLayer, self).__init__()

    def call(self, inputs):
        # 在这里使用custom_dtw...
        pass

我会感激任何关于如何做到这一点或指向正确方向的帮助。谢谢!

英文:

I'm currently working on a project where I have implemented a custom dynamic time warping (DTW) algorithm in Python. Now, I want to integrate this custom DTW with TensorFlow 2.5.0, specifically within a custom layer for an RNN model. The TensorFlow documentation doesn't cover this specific scenario and I have not been able to find any resources or examples that discuss this. Can anyone provide guidance on how to do this?

Here's the Python code for my custom DTW algorithm:

import numpy as np

def custom_dtw(seq1, seq2):
    # DTW algorithm implementation here...
    pass

I'm looking to use this within a custom TensorFlow layer like so:

import tensorflow as tf

class CustomDTWLayer(tf.keras.layers.Layer):
    def __init__(self):
        super(CustomDTWLayer, self).__init__()

    def call(self, inputs):
        # Use custom_dtw here...
        pass

I'd appreciate any help or pointers in the right direction. Thank you!

答案1

得分: 1

要将自定义动态时间规整算法与TensorFlow集成您需要将您的DTW函数封装在tf.py_function中这允许您在TensorFlow图中运行任意Python代码以下是如何做

```python
import tensorflow as tf

class CustomDTWLayer(tf.keras.layers.Layer):
    def __init__(self):
        super(CustomDTWLayer, self).__init__()

    def call(self, inputs):
        result = tf.py_function(custom_dtw, [inputs], tf.float32)
        return result

在这段代码中,custom_dtw是您的DTW函数,[inputs]是传递给函数的张量输入列表,tf.float32是函数的输出类型。

注意:因为tf.py_function在TensorFlow图之外运行,它无法受益于GPU加速,并且其梯度不会自动计算。

参考:

tf.py_function的TensorFlow文档:https://www.tensorflow.org/api_docs/python/tf/py_function

希望对您有所帮助!


<details>
<summary>英文:</summary>

To integrate a custom dynamic time warping algorithm with TensorFlow, you&#39;ll need to wrap your DTW function in a tf.py_function, which allows you to run arbitrary Python code within a TensorFlow graph. Here&#39;s how to do this:

    import tensorflow as tf
    
    class CustomDTWLayer(tf.keras.layers.Layer):
        def __init__(self):
            super(CustomDTWLayer, self).__init__()
    
        def call(self, inputs):
            result = tf.py_function(custom_dtw, [inputs], tf.float32)
            return result

In this code, custom_dtw is your DTW function, [inputs] is the list of tensor inputs to your function, and tf.float32 is the output type of your function.

Note: Because tf.py_function operates outside of the TensorFlow graph, it cannot benefit from GPU acceleration and its gradients are not automatically computed.

Reference:

TensorFlow documentation on tf.py_function: https://www.tensorflow.org/api_docs/python/tf/py_function

I hope this helps!

</details>



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  • 本文由 发表于 2023年7月27日 20:15:31
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