python之Tensorflow 相当于 pytorch 的 conv1d 是什么

只是想知道如何在 tensorflow 中执行一维卷积。具体来说，希望将此代码替换为 tensorflow:

inputs = F.pad(inputs, (kernel_size-1,0), 'constant', 0) 
 
output = F.conv1d(inputs, weight, padding=0, groups=num_heads) 

请您参考如下方法:

PyTorch 的 Tensorflow 等价物

torch.nn.functional.conv1d() 是 tf.nn.conv1d() 和 torch.nn.functional.pad() 是 tf.pad()。

例如:

(PyTorch 代码)

import torch.nn as nn 
import torch 
 
inputs = torch.tensor([1, 0, 2, 3, 0, 1, 1], dtype=torch.float32) 
filters = torch.tensor([2, 1, 3], dtype=torch.float32)  
 
inputs = inputs.unsqueeze(0).unsqueeze(0)                   # torch.Size([1, 1, 7]) 
filters = filters.unsqueeze(0).unsqueeze(0)                 # torch.Size([1, 1, 3]) 
conv_res = F.conv1d(inputs, filters, padding=0, groups=1)   # torch.Size([1, 1, 5]) 
pad_res = F.pad(conv_res, (1, 1), mode='constant', value=0) # torch.Size([1, 1, 7]) 

输出:

tensor([[[ 0.,  8., 11.,  7.,  9.,  4.,  0.]]]) 

( tensorflow 代码)

import tensorflow as tf 
tf.enable_eager_execution() 
 
i = tf.constant([1, 0, 2, 3, 0, 1, 1], dtype=tf.float32) 
k = tf.constant([2, 1, 3], dtype=tf.float32, name='k') 
 
data = tf.reshape(i, [1, int(i.shape[0]), 1], name='data') 
kernel = tf.reshape(k, [int(k.shape[0]), 1, 1], name='kernel') 
 
res = tf.nn.conv1d(data, kernel, 1, 'VALID') 
res = tf.pad(res[0], [[1, 1], [0, 0]], "CONSTANT") 

输出:

<tf.Tensor: id=555, shape=(7, 1), dtype=float32, numpy= 
array([[ 0.], 
       [ 8.], 
       [11.], 
       [ 7.], 
       [ 9.], 
       [ 4.], 
       [ 0.]], dtype=float32)>