How can I change the following code from pytorch to tensorflow?
I want to change the follow pytorch network (v1.2) to tensorflow. I am confusing between tf.nn.conv2d and tf.keras.layers.Conv2D what should I choose?

import torch.nn as nn  
nn.Sequential(nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, bias=True), 
    nn.BatchNorm2d(out_planes), 
    nn.ReLU(inplace=True))


tf.nn.conv2d is functional api and tf.keras.layers.Conv2D is layer-class api. You should use the latter one. It's quite as similar as the relationship between torch.nn.functional.conv2d and torch.nn.Conv2D. 

import tensorflow as tf
from tensorflow.keras import Sequential
from tensorflow.keras.layers import Conv2D, ReLU, BatchNormalization

model = Sequential()
model.add(Conv2D(filters=10, kernel_size=3, strides=1))
model.add(BatchNormalization())
model.add(ReLU())

