Function Differences with tf.nn.avg_pool2d
tf.nn.avg_pool2d
tf.nn.avg_pool2d(
input,
ksize,
strides,
padding,
data_format='NHWC',
name=None
) -> Tensor
For more information, see tf.nn.avg_pool2d.
mindspore.nn.AvgPool2d
mindspore.nn.AvgPool2d(
kernel_size=1,
stride=1,
pad_mode='valid',
data_format='NCHW'
)(x) -> Tensor
For more information, see mindspore.nn.AvgPool2d.
Differences
TensorFlow: Performs average pooling on the input Tensor.
MindSpore: MindSpore API implements the same function as TensorFlow, and only the parameter names and the way of using input Tensor are different.
Categories |
Subcategories |
TensorFlow |
MindSpore |
Differences |
---|---|---|---|---|
Parameters |
Parameter 1 |
input |
x |
Same function, used to input a 4-dimensional Tensor. The data input format is different |
Parameter 2 |
ksize |
kernel_size |
Same function, different parameter names, no default values for TensorFlow |
|
Parameter 3 |
strides |
stride |
Same function, different parameter names, no default values for TensorFlow |
|
Parameter 4 |
padding |
pad_mode |
Same function, different parameter names, no default values for TensorFlow |
|
Parameter 5 |
data_format |
data_format |
Same function, different default values of parameters |
|
Parameter 6 |
name |
- |
Not involved |
Code Example
The two APIs achieve the same function and have the same usage.
# TensorFlow
import tensorflow as tf
import numpy as np
y = tf.constant([[[[1, 0, 1], [0, 1, 1]]]], dtype=tf.float32)
out = tf.nn.avg_pool2d(input=y, ksize=1, strides=1, padding='SAME')
print(out.numpy())
# [[[[1. 0. 1.]
# [0. 1. 1.]]]]
# MindSpore
import mindspore
import mindspore.nn as nn
from mindspore import Tensor
pool = nn.AvgPool2d(kernel_size=1, stride=1, pad_mode='SAME')
x = Tensor([[[[1, 0, 1], [0, 1, 1]]]], dtype=mindspore.float32)
output = pool(x)
print(output)
# [[[[1. 0. 1.]
# [0. 1. 1.]]]]