javascript - Tensorflow 和 Tensorflow.js 的区别？

Question

我原以为这两个程序会返回相同的结果:

main.js:

const tf = require('@tensorflow/tfjs');
require('@tensorflow/tfjs-node');

var x = tf.tensor([0.101,0.102,0.103,0.104,0.105,0.106,0.107,0.108,0.109,0.110,0.111,0.112,0.113,0.114,0.115,0.116,0.117,0.118,0.119,0.120,0.121,0.122,0.123,0.124,0.125,0.126,0.127,0.128,0.129,0.130,0.131,0.132,0.133,0.134,0.135,0.136,0.137,0.138,0.139,0.140,0.141,0.142,0.143,0.144,0.145,0.146,0.147,0.148,0.149,0.150,0.151,0.152,0.153,0.154,0.155,0.156,0.157,0.158,0.159,0.160,0.161,0.162,0.163,0.164,0.165,0.166,0.167,0.168,0.169,0.170,0.171,0.172,0.173,0.174,0.175,0.176,0.177,0.178,0.179,0.180,0.181,0.182,0.183,0.184,0.185,0.186,0.187,0.188,0.189,0.190,0.191,0.192,0.193,0.194,0.195,0.196,0.197,0.198,0.199,0.200,0.201,0.202,0.203,0.204,0.205,0.206,0.207,0.208,0.209,0.210,0.211,0.212,0.213,0.214,0.215,0.216,0.217,0.218,0.219,0.220,0.221,0.222,0.223,0.224,0.225,0.226,0.227,0.228,0.229,0.230,0.231,0.232,0.233,0.234,0.235,0.236,0.237,0.238,0.239,0.240,0.241,0.242,0.243,0.244,0.245,0.246,0.247,0.248,0.249,0.250,0.251,0.252,0.253,0.254,0.255,0.256,0.257,0.258,0.259,0.260,0.261,0.262,0.263,0.264,0.265,0.266,0.267,0.268,0.269,0.270,0.271,0.272,0.273,0.274,0.275,0.276,0.277,0.278,0.279,0.280,0.281,0.282,0.283,0.284,0.285,0.286,0.287,0.288,0.289,0.290,0.291,0.292,0.293,0.294,0.295,0.296,0.297,0.298,0.299,0.300,0.301,0.302,0.303,0.304,0.305,0.306,0.307,0.308,0.309,0.310,0.311,0.312,0.313,0.314,0.315,0.316,0.317,0.318,0.319,0.320,0.321,0.322,0.323,0.324,0.325,0.326,0.327,0.328,0.329,0.330,0.331,0.332,0.333,0.334,0.335,0.336,0.337,0.338,0.339,0.340,0.341,0.342,0.343,0.344,0.345,0.346,0.347,0.348,0.349,0.350,0.351,0.352,0.353,0.354,0.355,0.356,0.357,0.358,0.359,0.360,0.361,0.362,0.363,0.364,0.365,0.366,0.367,0.368,0.369,0.370,0.371,0.372,0.373,0.374,0.375,0.376,0.377,0.378,0.379,0.380,0.381,0.382,0.383,0.384,0.385,0.386,0.387,0.388,0.389,0.390,0.391,0.392,0.393,0.394,0.395,0.396,0.397,0.398,0.399,0.400]).reshape([1,10,10,3])
var filters = tf.tensor([0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,0.10,0.11,0.12,0.13,0.14,0.15,0.16,0.17,0.18,0.19,0.20,0.21,0.22,0.23,0.24,0.25,0.26,0.27,0.28,0.29,0.30,0.31,0.32,0.33,0.34,0.35,0.36,0.37,0.38,0.39,0.40,0.41,0.42,0.43,0.44,0.45,0.46,0.47,0.48,0.49,0.50,0.51,0.52,0.53,0.54,0.55,0.56,0.57,0.58,0.59,0.60,0.61,0.62,0.63,0.64,0.65,0.66,0.67,0.68,0.69,0.70,0.71,0.72,0.73,0.74,0.75,0.76,0.77,0.78,0.79,0.80,0.81]).reshape([3,3,3,3])
var bias = tf.tensor([0.1,0.2,0.3])

var out = tf.add(
  tf.conv2d(x, filters, [2, 2], 'same'),
  bias)

console.log(x.dataSync())

main.py:

import tensorflow as tf
import json

x = tf.constant([0.101,0.102,0.103,0.104,0.105,0.106,0.107,0.108,0.109,0.110,0.111,0.112,0.113,0.114,0.115,0.116,0.117,0.118,0.119,0.120,0.121,0.122,0.123,0.124,0.125,0.126,0.127,0.128,0.129,0.130,0.131,0.132,0.133,0.134,0.135,0.136,0.137,0.138,0.139,0.140,0.141,0.142,0.143,0.144,0.145,0.146,0.147,0.148,0.149,0.150,0.151,0.152,0.153,0.154,0.155,0.156,0.157,0.158,0.159,0.160,0.161,0.162,0.163,0.164,0.165,0.166,0.167,0.168,0.169,0.170,0.171,0.172,0.173,0.174,0.175,0.176,0.177,0.178,0.179,0.180,0.181,0.182,0.183,0.184,0.185,0.186,0.187,0.188,0.189,0.190,0.191,0.192,0.193,0.194,0.195,0.196,0.197,0.198,0.199,0.200,0.201,0.202,0.203,0.204,0.205,0.206,0.207,0.208,0.209,0.210,0.211,0.212,0.213,0.214,0.215,0.216,0.217,0.218,0.219,0.220,0.221,0.222,0.223,0.224,0.225,0.226,0.227,0.228,0.229,0.230,0.231,0.232,0.233,0.234,0.235,0.236,0.237,0.238,0.239,0.240,0.241,0.242,0.243,0.244,0.245,0.246,0.247,0.248,0.249,0.250,0.251,0.252,0.253,0.254,0.255,0.256,0.257,0.258,0.259,0.260,0.261,0.262,0.263,0.264,0.265,0.266,0.267,0.268,0.269,0.270,0.271,0.272,0.273,0.274,0.275,0.276,0.277,0.278,0.279,0.280,0.281,0.282,0.283,0.284,0.285,0.286,0.287,0.288,0.289,0.290,0.291,0.292,0.293,0.294,0.295,0.296,0.297,0.298,0.299,0.300,0.301,0.302,0.303,0.304,0.305,0.306,0.307,0.308,0.309,0.310,0.311,0.312,0.313,0.314,0.315,0.316,0.317,0.318,0.319,0.320,0.321,0.322,0.323,0.324,0.325,0.326,0.327,0.328,0.329,0.330,0.331,0.332,0.333,0.334,0.335,0.336,0.337,0.338,0.339,0.340,0.341,0.342,0.343,0.344,0.345,0.346,0.347,0.348,0.349,0.350,0.351,0.352,0.353,0.354,0.355,0.356,0.357,0.358,0.359,0.360,0.361,0.362,0.363,0.364,0.365,0.366,0.367,0.368,0.369,0.370,0.371,0.372,0.373,0.374,0.375,0.376,0.377,0.378,0.379,0.380,0.381,0.382,0.383,0.384,0.385,0.386,0.387,0.388,0.389,0.390,0.391,0.392,0.393,0.394,0.395,0.396,0.397,0.398,0.399,0.400])
x = tf.reshape(x, [1,10,10,3])

filters = tf.constant([0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,0.10,0.11,0.12,0.13,0.14,0.15,0.16,0.17,0.18,0.19,0.20,0.21,0.22,0.23,0.24,0.25,0.26,0.27,0.28,0.29,0.30,0.31,0.32,0.33,0.34,0.35,0.36,0.37,0.38,0.39,0.40,0.41,0.42,0.43,0.44,0.45,0.46,0.47,0.48,0.49,0.50,0.51,0.52,0.53,0.54,0.55,0.56,0.57,0.58,0.59,0.60,0.61,0.62,0.63,0.64,0.65,0.66,0.67,0.68,0.69,0.70,0.71,0.72,0.73,0.74,0.75,0.76,0.77,0.78,0.79,0.80,0.81])
filters = tf.reshape(filters, [3,3,3,3])

bias = tf.constant([0.1,0.2,0.3])

out = tf.math.add(
  tf.nn.conv2d(x, filters, [1, 2, 2, 1], 'SAME'),
  bias)

with tf.Session() as sess:
  print(json.dumps(sess.run(out).tolist()))

相反，它们返回非常不同的值:

• Javascript 版本返回的张量的前 5 个值是:0.10100000351667404、0.10199999809265137、0.10300000011920929、0.10400000214576721、0.104999996721744 54
• Python版本返回的tensor的前5个值分别是:1.8199000358581543、1.9838900566101074、2.1478798389434814、1.8911800384521484、2.058410167694092

我错过了什么吗？我不应该期望这两个程序产生相同的结果吗？

Answer 1

两个程序输出相同的结果。如果 js 输出不同，请确保您使用的是最新版本 0.13.3。

python 代码的输出 here

下面是js代码

var x = tf.tensor([0.101,0.102,0.103,0.104,0.105,0.106,0.107,0.108,0.109,0.110,0.111,0.112,0.113,0.114,0.115,0.116,0.117,0.118,0.119,0.120,0.121,0.122,0.123,0.124,0.125,0.126,0.127,0.128,0.129,0.130,0.131,0.132,0.133,0.134,0.135,0.136,0.137,0.138,0.139,0.140,0.141,0.142,0.143,0.144,0.145,0.146,0.147,0.148,0.149,0.150,0.151,0.152,0.153,0.154,0.155,0.156,0.157,0.158,0.159,0.160,0.161,0.162,0.163,0.164,0.165,0.166,0.167,0.168,0.169,0.170,0.171,0.172,0.173,0.174,0.175,0.176,0.177,0.178,0.179,0.180,0.181,0.182,0.183,0.184,0.185,0.186,0.187,0.188,0.189,0.190,0.191,0.192,0.193,0.194,0.195,0.196,0.197,0.198,0.199,0.200,0.201,0.202,0.203,0.204,0.205,0.206,0.207,0.208,0.209,0.210,0.211,0.212,0.213,0.214,0.215,0.216,0.217,0.218,0.219,0.220,0.221,0.222,0.223,0.224,0.225,0.226,0.227,0.228,0.229,0.230,0.231,0.232,0.233,0.234,0.235,0.236,0.237,0.238,0.239,0.240,0.241,0.242,0.243,0.244,0.245,0.246,0.247,0.248,0.249,0.250,0.251,0.252,0.253,0.254,0.255,0.256,0.257,0.258,0.259,0.260,0.261,0.262,0.263,0.264,0.265,0.266,0.267,0.268,0.269,0.270,0.271,0.272,0.273,0.274,0.275,0.276,0.277,0.278,0.279,0.280,0.281,0.282,0.283,0.284,0.285,0.286,0.287,0.288,0.289,0.290,0.291,0.292,0.293,0.294,0.295,0.296,0.297,0.298,0.299,0.300,0.301,0.302,0.303,0.304,0.305,0.306,0.307,0.308,0.309,0.310,0.311,0.312,0.313,0.314,0.315,0.316,0.317,0.318,0.319,0.320,0.321,0.322,0.323,0.324,0.325,0.326,0.327,0.328,0.329,0.330,0.331,0.332,0.333,0.334,0.335,0.336,0.337,0.338,0.339,0.340,0.341,0.342,0.343,0.344,0.345,0.346,0.347,0.348,0.349,0.350,0.351,0.352,0.353,0.354,0.355,0.356,0.357,0.358,0.359,0.360,0.361,0.362,0.363,0.364,0.365,0.366,0.367,0.368,0.369,0.370,0.371,0.372,0.373,0.374,0.375,0.376,0.377,0.378,0.379,0.380,0.381,0.382,0.383,0.384,0.385,0.386,0.387,0.388,0.389,0.390,0.391,0.392,0.393,0.394,0.395,0.396,0.397,0.398,0.399,0.400]).reshape([1,10,10,3])
var filters = tf.tensor([0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,0.10,0.11,0.12,0.13,0.14,0.15,0.16,0.17,0.18,0.19,0.20,0.21,0.22,0.23,0.24,0.25,0.26,0.27,0.28,0.29,0.30,0.31,0.32,0.33,0.34,0.35,0.36,0.37,0.38,0.39,0.40,0.41,0.42,0.43,0.44,0.45,0.46,0.47,0.48,0.49,0.50,0.51,0.52,0.53,0.54,0.55,0.56,0.57,0.58,0.59,0.60,0.61,0.62,0.63,0.64,0.65,0.66,0.67,0.68,0.69,0.70,0.71,0.72,0.73,0.74,0.75,0.76,0.77,0.78,0.79,0.80,0.81]).reshape([3,3,3,3])
var bias = tf.tensor([0.1,0.2,0.3])

var out = tf.add(
  tf.conv2d(x, filters, [2, 2], 'same'),
  bias)

out.print()

<html>
  <head>
    <!-- Load TensorFlow.js -->
    <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@0.13.3/dist/tf.min.js"> </script>
  </head>

  <body>
  </body>
</html>