Tensorflow.js is an open-source library that is developed by Google for running machine learning models as well as deep learning neural networks in the browser or node environment.
The .enableDebugMode() function is used to enable the debug mode that would register data regarding every single executed kernels i.e. the runout time of the kernel implementation, including the rank, size, as well as shape of the resultant tensor.
Note:
- The Debug mode would substantially reduce the speed of our software because it would download the end result of every single action within the CPU which must not be utilized in the production.
- The Debug mode would not impact the timing data of the kernel performance as the download time here is not assessed in the kernel performance time.
Syntax:
tf.enableDebugMode()
Parameters: This method does not hold any parameter.
Return Value: It returns void.
Example 1:
Javascript
// Importing the tensorflow.js library import * as tf from "@tensorflow/tfjs" // Calling enableDebugMode() method await tf.enableDebugMode(); // Setting prod mode of the // environment tf.env().set('PROD', false); // Printing output console.log(tf.env().flags); |
Output:
{
"IS_BROWSER": true,
"IS_NODE": false,
"DEBUG": true,
"CPU_HANDOFF_SIZE_THRESHOLD": 128,
"PROD": false
}
Example 2:
Javascript
// Importing the tensorflow.js library import * as tf from "@tensorflow/tfjs" // Calling enableDebugMode() method await tf.enableDebugMode(); // Setting debug mode of the environment tf.env().set("DEBUG", !0) // Setting textures of the environment tf.env().set('WEBGL_FORCE_F16_TEXTURES', true); // Calling ready() method await tf.ready(); // Printing output console.log(tf.env().features); |
Output:
{
"IS_BROWSER": true,
"IS_NODE": false,
"DEBUG": true,
"CPU_HANDOFF_SIZE_THRESHOLD": 128,
"PROD": true,
"WEBGL_FORCE_F16_TEXTURES": true,
"WEBGL_VERSION": 2,
"HAS_WEBGL": true
}
Reference: https://js.tensorflow.org/api/latest/#enableDebugMode
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