Tensorflow.js is a Google-developed open-source toolkit for executing machine learning models and deep learning neural networks in the browser or on the node platform. It also enables developers to create machine learning models in JavaScript and utilize them directly in the browser or with Node.js.
The tf.layers.globalMaxPooling2d() function is used for apply Global max pooling operation for spatial data.
Syntax:
tf.layers.globalMaxPooling2d()
Parameters:
- args: It is an object with the following properties:
- dataFormat: The data format to utilize for the pooling layer.
- inputShape: If it is specified then, it will be utilized to construct an input layer that will be inserted before this layer.
- batchInputShape: If it is specified it will be used for creating an input layer which will be inserted before this layer.
- batchSize: It supports the inputShape to build the batchInputShape.
- dtype: It is the kind of data type for this layer. This parameter applies exclusively to input layers.
- name: It is of string type. It is the name of this layer.
- trainable: If it is set to be true then only the weights of this layer will be changed by fit.
- weights: The layer’s initial weight values.
- InputDType: It is used for Legacy support.
Returns: It returns GlobalMaxPooling2D
Example 1:
Javascript
import * as tf from "@tensorflow/tfjs"; const Input = tf.input({ shape: [5, 2, 1] }); const maxPooling2DLayer = tf.layers.globalMaxPooling2d( { dataFormat: 'channelsFirst' } ); const Output = maxPooling2DLayer.apply(Input); const Data = tf.ones([2, 5, 2, 1]); const model = tf.model({ inputs: Input, outputs: Output }); model.predict(Data).print(); |
Output:
Tensor
[[1, 1, 1, 1, 1],
[1, 1, 1, 1, 1]]
Example 2:
Javascript
import * as tf from "@tensorflow/tfjs"; const Input = tf.input({ shape: [2, 3, 3] }); const maxPooling2dLayer = tf.layers.globalMaxPooling2d({ dataFormat: 'channelsLast' }); const Output = maxPooling2dLayer.apply(Input); const model = tf.model({ inputs: Input, outputs: Output }); const Data = tf.tensor4d([2, 3, 5, 1, 3, 5, 8, 2, 2, 6, 8, 9, 9, 4, 8, 9, 3, 8, 4, 2, 2, 9, 2, 4, 6, 4, 2, 6, 4, 2, 5, 8, 2, 8, 3, 2 ], [2, 2, 3, 3]); model.predict(Data).print(); |
Output:
Tensor
[[9, 8, 9],
[9, 8, 4]]
Reference: https://js.tensorflow.org/api/latest/#layers.globalMaxPooling2d
Whether you’re preparing for your first job interview or aiming to upskill in this ever-evolving tech landscape, neveropen Courses are your key to success. We provide top-quality content at affordable prices, all geared towards accelerating your growth in a time-bound manner. Join the millions we’ve already empowered, and we’re here to do the same for you. Don’t miss out – check it out now!
