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.basicLSTMCell() function computes the next state and output of a BasicLSTMCell.
Syntax:
tf.basicLSTMCell (forgetBias, lstmKernel, lstmBias, data, c, h)
Parameters:
- forgetBias: The forget bias for the cell.
- lstmKernel: The weights for the cell.
- lstmBias: The bias for the cell.
- data: The input to the cell.
- c: Array of previous cell states.
- h: Array of previous cell outputs.
Returns: [tf.Tensor2D, tf.Tensor2D]
Example 1:
Javascript
import * as tf from "@tensorflow/tfjs";   const data = tf.tensor2d([7, 51, 50, 54, 24, 1, 48, 75], [4, 2]); const kernel = tf.tensor2d([49, 62, 47, 93, 12, 80,     24, 89, 34, 8, 96, 74, 56, 42, 32, 53, 7, 87, 35, 54], [5, 4]); const state = tf.tensor2d([97, 56, 32, 29, 57, 6, 8, 75, 26, 20, 1, 17], [4, 3]); const output = tf.tensor2d([27, 77, 90, 72, 9, 8, 94, 41, 89, 51, 18, 60], [4, 3]); const basicLSTMCell = tf.basicLSTMCell(0.8, kernel, 2.2, data, state, output);   console.log(basicLSTMCella) |
Output:
[
Tensor {
kept: false,
isDisposedInternal: false,
shape: [ 4, 3 ],
dtype: 'float32',
size: 12,
strides: [ 3 ],
dataId: { id: 19 },
id: 19,
rankType: '2',
scopeId: 0
},
Tensor {
kept: false,
isDisposedInternal: false,
shape: [ 4, 3 ],
dtype: 'float32',
size: 12,
strides: [ 3 ],
dataId: { id: 22 },
id: 22,
rankType: '2',
scopeId: 0
}
]
Example 2:Â
Javascript
import * as tf from "@tensorflow/tfjs";   const data = tf.tensor2d([70, 10, 62,     55, 74, 85, 66, 9], [4, 2]);   const kernel = tf.tensor2d([10, 82, 93, 83,     49, 73, 45, 77, 56, 29, 32, 2, 24,     39, 34, 91, 95, 61, 76, 69], [5, 4]);   const state = tf.tensor2d([29, 40, 79, 61,     5, 34, 78, 47, 86, 74, 46, 28], [4, 3]);   const output = tf.tensor2d([25, 55, 33, 85,     82, 65, 20, 75, 54, 59, 50, 3], [4, 3]);   const basicLSTMCell = tf.basicLSTMCell(1.0,     kernel, 2.0, data, state, output);   const input = tf.input({ shape: [4, 2] }); const simpleRNNLayer = tf.layers.simpleRNN({     units: 4,     returnSequences: true,     returnState: true,     cell: basicLSTMCell });   let outputs, finalState;   [outputs, finalState] = simpleRNNLayer.apply(input);   const model = tf.model({     inputs: input,     outputs: outputs });   const x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [1, 4, 2]);   model.predict(x).print(); |
Output:
Tensor
[[[0.8135326, -0.8665518, 0.946215 , 0.8714994],
[0.9547493, -0.9747651, 0.9873405, 0.9995403],
[0.9983249, -0.9986398, 0.9996439, 0.9999973],
[0.9999447, -0.9999344, 0.9999925, 1 ]]]
Reference: https://js.tensorflow.org/api/latest/#basicLSTMCell
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!
… [Trackback]
[…] Read More Information here on that Topic: geeksforgeeks.org/tensorflow-js-tf-basiclstmcell-function/ […]