Remember to give element values, not arrays, or two-dimensional arrays: a5.splice(1,0,) If you don't delete and want to add an element, we can set the second parameter to 0, followed by the element to be added.For example, let's add three new elements 1.5, 2.5, 3.5 to a5 above after 100: a5.splice(1,0,1.5,2.5,3.5) Our husband makes an array of 10 elements and deletes the first five empty elements: let a5 = The first parameter of the splice method is the starting position, and the second parameter is the number to delete. In the meantime, let's review that, as opposed to push, the pop method deletes the last element.The shift method is the opposite of unshift.įor example, let's pop the a3 above: let a4 = a3 įinally, we have a powerful splice method that you can add and delete anywhere. If you want to add new elements from scratch, you can use the unshift method: let a3 = new Array() Push can have several elements: let a3 = new Array() If you are too lazy to count a total of several elements, and want to add a new element at the end of the array, you can use the push method with an unlimited number of parameters. Not only is an empty array free to add elements, but once we have generated an array of lengths with new Array, we can still say nothing and assign values at will.For example, let's say we have Array of five new elements, which assigns a value to the ninth: let a2 = new Array(5) Tf.js will shake out two NaN s for us: Tensor We can use such arrays to generate tensors without any pressure: let a1_t = tf.tensor1d(a1) To avoid being skewed by the TensorFlow.js API, which is later full of static language features, let's first review the array operations for js.įirst of all, let's not forget that js is a dynamic language, js arrays are dynamic arrays, there is no such statement that fixed-length arrays are out of bounds.įor example, if we want to assign a value to the second element of an empty array, there is no problem: let a1 = Now that we use TensorFlow.js to write machine learning code instead of Python versions of TensorFlow and PyTorch, we want to make the code taste JS itself. TensorFlow.js Machine Learning Tutorial (2) - Tensor manipulation of JS taste
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