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Suppose, we have the following JSON object that may contain nesting upto any level −const obj = { "one": 1, "two": { "three": 3 }, "four": { "five": 5, "six": { "seven": 7 }, "eight": 8 }, "nine": 9 };We are required to write a JavaScript function that takes in one such nested JSON object and returns a new object that contains no nesting and maps the corresponding values to the keys using the dot ... Read More
Suppose, we have an array of numbers like this −const arr = [1, 6, 3, 1, 3, 1, 6, 3];We are required to write a JavaScript function that takes in one such array as the first and the only argument. Then the function should look for all such numbers in the array that appear for an odd number of times (excluding only once).For example, In the above array, the numbers 1 and 3 both appear for 3 times (odd), so our function should remove the third occurrence of both these numbers.And the output array should look like −const output = ... Read More
Suppose we have two array of numbers of the same length like this −const arr1 = [23, 67, 12, 87, 33, 56, 89, 34, 25]; const arr2 = [12, 60, 45, 54, 67, 84, 36, 73, 44];We are required to write a JavaScript function that takes in two such arrays as the first and the second argument. The function should then compare the corresponding values of both the arrays, and the function should return −-1, if the count of corresponding numbers greater in the first array than the second array are more than corresponding numbers greater in the second array1, ... Read More
Suppose we have an array of integers, (positive, negative and zero) like this −const arr = [23, -1, 0, 11, 18];We are required to write a JavaScript function that takes in one such array as the first and the only argument. The function should then find the fractional ratio for all three different groups, namely positive, negative and zero.For example −For the above array, its length is 5, the output for this array should be −const output = [.2, .2, .6];The output array will always contain 3 numbers, representing the fractional ratio of negative, zero and positive integers respectively. One ... Read More
Tensorflow can be used with the estimator to evaluate the model with the help of the ‘evaluate’ method that is present in ‘classifier’ module.Read More: What is TensorFlow and how Keras work with TensorFlow to create Neural Networks?We will use the Keras Sequential API, which is helpful in building a sequential model that is used to work with a plain stack of layers, where every layer has exactly one input tensor and one output tensor.A neural network that contains at least one layer is known as a convolutional layer. We can use the Convolutional Neural Network to build learning model. TensorFlow ... Read More
Tensorflow can be used with the estimator to compile the model with the help of the ‘train’ method.Read More: What is TensorFlow and how Keras work with TensorFlow to create Neural Networks?We will use the Keras Sequential API, which is helpful in building a sequential model that is used to work with a plain stack of layers, where every layer has exactly one input tensor and one output tensor.A neural network that contains at least one layer is known as a convolutional layer. We can use the Convolutional Neural Network to build learning model. TensorFlow Text contains collection of text related ... Read More
An estimator can be instantiated using Tensorflow by using the ‘DNNClassifier’ method that is present in ‘estimator’ class of Tensorflow library.Read More: What is TensorFlow and how Keras work with TensorFlow to create Neural Networks?We will use the Keras Sequential API, which is helpful in building a sequential model that is used to work with a plain stack of layers, where every layer has exactly one input tensor and one output tensor.A neural network that contains at least one layer is known as a convolutional layer. We can use the Convolutional Neural Network to build learning model. TensorFlow Text contains collection ... Read More
Tensorflow can be used to define feature columns for the estimator model by creating an empty list and accessing the ‘key’ values of the training dataset and iterating through it. During iteration, the feature names are appended to the empty list.Read More: What is TensorFlow and how Keras work with TensorFlow to create Neural Networks?We will use the Keras Sequential API, which is helpful in building a sequential model that is used to work with a plain stack of layers, where every layer has exactly one input tensor and one output tensor.A neural network that contains at least one layer ... Read More
An input function that would be used to train or evaluate the model can be created in Tensorflow by using the ‘from_tensor_slices’ method and creating a dictionary of the features of the iris dataset.Read More: What is TensorFlow and how Keras work with TensorFlow to create Neural Networks?We will use the Keras Sequential API, which is helpful in building a sequential model that is used to work with a plain stack of layers, where every layer has exactly one input tensor and one output tensor.A neural network that contains at least one layer is known as a convolutional layer. We ... Read More
A two-element tuple can be returned by processing iris flower dataset by creating a method that takes the features and labels, and returns them as Numpy arrays.Read More: What is TensorFlow and how Keras work with TensorFlow to create Neural Networks?We will use the Keras Sequential API, which is helpful in building a sequential model that is used to work with a plain stack of layers, where every layer has exactly one input tensor and one output tensor.A neural network that contains at least one layer is known as a convolutional layer. We can use the Convolutional Neural Network to ... Read More