Please help each problem step by step with a find code for understanding with explanation. thank you Problem#3:Suppose 'x1' a tensor of shape (2,4).x1 = tf.constant ( [[1,2, 3, 4], [5, 6, 7, 8]])Create a new tensor with shape of (4,2,4) using 'tf.stack' function.Problem#4:Suppose 'x1' a tensor of shape (2,4).x1 = tf.constant ([[1,2, 3, 4], [5, 6, 7, 8]])Create a new tensor with shape of (1,2,4).Problem#5:Suppose 'x1' a tensor of shape (3,4).x1 = tf.constant ([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])Re-shape this tensor into a new tensor of shape (6,2). Print the values of the new tensor. Problem#7:Given the matrices:4-2 169-4A= 6 8 -5 B = 7 537 9 10-8 2 1Write TensorFlow code to verify the following properties:a. Associative propertyA(B+C) = AB + AC[[-31 -13 -7][173 147 -25][117 57 112]], shape=(3, 3), dtype=int32)-4C = 103Both sides of the above equation will evaluate to the following matrix.Ctf.Tensor(b. Distributive property(AB)C = A(BC)-5 276 1-98Both sides of the above equation will evaluate to the following matrix.Ctf.Tensor([[ 209 347 -136][297 -111 308][1207 562 250]], shape=(3, 3), dtype=int32)Please notice that the matrix multiplication operation defined in Problem#7 is 'Matrix Product'(not element wise matrix multiplication). The matrix multiplication function in TensorFlow libraryis 'matmul'.Element wise matrix multiplicationIn mathematics, the Hadamard product is a binary operation that takes two matrices of thesame dimensions and produces another matrix of the same dimension as the operands whereeach element i, j is the product of elements i, j of the original two matrices. It should not beconfused with the more common matrix product.Matrix ProductIn mathematics, matrix multiplication is a binary operation that produces a matrix from twomatrices. For matrix multiplication, the number of columns in the first matrix must be equal tothe number of rows in the second matrix. The result matrix, known as the matrix product, hasthe number of rows of the first and the number of columns of the second matrix.

In this set of problems, we are working with tensors using TensorFlow, a popular machine learning…

Problem#3: Suppose 'x1' a tensor of shape (2,4). x1 = tf.constant ([[1,2,3,4], [5,6,7,8]]) Create a new tensor with shape of (4,2,4) using 'tf.stack' function. Problem#4: Suppose 'x1' a tensor of shape (2,4). x1 = tf.constant ([[1,2,3,4], [5,6,7,8]]) Create a new tensor with shape of (1,2,4). Problem#5: Suppose 'x1' a tensor of shape (3,4). x1 = tf.constant ([[1,2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]) Re-shape this tensor into a new tensor of shape (6,2). Print the values of the new tensor.

Problem#3: Suppose 'x1' a tensor of shape (2,4). x1 = tf.constant ([[1,2,3,4], [5,6,7,8]]) Create a new tensor with shape of (4,2,4) using 'tf.stack' function. Problem#4: Suppose 'x1' a tensor of shape (2,4). x1 = tf.constant ([[1,2,3,4], [5,6,7,8]]) Create a new tensor with shape of (1,2,4). Problem#5: Suppose 'x1' a tensor of shape (3,4). x1 = tf.constant ([[1,2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]) Re-shape this tensor into a new tensor of shape (6,2). Print the values of the new tensor.

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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