Calculate the output of the network given the following neural network:
Weights between input and hidden layer are as follows: w11 = 1.2 w12 = 1.5 w21 = 1.5 w22 = 2.0 w31 = 2.0 w32 = 1.0
Weights between input and hidden layer are as follows: w11 = 1.5 w21 = 2.1
Inputs are: x1 = 0.7 x2 = 0.9 x3 = 0.1
Calculate the output of the network given the following neural network: Weights between input and hidden...
Calculate the output of the network given the following neural network: Weights between input and hidden layer are as follows: w11 = 1.2 w12 = 1.5 w21 = 1.5 w22 = 2.0 w31 = 2.0 w32 = 1.0 Weights between input and hidden layer are as follows: w11 = 1.5 w21 = 2.1 Inputs are: x1 = 0.7 x2 = 0.9 x3 = 0.1
Draw a fully connected neural network with 1 hidden layer where the number of units input, hidden layer, and output layer are 3, 2, 1, respectively. . (5+5+5+5) a. Show all the weight matrices and their dimensions for this neural network. b. Label the network connections using the weight values (e.g., w12, w23). c. Total how many weights do you need to train in this neural network? . Explain supervised and unsupervised learning in your own words. (10) Draw a...
1). The weight of w12 is damaged. Before this power failure the output of the network is 0.92129 when input x was applied. Compute the value of w12 weight supposing that the activation function is the logsig (Ans: w12 = 7.5) 2). A power failure damaged weights w11 and w12. Before the damage the output network was 0.539915 when the first column of x was applied and 0.327393 for the second column of x. Compute the values of w11 and...
A) Describe in details how Neural Network work. Make sure to show how to calculate the value of nodes in output layer in the feedforward step, and how to update weights between output layer and hidden layer, and weights between input and hidden layer. Show all the involved formula in the steps. What are the advantages and disadvantages of Neural Network?
1. Consider a neural network, which contains one hidden layer and an output layer with one output unit. Let the hidden units have negative sigmoid as the activation function, which is formulated as 1 n(v) 1 + exp(-1) and the output unit has a linear activation function in which the output is equal to the activation input). (a) Show that the derivative of the negative sigmoid obeys the following relation dn(v) dv = n(v)(1 + n(v)) (b) Let the cost...
4.7. Consider a two-layer feedforward ANN with two inputs a and b, one hidden unit c, and one output unit d. This network has five weights (wca, Wcb, Wco, Wse, Wao). where wro represents the threshold weight for unit x. Initialize these weights to the values (.1,.1,.1,.1,.1), then give their values after each of the first two training iterations of the BACKPROPAGATION algorithm. Assume learning rte '-.3, momentum α-: 0.9, incremental weight updates, and the following training examples: 0 1...