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1. Consider a neural network, which contains one hidden layer and an output layer with one...
Exercise Optimization in neural network Consider a very simple neural network with two input values, one...
Exercise Optimization in neural network Consider a very simple neural network with two input values, one output value, and a single neuron with sigmoid activation. Each input to the neuron has an associated weight, and the neuron has a bias. So the network represents functions of the form o(W1X1 + W222 + b). We train the neural network using least squares loss on a single piece of training data ((1, -1),0). Initially all weights and biases are set to 1....
1. Compared with PID Control, what are the advantages and disadvantages of Neural Network Control...
1. Compared with PID Control, what are the advantages and disadvantages of Neural Network Control? 2. The multi-layer neural network shown in Figure I has two inputs and one output. The network has two neurons in a hidden layer. The network is to be trained with backpropagation algorithm. Each neuron has a sigmoid activation function: Assume that the biases to the neurons is +1 and the learning rate is 1. The network has the following initial weights: (w). w1 wa1...
5. (10 points) Optimization in neural network Consider a very simple neural network with two input...
5. (10 points) Optimization in neural network Consider a very simple neural network with two input values, one output value, and a single neuron with sigmoid activation. Each input to the neuron has an associated weight, and the neuron has a bias. So the network represents functions of the form o(W121 + W222 +b). We train the neural network using least squares loss on a single piece of training data ((1, -1),0). Initially all weights and biases are set to...
2. (20) Design an artificial neural network with two hidden layers. First hidden layer has s...
2. (20) Design an artificial neural network with two hidden layers. First hidden layer has s neurons, second hidden layer has 3 neurons. Input parameters are 3, output parameter i s (20) What is the fundemental philosophy in backpropagation training algorithm, Explain detail. 4 (30) Define the following terms and their effects on the performance of ANN. a) Learning factor b) Momentum factor. c) Number of hidden neuron d) Training data e) Initial Weights Target Output
1). The weight of w12 is damaged. Before this power failure the output of the network...
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...
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 a...
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...
4.7. Consider a two-layer feedforward ANN with two inputs a and b, one hidden unit c, and one output unit d. This net...
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...
A deep learning problem. The following matrices describing a neural network were uncovered by scientists. The...
A deep learning problem.
The following matrices describing a neural network were uncovered by scientists. The weights for the hidden layer are given in the matrix W[1] = [0 1] The bias for the hidden layer is given in the vector b[1] = [1] The weights for the output layer are given in the vector W[2] [8] 0 1 The biases for the output layer are 612] = -0.5 0.75 The input X is given in the vector X 1.25...
Once again, consider the use of DNN for classification task with the specific architecture below that we have encounter...
Once again, consider the use of DNN for classification task with the specific architecture below that we have encountered in the class as well as in assignment 1. This question will investigate deeper into this network to provide it with further flexiblity. sottmax Output layer h(x) Hidden layer hx) w-1, bi-i 2,ha Hidden layer Wih1 Input layer h° (x)x Since the last layer has lwa hidden units followed by a softmax functio, this DNN is a binary classifier. Binary classifier...
1. For the function (t) below, T 2 and Vm-100 V. vt) 3 2 012 3...
1. For the function (t) below, T 2 and Vm-100 V. vt) 3 2 012 3 (a) Sketch v'(t) and derive the Fourier coefficients for '(t). (b) Use your results from part (a) to determine for Fourier coefficients for v(t). Express your solution in the complex form of the Fourier series, nugt and verify your solution by plotting your results using Matlab. 2. Assume that the signal above is the input to the bandpass filter shown below. y (t (a)...
Exercise Optimization in neural network Consider a very simple neural network with two input values, one output value, and a single neuron with sigmoid activation. Each input to the neuron has an associated weight, and the neuron has a bias. So the network represents functions of the form o(W1X1 + W222 + b). We train the neural network using least squares loss on a single piece of training data ((1, -1),0). Initially all weights and biases are set to 1....
1. Compared with PID Control, what are the advantages and disadvantages of Neural Network Control? 2. The multi-layer neural network shown in Figure I has two inputs and one output. The network has two neurons in a hidden layer. The network is to be trained with backpropagation algorithm. Each neuron has a sigmoid activation function: Assume that the biases to the neurons is +1 and the learning rate is 1. The network has the following initial weights: (w). w1 wa1...
5. (10 points) Optimization in neural network Consider a very simple neural network with two input values, one output value, and a single neuron with sigmoid activation. Each input to the neuron has an associated weight, and the neuron has a bias. So the network represents functions of the form o(W121 + W222 +b). We train the neural network using least squares loss on a single piece of training data ((1, -1),0). Initially all weights and biases are set to...
2. (20) Design an artificial neural network with two hidden layers. First hidden layer has s neurons, second hidden layer has 3 neurons. Input parameters are 3, output parameter i s (20) What is the fundemental philosophy in backpropagation training algorithm, Explain detail. 4 (30) Define the following terms and their effects on the performance of ANN. a) Learning factor b) Momentum factor. c) Number of hidden neuron d) Training data e) Initial Weights Target Output
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...
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...
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...
A deep learning problem.
The following matrices describing a neural network were uncovered by scientists. The weights for the hidden layer are given in the matrix W[1] = [0 1] The bias for the hidden layer is given in the vector b[1] = [1] The weights for the output layer are given in the vector W[2] [8] 0 1 The biases for the output layer are 612] = -0.5 0.75 The input X is given in the vector X 1.25...
Once again, consider the use of DNN for classification task with the specific architecture below that we have encountered in the class as well as in assignment 1. This question will investigate deeper into this network to provide it with further flexiblity. sottmax Output layer h(x) Hidden layer hx) w-1, bi-i 2,ha Hidden layer Wih1 Input layer h° (x)x Since the last layer has lwa hidden units followed by a softmax functio, this DNN is a binary classifier. Binary classifier...
1. For the function (t) below, T 2 and Vm-100 V. vt) 3 2 012 3 (a) Sketch v'(t) and derive the Fourier coefficients for '(t). (b) Use your results from part (a) to determine for Fourier coefficients for v(t). Express your solution in the complex form of the Fourier series, nugt and verify your solution by plotting your results using Matlab. 2. Assume that the signal above is the input to the bandpass filter shown below. y (t (a)...
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