1 Question 1A Deep Neural Network is shown in Figure 1. The aim of this network is to classify human and dog.1. Calculate the initial error of the Deep Neural Network by feeding the following input: X₁ = 5, X₂ = 2.The X₁ indicates the length of the tail and X₂ indicates the number of eyes.2. Update the layer 2 weights (ws,we,w7,ws) of the Deep Neural Network shown in Figure 1 for a singleiteration. The learning rate is set to a = 1 and there are no biases in the neurons.3. Recalculate the error with the updated weights of layer 2 and report its improvement with respect tothe initial error (percentage wise).4. (Bonus Question) Update and report the layer 1 weights (w₁,₂,3,w₁) of the network and estimatethe improvement to the error..InputX₁-5X₂-2W₂-0W₂=0w₂ = 0W₁=0Layer 13₁W₁=0₂0w₂0Layer 2PredictedOutputRealOutput10Figure 1: A Deep Neural Network designed for dog or human detection Formula SheetThe ANN neurons function is described as below:(1)where w, b, X and o(2) are the weight, bias, input and activation function respectively. Sigmoid functiono(z)= is widely used as an activation function (outputs 0-1).The error function can also be shown as the following:ny = o(w₂X₁ + bi)i=1JE₁E =Əw(L)i=1y(2) = $ (z(L))The parital derivative of error, with respect of the weight for layer (L) is calculated as below:(3 - 9)²- = y(L-1) ó' (z(L)) 2 (y(L) — ŷ),where the derivative of the sigmoid activation function is o' (z(2)) = 6 (z(¹)) ((1 − 6 (z(¹²))).For each iteration, the weight can then be updated by the following:JE'dw'where learning rate a dictates how fast the weights need to be adjusted.ww-a-(2)(3)(4)(5)

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1 Question 1 A Deep Neural Network is shown in Figure 1. The aim of this network is to classify human and dog. 1. Calculate the initial error of the Deep Neural Network by feeding the following input: X₁ = 5, X₂ = 2. The X₁ indicates the length of the tail and X₂ indicates the number of eyes. 2. Update the layer 2 weights (ws,we, w7 ws) of the Deep Neural Network shown in Figure 1 for a single iteration. The learning rate is set to a = 1 and there are no biases in the neurons. 3. Recalculate the error with the updated weights of layer 2 and report its improvement with respect to the initial error (percentage wise). 4. (Bonus Question) Update and report the layer 1 weights (w₁,w₂, 3, 4) of the network and estimate the improvement to the error. Input /₂ = 0 Layer 1 3/₂ /₂0 Layer 2 Predicted Output Real Output 1 0 BE Figure 1: A Deep Neural Network designed for dog or human detection

1 Question 1 A Deep Neural Network is shown in Figure 1. The aim of this network is to classify human and dog. 1. Calculate the initial error of the Deep Neural Network by feeding the following input: X₁ = 5, X₂ = 2. The X₁ indicates the length of the tail and X₂ indicates the number of eyes. 2. Update the layer 2 weights (ws,we, w7 ws) of the Deep Neural Network shown in Figure 1 for a single iteration. The learning rate is set to a = 1 and there are no biases in the neurons. 3. Recalculate the error with the updated weights of layer 2 and report its improvement with respect to the initial error (percentage wise). 4. (Bonus Question) Update and report the layer 1 weights (w₁,w₂, 3, 4) of the network and estimate the improvement to the error. Input /₂ = 0 Layer 1 3/₂ /₂0 Layer 2 Predicted Output Real Output 1 0 BE Figure 1: A Deep Neural Network designed for dog or human detection

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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Select the correct option. Given sequential data: 1. feed forward neural networks can successfully cope with it only if their input layer is wide enough 2. even after a bit of data hacking, feed forward neural networks struggle to meet sequential data's basic requirements 3. recurrent neural networks are not so great in dealing with it 4. none of the above.
Please provide an illustration of how a neural network that has an excessive number of layers could result in an issue.Overfitting will be discussed, along with ways to prevent it, in the following paragraphs.
Neural network regression of car-prices We assume that the car prices (y) can be predicted as ?=?(?) where ?=[?0..?9..] are the car features and ? is a three layer fully connected neural network. We first create the matrix X_train, whose columns are the normalized training features. Create a three layer neural network with hidden layers of h1 and h2 neurons respectively. Use RELU activations at the hidden layers and no activation on the output layer. class Net(nn.Module): def __init__(self,Nfeatures,Noutput,Nh1=10,Nh2=10): super(Net, self).__init__() # YOUR CODE HERE def forward(self, x): # YOUR CODE HERE featureScale = np.max(np.abs(X_train),axis=0,keepdims=True)X_train_T = X_train/featureScaleX_train_T = torch.tensor(X_train_T.astype(np.float32)) ymax = np.max(y_train,axis=0,keepdims=True)y_train_T = torch.tensor(y_train.astype(np.float32)/ymax).unsqueeze(1) Training YOUR CODE BELOW Define a network with 20 features/neurons in hidden…
Neural network regression of car-prices We assume that the car prices (y) can be predicted as ?=?(?) where ?=[?0..?9..] are the car features and ? is a three layer fully connected neural network. We first create the matrix X_train, whose columns are the normalized training features. Create a three layer neural network with hidden layers of h1 and h2 neurons respectively. Use RELU activations at the hidden layers and no activation on the output layer. class Net(nn.Module): def __init__(self,Nfeatures,Noutput,Nh1=10,Nh2=10): super(Net, self).__init__() # YOUR CODE HERE def forward(self, x): # YOUR CODE HERE
Which of the following statements about various neural network layers is true? (Multiple Answers Possible) A. A perceptron, or fully-connected layer, lacks the ability to capture class imbalance information in training data. B. A multi-layer perceptron can consider the interactions between multiple input fea- tures and an output class. C. A convolutional layer is best applied when the input to that layer contains local structure. D. A pooling layer can only be applied to 2-dimensional or higher input data. E. A vanilla RNN cell can be formulated as a fully-connected layer operation, half of whose inputs at each timestep from previous output and half from an input sequence.
a. Define overfitting and underfitting in the context of neural networks. How do they affect themodel's performance on unseen data?
10. Describe the convolutional layer in neural networks. How is it related to regularization? 11. Describe how batch normalization is performed and in which way it helps with training multilayer neural networks with ReLU activation function. 12. What is the significance of the Representer Theorem for learning using kernels (e.g. Gaussians).
1. Explain how the back-propagation algorithm uses the delta rule to train a 3-layer network. (Use a 2-D input pattern space, a 2-D hidden layer, and a 1-D output layer to explain the delta rule in back-propagation). 2. How do you set up a 3-layer neural net using Neurolab in Python to set up the network and train it. Explain how it is set up and trained.
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Lab 8: Deep LearningOverviewFor this exercise we will continue working with the Titanic data from the previous labs.Set-up1. Load the Titanic and Titanic_hidden datasetsLab Steps1. Create a training/test set using the Titanic data Note: If you’re using the technique shown in class:the matrix model will exclude 1 and Name2. Set up a deep neural network with the following: two hidden layers with 30 units and ReLU activationand an output layer with a single unit and sigmoid activation3. Compile the model with binary_crossentropy loss and the accuracy metric4. Fit the model on the training set, evaluate it with the test set5. Get the titanic_hidden in a form Keras can use: you’ll can set it up with code similar to this:new_data_x <- titanic_hidden %>% model.matrix(object = Survived ~ . -1 -Name) %>% scale()new_data_y <- titanic_hidden$Survived6. Test the model’s accuracy with the hidden data it’s never seen before solve it using Python