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

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
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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

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