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
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
Section: Chapter Questions
Problem 1PE
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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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