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In this section we create the model object and define the loss function and optimizer. For the loss function use nn.CrossEntropyLoss() instead of the mean-squared loss done in class. Use the same optimizer as in class. learning_rate = 0.001 num_epochs = 5 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") #this is to use gpu if available # **create your neural net model here, call the variable 'model'** # ** complete following lines of code to define loss function and optimizer #criterion = **complete this** #optimizer = **complete this**

In this section we create the model object and define the loss function and optimizer. For the loss function use nn.CrossEntropyLoss() instead of the mean-squared loss done in class. Use the same optimizer as in class. learning_rate = 0.001 num_epochs = 5 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") #this is to use gpu if available # **create your neural net model here, call the variable 'model'** # ** complete following lines of code to define loss function and optimizer #criterion = **complete this** #optimizer = **complete this**

Database System Concepts
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
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In this section we create the model object and define the loss function and optimizer.

  • For the loss function use nn.CrossEntropyLoss() instead of the mean-squared loss done in class.
  • Use the same optimizer as in class.

 

learning_rate = 0.001
num_epochs = 5

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") #this is to use gpu if available
 
# **create your neural net model here, call the variable 'model'**

# ** complete following lines of code to define loss function and optimizer
#criterion = **complete this**
#optimizer = **complete this**
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