1. Introduction to Deep Learning and PyTorch

Activity 1.01: Creating a Single-Layer Neural Network

Solution

1. Import the required libraries, including pandas, for importing a CSV file:

   import pandas as pd
   import torch
   import torch.nn as nn
   import matplotlib.pyplot as plt

2. Read the CSV file containing the dataset:

   data = pd.read_csv("SomervilleHappinessSurvey2015.csv")

3. Separate the input features from the target. Note that the target is located in the first column of the CSV file. Convert the values into tensors, making sure the values are converted into floats:

   x = torch.tensor(data.iloc[:,1:].values).float()
   y = torch.tensor(data.iloc[:,:1].values).float()

4. Define the architecture of the model and store it in a variable named model. Remember to create a single-layer model:

   model = nn.Sequential(nn.Linear(6, 1),
                         nn.Sigmoid())

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