Jovian
Sign In
Learn practical skills, build real-world projects, and advance your career

Linear Regression

We'll create a model that predicts crop yields for apples and oranges (target variables) by looking at the average temperature, rainfall, and humidity (input variables or features) in a region. Here's the training data:
import numpy as np
import torch
# Input matrix
inputs = np.array([[73,67,43],
                 [91,88,64],
                 [87,134,58],
                   [102, 43, 37], 
                   [69, 96, 70]], dtype='float32')
inputs
array([[ 73.,  67.,  43.],
       [ 91.,  88.,  64.],
       [ 87., 134.,  58.],
       [102.,  43.,  37.],
       [ 69.,  96.,  70.]], dtype=float32)
targets = np.array([[56, 70], 
                    [81, 101], 
                    [119, 133], 
                    [22, 37], 
                    [103, 119]], dtype='float32')
targets
array([[ 56.,  70.],
       [ 81., 101.],
       [119., 133.],
       [ 22.,  37.],
       [103., 119.]], dtype=float32)
inputs = torch.from_numpy(inputs)
targets = torch.from_numpy(targets)
print(inputs)
print(targets)
tensor([[ 73., 67., 43.], [ 91., 88., 64.], [ 87., 134., 58.], [102., 43., 37.], [ 69., 96., 70.]]) tensor([[ 56., 70.], [ 81., 101.], [119., 133.], [ 22., 37.], [103., 119.]])