#The learning part of linear regression is to figure out a set of weights w11, w12,... w23, b1 & b2 using the training data, to make accurate predictions for new data. The learned weights will be used to predict the yields for apples and oranges in a new region using the average temperature, rainfall, and humidity for that region.
#

#We'll train our model by adjusting the weights slightly many times to make better predictions, using an optimization technique called gradient descent. Let's begin by importing Numpy and PyTorch.

#TRAINING DATA

import numpy as np
import torch

# Input (temp, rainfall, humidity)
inputs = np.array([[73, 67, 43], 
                   [91, 88, 64], 
                   [87, 134, 58], 
                   [102, 43, 37], 
                   [69, 96, 70]], dtype='float32')
# Targets (apples, oranges)
targets = np.array([[56, 70], 
                    [81, 101], 
                    [119, 133], 
                    [22, 37], 
                    [103, 119]], dtype='float32')