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2 years ago
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')

inputs
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')

targets
array([[ 56.,  70.],
       [ 81., 101.],
       [119., 133.],
       [ 22.,  37.],
       [103., 119.]], dtype=float32)
# Convert both input and target arrays into tensors

inputs = torch.from_numpy(inputs)
targets = torch.from_numpy(targets)


inputs, 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.]]))
# Random weights and biases

w = torch.randn(2, 3, requires_grad=True)
b = torch.randn(2, requires_grad=True)

w, b

(tensor([[ 0.5532, -0.0945, -0.4240],
         [-0.7621,  0.0267,  0.2105]], requires_grad=True),
 tensor([1.9154, 1.0826], requires_grad=True))