Daniel Voigt Godoy - Deep Learning with PyTorch Step-by-Step A Beginner’s Guide-leanpub

Output
PackedSequence(data=tensor([[ 1.0349, 0.9661],
[-1.1247, -0.9683],
[-1.0911, 0.9254],
[ 0.8055, -0.9169],
[ 0.8182, -0.9944],
[-1.0771, -1.0414],
[-0.8251, -0.9499],
[ 1.0081, 0.7680],
[-0.8670, 0.9342]]), batch_sizes=tensor([3, 3, 2, 1]),
sorted_indices=tensor([0, 2, 1]), unsorted_indices=tensor([0, 2,
1]))
Variable-Length Dataset
Let’s create a dataset with variable-length sequences and train a model using it:
Data Generation
1 var_points, var_directions = generate_sequences(variable_len=True)
2 var_points[:2]
Output
[array([[ 1.12636495, 1.1570899 ],
[ 0.87384513, -1.00750892],
[-0.9149893 , -1.09150317],
[-1.0867348 , 1.07731667]]),
array([[ 0.92250954, -0.89887678],
[ 1.0941646 , 0.92300589]])]
Data Preparation
We simply cannot use a TensorDataset, because we cannot create a tensor out of a
list of elements with different sizes.
So, we must build a custom dataset that makes a tensor out of each sequence and,
when prompted for a given item, returns the corresponding tensor and associated
label:
664 | Chapter 8: Sequences