A Recurrent Neural Network (RNN) often uses ordered sequences as inputs.
Real-world sequences have different lengths, especially in Natural Language
Processing (NLP) because all words don’t have the same number of characters and
all sentences don’t have the same number of words. In
PyTorch, the inputs of a neural network are often
managed by a
DataLoader groups the input in batches. This is better for training a neural
network because it’s faster and more efficient than sending the inputs one by
one to the neural network. The issue with this approach is that it assumes every
input has the same shape. As stated before, sequences don’t have a consistent
shape, so how one can train a RNN in PyTorch with variable-length sequences and
still benefit from the
Here I will show a complete training example based on an official PyTorch RNN tutorial, whose goal is to classify names according to their origin.
To create a
we first need to create an
that represents how to generate training examples.
Here we copy the code and functions from the PyTorch tutorial and define a
__iter__() method that calls
random_training_example(). This returns:
- The origin of the name (the country)
- The name itself
- The integer-encoded category tensor
- The integer-encoded name tensor (of variable length)
This part highlights the problem with variable length sequences.
random_training_example() generates data of variable lengths because names
have inconsistent lengths. If we create a
DataLoader with our
IterableDataset, PyTorch will complain that it cannot create batches of
examples if they all have different shapes. We must therefore create an
collate() function that will equalize the sequences lengths and
DataLoader to call the collate function.
I have taken this function from here and adapted it to our custom
IterableDataset. This also makes the code compatible with the rest of the tutorial. It works as follows. When executed, the function
__call__() gets N items coming from the
__iter__() method defined earlier, where N is the size of the batches. Line 3-6 retrieve the 4 parts returned by
random_training_example(). Then line 7 zips the items so that we have a Python
list of N elements composed of
data (the integer-encoded names),
labels (the integer-encoded labels),
data_text. Then, line 8, the list is sorted according to the length of the first item (the integer-encoded name) so that the longest names come first and the shortest ones come last. On line 9 and 10, the sequences representing the names are retrieved and padded with the
pad_sequence() function meaning that they are filled with a padding value. To understand what
pad_sequence() does, let’s see an example:
If we have a
sequences variable (a list of integer-encoded names) of:
tensor([20, 18, 19, 24, 20, 25, 7, 0, 13, 8, 13]) tensor([ 0, 22, 4, 17, 24, 0, 13, 14, 5, 5]) tensor([15, 4, 19, 19, 8, 6, 17, 4, 22]) tensor([ 1, 4, 11, 17, 14, 18, 4]) tensor([10, 0, 11, 20, 25, 0]) tensor([15, 14, 20, 11, 8, 13]) tensor([21, 8, 2, 19, 14, 17]) tensor([3, 0, 13, 10, 18])
pad_sequence() will output a PyTorch Tensor:
tensor([[20, 0, 15, 1, 10, 15, 21, 3], [18, 22, 4, 4, 0, 14, 8, 0], [19, 4, 19, 11, 11, 20, 2, 13], [24, 17, 19, 17, 20, 11, 19, 10], [20, 24, 8, 14, 25, 8, 14, 18], [25, 0, 6, 18, 0, 13, 17, 0], [ 7, 13, 17, 4, 0, 0, 0, 0], [ 0, 14, 4, 0, 0, 0, 0, 0], [13, 5, 22, 0, 0, 0, 0, 0], [ 8, 5, 0, 0, 0, 0, 0, 0], [13, 0, 0, 0, 0, 0, 0, 0]])
The first array in the Tensor
[20, 0, 15, 1, 10, 15, 21, 3] is composed of the first integer-encoded character of each name (the first column of the
sequences). That is, the first name in the batch starts with 20, the second name in the batch starts with 0 and so on. The 6th array (representing the 6th character of each name) in the Tensor
[25, 0, 6, 18, 0, 13, 17, 0] has a 0 at the end because the last name has only 5 characters, so a 0 is used as a placeholder. The same logic applies for all subsequent arrays. Note that using 0 as a padding value is not an issue, because as we’ll see it cannot be confused with the integer-encoded ‘a’ character by the RNN model.
After the padding, line 11 we get the length of each name in the sorted list, and lines 12-14 retrieve the labels and textual representations of the input in the order of the sorted batch (so they’re in the same order as the padded sequences).
Constructing the RNN model #
Here we define the RNN model, composed of 4 steps:
- Map the padded, integer-encoded characters to embedding vectors
- Unpad the sequences and feed them to a Gated Recurrent Unit (GRU)
- Get the GRU output and feed it to a linear layer
- Apply the softmax function to interpret the output as probabilities
Lines 5-10 are the definition of the three layers (embeddings, GRU and linear)
and their initialization. The
forward() function takes the padded, integer-encoded sequences and their lengths as an input. Line 13 maps each integer-encoded character to an embedding vector. In this step, the padding value (0) is also mapped to an embedding vector, because it is confused with the letter ‘a’. But this is not an issue because line 14 the sequences are packed according to their original lengths so that the GRU doesn’t see the padded values. Line 16, we use the
pad_packed_sequence() function on the GRU output to reverse the previous packing. Lines 18-24 retrieve the last output for each sequence and applies the last activation function. Line 26 applies the linear layer and line 28 the softmax function.
The training loop #
The training loop simply gets training examples, feeds them into the model, computes the loss and updates the model’s weight. Please refer to the PyTorch tutorial for a more useful training loop.