IMPLEMENTATION OF CTC LOSS
In PyTorch, the ‘torch.nn.CTCLoss’ class is used to implement the Connectionist Temporal Classification (CTC) loss
Python3
import torchimport torch.nn as nnctc_loss = nn.CTCLoss()loss = ctc_loss(log_probs, targets, input_lengths, target_lengths) |
The arguments that needs to be passed are
- log_probs: The input sequence of log probabilities. It is typically the output of a neural network applied to the input sequence.
- targets: The target sequence. This is usually a 1-dimensional tensor of class indices.
- input_lengths: A 1-dimensional tensor containing the lengths of each sequence in the batch.
- target_lengths: A 1-dimensional tensor containing the lengths of each target sequence in the batch.
Assuming that we have a model, dataloader instantiated we can use CTC loss as below.
Python3
import torchimport torch.nn as nnimport torch.optim as optim# First define your model# Second define your dataloader to give inputs, targets,input_length and target_lengthctc_loss = nn.CTCLoss()optimizer = optim.Adam(model.parameters(), lr=0.001)for epoch in range(num_epochs): for inputs, targets, input_lengths, target_lengths in dataloader: optimizer.zero_grad() outputs = model(inputs) loss = ctc_loss(outputs, targets, input_lengths, target_lengths) loss.backward() optimizer.step() |
One can adapt the above code as below:
- Define the neural network model for the sequence-to-sequence task.
- Ensure that the input and target sequences are appropriately shaped and padded. Define a DataLoader to iterate over your dataset, providing batches of input sequences (inputs), target sequences (targets), input sequence lengths (input_lengths), and target sequence lengths (target_lengths).
- Instantiate the CTC loss (nn.CTCLoss()) and an optimizer (Adam optimizer in this case) to optimize the parameters of your model.
- Intiate the training: The outer loop iterates over the specified number of epochs and the CTC loss is computed by comparing the predicted outputs (outputs) with the target sequences (targets).After calculating the loss, the gradients are computed via backpropagation (loss.backward()), and the optimizer is used to update the model parameters (optimizer.step()).
Connectionist Temporal Classification
CTC is an algorithm employed for training deep neural networks in tasks like speech recognition and handwriting recognition, as well as other sequential problems where there is no explicit information about alignment between the input and output. CTC provides a way to get around when we don’t know how the inputs maps to the output.
Contact Us