Multi-output models

1. Multi-output models

Welcome back! In this video, we'll look at multi-output models.

2. Why multi-output?

Just like multi-input models, multi-output architectures are everywhere. Their simplest use-case is for multi-task learning, where we want to predict two things from the same input, such as a car's make and model from its picture. In multi-label classification problem, the input can belong to multiple classes simultaneously. For instance, an image can depict both a beach and people. For each of these labels, a separate output from the model is needed. Finally, in very deep models built of blocks of layers, it is a common practice to add extra outputs predicting the same targets after each block. These additional outputs ensure that the early parts of the model are learning features useful for the task at hand while also serving as a form of regularization to boost the robustness of the network.

3. Character and alphabet classification

Let's use the Omniglot dataset again to build a model to predict both the character and the alphabet it comes from based on the image. First, we will pass the image through some layers to obtain its embedding.

4. Character and alphabet classification

Then we add two independent classifiers on top, one for each output.

5. Two-output Dataset

The good news is that we have already done much of the work needed. We can reuse the OmniglotDataset we built before, with just one small difference in the samples we pass it. When the alphabet was an input to the model, we represented it as a one-hot vector. Now that it is an output, all we need is the integer representing the class label, just like with the other output, the character. This will be a number between 0 and 29 since we have 30 alphabets in the Dataset.

6. Two-output architecture

Let's look at the model's architecture. We start with defining a sub-network for processing the image identical to the one we used before. Then, we define two classifier layers, one for each output, with the output shape corresponding to the number of alphabets (30) and characters (964), respectively. In the forward method, we first pass the image through its dedicated sub-network, and then feed the result separately to each of the two classifiers. Finally, we return the two outputs.

7. Training loop

Let's examine the training loop. The beginning should look familiar, except for the fact that now the model produces two outputs instead of one. Having produced these outputs, we calculate the loss for each of them separately using the appropriate target labels. Next, we need to define the total loss for the model to optimize. Here, we just sum the two partial losses together, indicating that the accuracy of predicting the alphabet and the character is equally important. If that is not the case, we can weigh the partial losses with some weights to reflect their relative importance. We will explore this idea later in the next video. Finally, we run backpropagation and the optimization step as always.

8. Let's practice!

Let's build a multi-output model!

This exercise is part of the course

Intermediate Deep Learning with PyTorch

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Learn how to train neural networks in a robust way. In this chapter, you will use object-oriented programming to define PyTorch datasets and models and refresh your knowledge of training and evaluating neural networks. You will also get familiar with different optimizers and, finally, get to grips with various techniques that help mitigate the problems of unstable gradients so ubiquitous in neural nets training.

Exercise 1: PyTorch and object-oriented programming Exercise 2: PyTorch Dataset Exercise 3: PyTorch DataLoader Exercise 4: PyTorch Model Exercise 5: Optimizers, training, and evaluation Exercise 6: Training loop Exercise 7: Optimizers Exercise 8: Model evaluation Exercise 9: Vanishing and exploding gradients Exercise 10: Initialization and activation Exercise 11: Activations: ReLU vs. ELU Exercise 12: Batch Normalization

Train neural networks to solve image classification tasks. In this chapter, you will learn how to handle image data in PyTorch and get to grips with convolutional neural networks (CNNs). You will practice training and evaluating an image classifier while learning about how to improve the model performance with data augmentation.

Exercise 1: Handling images with PyTorch Exercise 2: Image dataset Exercise 3: Data augmentation Exercise 4: Data augmentation in PyTorch Exercise 5: Convolutional Neural Networks Exercise 6: The convolutional layer Exercise 7: Building convolutional networks Exercise 8: Training image classifiers Exercise 9: Choosing augmentations Exercise 10: Dataset with augmentations Exercise 11: Image classifier training loop Exercise 12: Evaluating image classifiers Exercise 13: Multi-class model evaluation Exercise 14: Analyzing metrics per class

Build and train recurrent neural networks (RNNs) for processing sequential data such as time series, text, or audio. You will learn about the two most popular recurrent architectures, Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks, as well as how to prepare sequential data for model training. You will practice your skills by training and evaluating a recurrent model for predicting electricity consumption.

Exercise 1: Handling sequences with PyTorch Exercise 2: Generating sequences Exercise 3: Sequential Dataset Exercise 4: Recurrent Neural Networks Exercise 5: Sequential architectures Exercise 6: Building a forecasting RNN Exercise 7: LSTM and GRU cells Exercise 8: RNN vs. LSTM vs. GRU Exercise 9: LSTM network Exercise 10: GRU network Exercise 11: Training and evaluating RNNs Exercise 12: RNN training loop Exercise 13: Evaluating forecasting models

Build multi-input and multi-output models, demonstrating how they can handle tasks requiring more than one input or generating multiple outputs. You will explore how to design and train these models using PyTorch and delve into the crucial topic of loss weighting in multi-output models. This involves understanding how to balance the importance of different tasks when training a model to perform multiple tasks simultaneously.

Exercise 1: Multi-input models Exercise 2: Two-input dataset Exercise 3: Two-input model Exercise 4: Training two-input model Exercise 5: Multi-output models

Current Exercise

Exercise 6: Two-output Dataset and DataLoader Exercise 7: Two-output model architecture Exercise 8: Training multi-output models Exercise 9: Evaluation of multi-output models and loss weighting Exercise 10: Multi-output model evaluation Exercise 11: Loss weighting Exercise 12: Wrap-up