Implementing random search

Hyperparameter search is a computationally costly approach to experiment with different hyperparameter values. However, it can lead to performance improvements. In this exercise, you will implement a random search algorithm.

You will randomly sample 10 values of the learning rate and momentum from the uniform distribution. To do so, you will use the np.random.uniform() function.

numpy package has already been imported as np, and a plot_hyperparameter_search() function has been created to visualize your results.

Questo esercizio fa parte del corso

Introduction to Deep Learning with PyTorch

Visualizza il corso

Istruzioni dell'esercizio

Randomly sample a learning rate factor between 2 and 4 so that the learning rate (lr) is bounded between \(10^{-2}\) and \(10^{-4}\).
Randomly sample a momentum between 0.85 and 0.99.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

values = []
for idx in range(10):
    # Randomly sample a learning rate factor between 2 and 4
    factor = ____
    lr = 10 ** -factor
    
    # Randomly select a momentum between 0.85 and 0.99
    momentum = ____
    
    values.append((lr, momentum))
       
plot_hyperparameter_search(values)

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Introduction to Deep Learning with PyTorch

IntermediárioNível de habilidade

4.8+

Inizia il corso gratis

Self-driving cars, smartphones, search engines... Deep learning is now everywhere. Before you begin building complex models, you will become familiar with PyTorch, a deep learning framework. You will learn how to manipulate tensors, create PyTorch data structures, and build your first neural network in PyTorch with linear layers.

Exercise 1: Introduction to deep learning with PyTorch Exercise 2: Getting started with PyTorch tensors Exercise 3: Checking and adding tensors Exercise 4: Neural networks and layers Exercise 5: Linear layer network Exercise 6: Understanding weights Exercise 7: Hidden layers and parameters Exercise 8: Your first neural network Exercise 9: Stacking linear layers Exercise 10: Counting the number of parameters

To train a neural network in PyTorch, you will first need to understand additional components, such as activation and loss functions. You will then realize that training a network requires minimizing that loss function, which is done by calculating gradients. You will learn how to use these gradients to update your model's parameters.

Exercise 1: Discovering activation functions Exercise 2: Activate your understanding!Exercise 3: The sigmoid and softmax functions Exercise 4: Running a forward pass Exercise 5: Building a binary classifier in PyTorch Exercise 6: From regression to multi-class classification Exercise 7: Using loss functions to assess model predictions Exercise 8: Creating one-hot encoded labels Exercise 9: Calculating cross entropy loss Exercise 10: Using derivatives to update model parameters Exercise 11: Accessing the model parameters Exercise 12: Updating the weights manually Exercise 13: Using the PyTorch optimizer

Now that you've learned the key components of a neural network, you'll train one using a training loop. You'll explore potential issues like vanishing gradients and learn strategies to address them, such as alternative activation functions and tuning learning rate and momentum.

Exercise 1: A deeper dive into loading data Exercise 2: Using TensorDataset Exercise 3: Using DataLoader Exercise 4: Writing our first training loop Exercise 5: Using the MSELoss Exercise 6: Writing a training loop Exercise 7: ReLU activation functions Exercise 8: Implementing ReLU Exercise 9: Implementing leaky ReLU Exercise 10: Understanding activation functions Exercise 11: Learning rate and momentum Exercise 12: Experimenting with learning rate Exercise 13: Experimenting with momentum

Training a deep learning model is an art, and to make sure our model is trained correctly, we need to keep track of certain metrics during training, such as the loss or the accuracy. We will learn how to calculate such metrics and how to reduce overfitting.

Exercise 1: Layer initialization and transfer learning Exercise 2: Fine-tuning process Exercise 3: Freeze layers of a model Exercise 4: Layer initialization Exercise 5: Evaluating model performance Exercise 6: Writing the evaluation loop Exercise 7: Calculating accuracy using torchmetrics Exercise 8: Fighting overfitting Exercise 9: Experimenting with dropout Exercise 10: Understanding overfitting Exercise 11: Improving model performance Exercise 12: Implementing random search

Esercizio in corso

Exercise 13: Wrap-up video