Inisialisasi layer

Inisialisasi bobot jaringan saraf telah menjadi fokus para peneliti selama bertahun-tahun. Saat melatih jaringan, metode yang digunakan untuk menginisialisasi bobot berdampak langsung pada kinerja akhir jaringan.

Sebagai praktisi machine learning, Anda perlu mampu bereksperimen dengan berbagai strategi inisialisasi. Pada latihan ini, Anda membuat sebuah jaringan saraf kecil yang terdiri atas dua layer dan Anda memutuskan untuk menginisialisasi bobot setiap layer menggunakan metode uniform.

Latihan ini adalah bagian dari kursus

Pengantar Deep Learning dengan PyTorch

Petunjuk latihan

Untuk setiap layer (layer0 dan layer1), gunakan metode inisialisasi uniform untuk menginisialisasi bobotnya.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

layer0 = nn.Linear(16, 32)
layer1 = nn.Linear(32, 64)

# Use uniform initialization for layer0 and layer1 weights
nn.____.____(____)
nn.____.____(____)

model = nn.Sequential(layer0, layer1)

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Pengantar Deep Learning dengan PyTorch

SkillTag.level.intermediateSkillTag.label

4.8+

Mulai Kursus 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: Inisialisasi layer dan transfer learning Exercise 2: Proses fine-tuning Exercise 3: Bekukan layer pada sebuah model Exercise 4: Inisialisasi layer

Latihan Saat Ini

Exercise 5: Mengevaluasi kinerja model Exercise 6: Menulis evaluation loop Exercise 7: Menghitung akurasi menggunakan torchmetrics Exercise 8: Mengatasi overfitting Exercise 9: Mencoba dropout Exercise 10: Memahami overfitting Exercise 11: Meningkatkan kinerja model Exercise 12: Menerapkan pencarian acak Exercise 13: Video penutup