İki çıktılı model mimarisi

Bu egzersizde, hem karakteri hem de alfabeyi tahmin edebilen çok çıktılı bir sinir ağı mimarisi kuracaksın.

Genel yapıyı hatırla: .__init__() metodunda, ileri geçişte kullanacağın katmanları tanımlarsın. forward() metodunda ise önce giriş görüntüsünü birkaç katmandan geçirip bir gömleme (embedding) elde edeceksin; ardından bu gömlemeyi iki ayrı sınıflandırıcı katmana (her çıktı için bir tane) vereceksin.

torch.nn zaten alışık olduğumuz kısaltmayla içe aktarılmış durumda, hadi bir model kuralım!

Bu egzersiz

PyTorch ile Orta Düzey Deep Learning

kursunun bir parçasıdır

Kursu Görüntüle

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

class Net(nn.Module):
    def __init__(self):
        super().__init__()
        self.image_layer = nn.Sequential(
            nn.Conv2d(1, 16, kernel_size=3, padding=1),
            nn.MaxPool2d(kernel_size=2),
            nn.ELU(),
            nn.Flatten(),
            nn.Linear(16*32*32, 128)
        )
        # Define the two classifier layers
        ____ = ____
        ____ = ____

Kodu Düzenle ve Çalıştır

Bu egzersiz

PyTorch ile Orta Düzey Deep Learning

kursunun bir parçasıdır

IntermediárioNível de habilidade

4.8+

Kursa Ücretsiz Başlayın

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: Çok girdili modeller Exercise 2: İki girişli veri kümesi Exercise 3: İki girdili model Exercise 4: İki girdili modeli eğitme Exercise 5: Çok çıktılı modeller Exercise 6: İki Çıktılı Dataset ve DataLoader Exercise 7: İki çıktılı model mimarisi

Geçerli Egzersiz

Exercise 8: Çoklu çıktı veren modelleri eğitme Exercise 9: Çok çıktılı modellerin değerlendirilmesi ve kayıp ağırlıklandırma Exercise 10: Çoklu çıktı model değerlendirmesi Exercise 11: Kayıp ağırlıklandırma Exercise 12: Kapanış