Modelleri değerlendirme

İki model eğitildi ve hazır: çok sayıda parametreye sahip large_model ve daha az parametreli small_model. Her iki model de senin için hazır olan train_features ve train_labels ile eğitildi. Ayrıca test_features ve test_labels'dan oluşan ayrı bir test kümesi de var.

Amacın, modellerin göreli performansını değerlendirmek ve modellerden herhangi birinin aşırı öğrenme (overfitting) belirtisi gösterip göstermediğini belirlemek. Bunu, large_model ve small_model'i hem eğitim hem de test kümelerinde değerlendirerek yapacaksın. Her model için, özellikler x ve etiketler y için kaybı (loss) hesaplamak üzere .evaluate(x, y) yöntemini uygulayabilirsin. Sonrasında ortaya çıkan dört kaybı karşılaştıracaksın.

Bu egzersiz

Python ile TensorFlow’a Giriş

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

Küçük modeli eğitim verisiyle değerlendir.
Küçük modeli test verisiyle değerlendir.
Büyük modeli eğitim verisiyle değerlendir.
Büyük modeli test verisiyle değerlendir.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Evaluate the small model using the train data
small_train = small_model.evaluate(____, ____)

# Evaluate the small model using the test data
small_test = ____

# Evaluate the large model using the train data
large_train = large_model.evaluate(____, ____)

# Evaluate the large model using the test data
large_test = ____

# Print losses
print('\n Small - Train: {}, Test: {}'.format(small_train, small_test))
print('Large - Train: {}, Test: {}'.format(large_train, large_test))

Kodu Düzenle ve Çalıştır

Bu egzersiz

Python ile TensorFlow’a Giriş

kursunun bir parçasıdır

IntermediárioNível de habilidade

4.8+

Kursa Ücretsiz Başlayın

Before you can build advanced models in TensorFlow 2, you will first need to understand the basics. In this chapter, you’ll learn how to define constants and variables, perform tensor addition and multiplication, and compute derivatives. Knowledge of linear algebra will be helpful, but not necessary.

Exercise 1: Constants and variables Exercise 2: Defining data as constants Exercise 3: Defining variables Exercise 4: Basic operations Exercise 5: Performing element-wise multiplication Exercise 6: Making predictions with matrix multiplication Exercise 7: Summing over tensor dimensions Exercise 8: Advanced operations Exercise 9: Reshaping tensors Exercise 10: Optimizing with gradients Exercise 11: Working with image data

In this chapter, you will learn how to build, solve, and make predictions with models in TensorFlow 2. You will focus on a simple class of models – the linear regression model – and will try to predict housing prices. By the end of the chapter, you will know how to load and manipulate data, construct loss functions, perform minimization, make predictions, and reduce resource use with batch training.

Exercise 1: Input data Exercise 2: Load data using pandas Exercise 3: Setting the data type Exercise 4: Loss functions Exercise 5: Loss functions in TensorFlow Exercise 6: Modifying the loss function Exercise 7: Linear regression Exercise 8: Set up a linear regression Exercise 9: Train a linear model Exercise 10: Multiple linear regression Exercise 11: Batch training Exercise 12: Preparing to batch train Exercise 13: Training a linear model in batches

The previous chapters taught you how to build models in TensorFlow 2. In this chapter, you will apply those same tools to build, train, and make predictions with neural networks. You will learn how to define dense layers, apply activation functions, select an optimizer, and apply regularization to reduce overfitting. You will take advantage of TensorFlow's flexibility by using both low-level linear algebra and high-level Keras API operations to define and train models.

Exercise 1: Dense layers Exercise 2: The linear algebra of dense layers Exercise 3: The low-level approach with multiple examples Exercise 4: Using the dense layer operation Exercise 5: Activation functions Exercise 6: Binary classification problems Exercise 7: Multiclass classification problems Exercise 8: Optimizers Exercise 9: The dangers of local minima Exercise 10: Avoiding local minima Exercise 11: Training a network in TensorFlow Exercise 12: Initialization in TensorFlow Exercise 13: Defining the model and loss function Exercise 14: Training neural networks with TensorFlow

In the final chapter, you'll use high-level APIs in TensorFlow 2 to train a sign language letter classifier. You will use both the sequential and functional Keras APIs to train, validate, make predictions with, and evaluate models. You will also learn how to use the Estimators API to streamline the model definition and training process, and to avoid errors.

Exercise 1: Keras ile sinir ağlarını tanımlama Exercise 2: Keras'ta sequential model Exercise 3: Sıralı bir modeli derlemek Exercise 4: Birden fazla girdili bir model tanımlama Exercise 5: Keras ile eğitim ve doğrulama Exercise 6: Keras ile Eğitim Exercise 7: Keras ile metrikler ve doğrulama Exercise 8: Aşırı öğrenmeyi tespit etme Exercise 9: Modelleri değerlendirme

Geçerli Egzersiz

Exercise 10: Estimators API ile modelleri eğitme Exercise 11: Estimators ile eğitime hazırlık Exercise 12: Estimator'ları Tanımlama Exercise 13: Tebrikler!