TensorFlow ile sinir ağlarını eğitme

Önceki egzersizde bir model, model(w1, b1, w2, b2, features), ve bir kayıp fonksiyonu, loss_function(w1, b1, w2, b2, features, targets), tanımladın; ikisi de bu egzersizde kullanılabilir durumda. Şimdi modeli eğitecek ve ardından test_features ve test_targets'tan oluşan ve sana sağlanan bir test kümesinde temerrüt sonuçlarını tahmin ederek performansını değerlendireceksin. Eğitilebilir değişkenler w1, b1, w2 ve b2'dir. Ayrıca şu işlemler senin için içe aktarıldı: keras.activations.relu() ve keras.layers.Dropout().

Bu egzersiz

Python ile TensorFlow’a Giriş

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

Eniyileyiciyi minimizasyon yapacak şekilde ayarla.
Dört eğitilebilir değişkeni loss_function()'a argüman olarak göründükleri sırayla var_list'e ekle.
Modeli ve test_features'ı kullanarak test_targets için değerleri tahmin et.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Train the model
for j in range(100):
    # Complete the optimizer
	opt.____(lambda: loss_function(w1, b1, w2, b2), 
                 var_list=[____, ____, ____, ____])

# Make predictions with model using test features
model_predictions = model(w1, b1, w2, b2, ____)

# Construct the confusion matrix
confusion_matrix(test_targets, model_predictions)

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: Yoğun katmanlar Exercise 2: Yoğun katmanların lineer cebiri Exercise 3: Birden çok örnekle düşük seviyeli yaklaşım Exercise 4: Yoğun katman işlemini kullanma Exercise 5: Aktivasyon fonksiyonları Exercise 6: İkili sınıflandırma problemleri Exercise 7: Çok sınıflı sınıflandırma problemleri Exercise 8: Optimize Ediciler Exercise 9: Yerel minimumların tehlikeleri Exercise 10: Yerel minimadan kaçınma Exercise 11: TensorFlow'da bir ağı eğitme Exercise 12: TensorFlow'da Başlatma Exercise 13: Modeli ve kayıp fonksiyonunu tanımlama Exercise 14: TensorFlow ile sinir ağlarını eğitme

Geçerli Egzersiz

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: Defining neural networks with Keras Exercise 2: The sequential model in Keras Exercise 3: Compiling a sequential model Exercise 4: Defining a multiple input model Exercise 5: Training and validation with Keras Exercise 6: Training with Keras Exercise 7: Metrics and validation with Keras Exercise 8: Overfitting detection Exercise 9: Evaluating models Exercise 10: Training models with the Estimators API Exercise 11: Preparing to train with Estimators Exercise 12: Defining Estimators Exercise 13: Congratulations!