Veri türünü ayarlama

Bu egzersizde hem veri yükleyecek hem de türünü ayarlayacaksın. housing hazır ve pandas pd olarak içe aktarılmış durumda. numpy ve tensorflow'u içe aktaracak ve housing içindeki sütunları kullanarak, belirtilen veri türüne sahip, tensorflow içinde kullanılabilir tensörler tanımlayacaksın. Örneğin, price sütununu housing['price'] ile seçebileceğini unutma.

Bu egzersiz

Python ile TensorFlow’a Giriş

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

numpy ve tensorflow'u standart kısaltmalarıyla içe aktar.
price tensörünü 32 bit kayan noktalı sayı veri türüne sahip olacak şekilde bir numpy dizisiyle ayarla.
waterfront tensörünü Boole veri türüne dönüştürmek için tensorflow fonksiyonu cast()'i kullan.
Önce price, sonra waterfront'u yazdır. Önemli bir fark gördün mü?

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Import numpy and tensorflow with their standard aliases
____
____

# Use a numpy array to define price as a 32-bit float
price = np.____(housing['price'], np.____)

# Define waterfront as a Boolean using cast
waterfront = tf.____(housing['waterfront'], tf.____)

# Print price and waterfront
print(____)
print(____)

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: Girdi verisi Exercise 2: pandas kullanarak veri yükleme Exercise 3: Veri türünü ayarlama

Geçerli Egzersiz

Exercise 4: Kayıp fonksiyonları Exercise 5: TensorFlow'da kayıp fonksiyonları Exercise 6: Kayıp fonksiyonunu değiştirme Exercise 7: Doğrusal regresyon Exercise 8: Doğrusal regresyonu kur Exercise 9: Doğrusal bir modeli eğit Exercise 10: Çoklu doğrusal regresyon Exercise 11: Toplu eğitim Exercise 12: Toplu eğitim için hazırlık Exercise 13: Doğrusal bir modeli bölümler halinde eğitme

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: 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!