Doğrusal regresyonu kur

Tek değişkenli bir doğrusal regresyon, tek bir özellik ile hedef tensör arasındaki ilişkiyi bulur. Bu egzersizde, bir mülkün arsa büyüklüğü ve fiyatını kullanacağız. Videoda konuştuğumuz gibi, her iki tensörün de doğal logaritmasını alacağız; bunlar price_log ve size_log olarak mevcut.

Bu egzersizde modeli ve kayıp fonksiyonunu tanımlayacaksın. Ardından intercept ve slope için iki farklı değer üzerinde kayıp fonksiyonunu değerlendireceksin. Tahmin edilen değerlerin intercept + features*slope ile verildiğini unutma. Ayrıca, senin için keras.losses.mse() fonksiyonunun mevcut olduğunu not et. Ek olarak, slope ve intercept değişken olarak tanımlanmıştır.

Bu egzersiz

Python ile TensorFlow’a Giriş

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

intercept, features ve slope kullanarak, add() veya multiply() kullanmadan, doğrusal regresyon için tahmin değerlerini döndüren bir fonksiyon tanımla.
Modelin değişkenleri olan intercept ve slopeu argüman olarak ekleyerek loss_function() fonksiyonunu tamamla.
targets ve predictions kullanarak ortalama kare hatayı hesapla.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Define a linear regression model
def linear_regression(intercept, slope, features = size_log):
	return ____

# Set loss_function() to take the variables as arguments
def loss_function(____, ____, features = size_log, targets = price_log):
	# Set the predicted values
	predictions = linear_regression(intercept, slope, features)
    
    # Return the mean squared error loss
	return keras.losses.____

# Compute the loss for different slope and intercept values
print(loss_function(0.1, 0.1).numpy())
print(loss_function(0.1, 0.5).numpy())

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 Exercise 4: Kayıp fonksiyonları Exercise 5: TensorFlow'da kayıp fonksiyonları Exercise 6: Kayıp fonksiyonunu değiştirme Exercise 7: Doğrusal regresyon Exercise 8: Doğrusal regresyonu kur

Geçerli Egzersiz

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!