Çoklu doğrusal regresyon

Çoğu durumda, tek değişkenli doğrusal regresyon yapmak, doğru tahminler için işe yarar bir model üretmez. Bu egzersizde, birden fazla özelliğin kullanıldığı çoklu regresyon yapacaksın.

Hedef olarak price_log, özellikler olarak ise size_log ve bedrooms kullanacaksın. Bu tensörlerin her biri tanımlandı ve hazır. Ayrıca, ortalama kare hata kaybı yerine ortalama mutlak hata kaybına geçeceksin: keras.losses.mae(). Son olarak, tahmin edilen değerler şu şekilde hesaplanır: params[0] + feature1*params[1] + feature2*params[2]. Üç ayrı değişken kullanmak yerine, params adlı bir parametre vektörünü değişken olarak tanımladığımıza dikkat et. Burada params[0] kesişim (intercept), params[1] ve params[2] ise eğimlerdir (slope).

Bu egzersiz

Python ile TensorFlow’a Giriş

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

Tahmin değerlerini döndüren bir doğrusal regresyon modeli tanımla.
loss_function() fonksiyonunu parametre vektörünü girdi olarak alacak şekilde ayarla.
Ortalama mutlak hata kaybını kullan.
Eniyileme (minimizasyon) işlemini tamamla.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Define the linear regression model
def linear_regression(params, feature1 = size_log, feature2 = bedrooms):
	return params[0] + feature1*____ + feature2*____

# Define the loss function
def loss_function(____, targets = price_log, feature1 = size_log, feature2 = bedrooms):
	# Set the predicted values
	predictions = linear_regression(params, feature1, feature2)
  
	# Use the mean absolute error loss
	return keras.losses.____(targets, predictions)

# Define the optimize operation
opt = keras.optimizers.Adam()

# Perform minimization and print trainable variables
for j in range(10):
	opt.minimize(lambda: loss_function(____), var_list=[____])
	print_results(params)

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 Exercise 9: Doğrusal bir modeli eğit Exercise 10: Çoklu doğrusal regresyon

Geçerli Egzersiz

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!