Menyusun regresi linear

Regresi linear univariat mengidentifikasi hubungan antara satu fitur dan tensor target. Dalam latihan ini, kita akan menggunakan ukuran lahan properti dan harganya. Seperti yang dibahas dalam video, kita akan mengambil logaritma natural dari kedua tensor, yang tersedia sebagai price_log dan size_log.

Pada latihan ini, Anda akan mendefinisikan model dan fungsi loss. Anda kemudian akan mengevaluasi fungsi loss untuk dua nilai intercept dan slope yang berbeda. Ingat bahwa nilai prediksi diberikan oleh intercept + features*slope. Perhatikan juga bahwa keras.losses.mse() tersedia untuk Anda. Selain itu, slope dan intercept telah didefinisikan sebagai variabel.

Latihan ini adalah bagian dari kursus

Pendahuluan TensorFlow di Python

Petunjuk latihan

Definisikan fungsi yang mengembalikan nilai prediksi untuk regresi linear menggunakan intercept, features, dan slope, tanpa menggunakan add() atau multiply().
Lengkapi loss_function() dengan menambahkan variabel model, intercept dan slope, sebagai argumen.
Hitung rata-rata galat kuadrat menggunakan targets dan predictions.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# 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())

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Pendahuluan TensorFlow di Python

SkillTag.level.intermediateSkillTag.label

4.8+

Mulai Kursus Gratis

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: Data masukan Exercise 2: Muat data menggunakan pandas Exercise 3: Menetapkan tipe data Exercise 4: Fungsi kerugian Exercise 5: Fungsi loss di TensorFlow Exercise 6: Memodifikasi fungsi loss Exercise 7: Regresi linear Exercise 8: Menyusun regresi linear

Latihan Saat Ini

Exercise 9: Latih model linear Exercise 10: Regresi linear berganda Exercise 11: Pelatihan batch Exercise 12: Mempersiapkan pelatihan batch Exercise 13: Melatih model linear secara batch

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!