Membuat prediksi dengan perkalian matriks

Pada bab-bab berikutnya, Anda akan mempelajari cara melatih model regresi linear. Proses ini akan menghasilkan sebuah vektor parameter yang dapat dikalikan dengan data masukan untuk menghasilkan prediksi. Pada latihan ini, Anda akan menggunakan data masukan, features, dan vektor target, bill, yang diambil dari himpunan data kartu kredit yang akan kita gunakan di bagian kursus berikutnya.

\(features = \begin{bmatrix} 2 & 24 \\ 2 & 26 \\ 2 & 57 \\ 1 & 37 \end{bmatrix}\), \(bill = \begin{bmatrix} 3913 \\ 2682 \\ 8617 \\ 64400 \end{bmatrix}\), \(params = \begin{bmatrix} 1000 \\ 150 \end{bmatrix}\)

Matriks data masukan, features, memiliki dua kolom: tingkat pendidikan dan usia. Vektor target, bill, adalah besaran tagihan peminjam kartu kredit.

Karena kita belum melatih model, Anda akan memasukkan tebakan untuk nilai vektor parameter, params. Anda kemudian akan menggunakan matmul() untuk melakukan perkalian matriks antara features dan params guna menghasilkan prediksi, billpred, yang akan Anda bandingkan dengan bill. Perhatikan bahwa kita telah mengimpor matmul() dan constant().

Latihan ini adalah bagian dari kursus

Pendahuluan TensorFlow di Python

Petunjuk latihan

Definisikan features, params, dan bill sebagai konstanta.
Hitung vektor nilai prediksi, billpred, dengan mengalikan data masukan, features, dengan parameter, params. Gunakan perkalian matriks, bukan produk elemen-per-elemen.
Definisikan error sebagai target, bill, dikurangi nilai prediksi, billpred.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Define features, params, and bill as constants
features = ____([[2, 24], [2, 26], [2, 57], [1, 37]])
params = ____([[1000], [150]])
bill = ____([[3913], [2682], [8617], [64400]])

# Compute billpred using features and params
billpred = ____

# Compute and print the error
error = ____ - ____
print(error.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: Konstanta dan variabel Exercise 2: Mendefinisikan data sebagai konstanta Exercise 3: Mendefinisikan variabel Exercise 4: Operasi dasar Exercise 5: Melakukan perkalian elemen-per-elemen Exercise 6: Membuat prediksi dengan perkalian matriks

Latihan Saat Ini

Exercise 7: Menjumlahkan berdasarkan dimensi tensor Exercise 8: Operasi lanjutan Exercise 9: Mengubah bentuk tensor Exercise 10: Optimasi dengan gradien Exercise 11: Bekerja dengan data citra

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: 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!