Melatih model linear secara batch

Dalam latihan ini, kita akan melatih model regresi linear secara batch, melanjutkan dari latihan sebelumnya. Kita akan melangkah melalui himpunan data per batch dan memperbarui variabel model, yaitu intercept dan slope, setelah setiap langkah. Pendekatan ini memungkinkan kita melatih dengan himpunan data yang terlalu besar untuk dimuat seluruhnya di memori.

Perhatikan bahwa fungsi loss, loss_function(intercept, slope, targets, features), telah disiapkan untuk Anda. Selain itu, keras sudah diimpor dan numpy tersedia sebagai np. Variabel yang dapat dilatih harus dimasukkan ke dalam var_list sesuai urutan kemunculannya sebagai argumen fungsi loss.

Latihan ini adalah bagian dari kursus

Pendahuluan TensorFlow di Python

Petunjuk latihan

Gunakan pengoptimal .Adam().
Muat data dari 'kc_house_data.csv' secara batch dengan chunksize 100.
Ekstrak kolom price dari batch, ubah menjadi array numpy bertipe float 32-bit, lalu tetapkan ke price_batch.
Lengkapi fungsi loss, isi daftar variabel yang dapat dilatih, dan lakukan minimization.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Initialize Adam optimizer
opt = keras.optimizers.____

# Load data in batches
for batch in pd.read_csv('____', ____=____):
	size_batch = np.array(batch['sqft_lot'], np.float32)

	# Extract the price values for the current batch
	price_batch = np.array(batch['____'], np.____)

	# Complete the loss, fill in the variable list, and minimize
	opt.minimize(lambda: loss_function(____, slope, price_batch, size_batch), var_list=[intercept, ____])

# Print trained parameters
print(intercept.numpy(), slope.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 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

Latihan Saat Ini

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!