Menghindari local minima

Soal sebelumnya menunjukkan betapa mudahnya terjebak pada local minima. Kita memiliki masalah optimasi sederhana dengan satu variabel, dan gradient descent tetap gagal menemukan global minimum ketika harus melewati local minima terlebih dahulu. Salah satu cara untuk menghindari masalah ini adalah menggunakan momentum, yang memungkinkan optimizer menembus local minima. Kita akan kembali menggunakan fungsi loss dari soal sebelumnya, yang telah didefinisikan dan tersedia untuk Anda sebagai loss_function().

Grafik fungsi satu variabel yang memiliki beberapa local minima dan satu global minimum.

Beberapa optimizer di tensorflow memiliki parameter momentum, termasuk SGD dan RMSprop. Anda akan menggunakan RMSprop pada latihan ini. Perhatikan bahwa x_1 dan x_2 kali ini diinisialisasi dengan nilai yang sama. Selain itu, keras.optimizers.RMSprop() juga telah diimpor untuk Anda dari tensorflow.

Latihan ini adalah bagian dari kursus

Pendahuluan TensorFlow di Python

Petunjuk latihan

Atur operasi opt_1 untuk menggunakan learning rate 0.01 dan momentum 0.99.
Atur opt_2 untuk menggunakan optimizer root mean square propagation (RMS) dengan learning rate 0.01 dan momentum 0.00.
Definisikan operasi minimization untuk opt_2.
Cetak x_1 dan x_2 sebagai array numpy.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Initialize x_1 and x_2
x_1 = Variable(0.05,float32)
x_2 = Variable(0.05,float32)

# Define the optimization operation for opt_1 and opt_2
opt_1 = keras.optimizers.RMSprop(learning_rate=____, momentum=____)
opt_2 = ____

for j in range(100):
	opt_1.minimize(lambda: loss_function(x_1), var_list=[x_1])
    # Define the minimization operation for opt_2
	____

# Print x_1 and x_2 as numpy arrays
print(____, ____)

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: 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: Lapisan dense Exercise 2: Aljabar linear pada dense layer Exercise 3: Pendekatan level rendah dengan beberapa contoh Exercise 4: Menggunakan operasi dense layer Exercise 5: Fungsi aktivasi Exercise 6: Masalah klasifikasi biner Exercise 7: Masalah klasifikasi multiclass Exercise 8: Optimizer Exercise 9: Bahaya minimum lokal Exercise 10: Menghindari local minima

Latihan Saat Ini

Exercise 11: Melatih jaringan di TensorFlow Exercise 12: Inisialisasi di TensorFlow Exercise 13: Mendefinisikan model dan fungsi loss Exercise 14: Melatih neural network dengan 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!