Deteksi overfitting

Dalam latihan ini, kita akan bekerja dengan subset kecil dari contoh pada himpunan data huruf bahasa isyarat asli. Sampel yang kecil, ditambah model dengan banyak parameter, umumnya akan menyebabkan overfitting. Artinya, model Anda hanya akan menghafal kelas dari tiap contoh, alih-alih mengenali fitur yang dapat digeneralisasi ke banyak contoh.

Anda akan mendeteksi overfitting dengan memeriksa apakah loss pada sampel validasi jauh lebih tinggi daripada loss pada sampel pelatihan dan apakah nilainya meningkat saat pelatihan dilanjutkan. Dengan sampel kecil dan laju pembelajaran yang tinggi, model akan kesulitan berkonvergensi pada optimum. Anda akan menetapkan laju pembelajaran rendah untuk optimizer, sehingga lebih mudah mengidentifikasi overfitting.

Perhatikan bahwa keras telah diimpor dari tensorflow.

Latihan ini adalah bagian dari kursus

Pendahuluan TensorFlow di Python

Petunjuk latihan

Definisikan model sequential di keras bernama model.
Tambahkan layer dense pertama dengan 1024 node, aktivasi relu, dan bentuk masukan (784,).
Atur laju pembelajaran ke 0,001.
Atur operasi fit() untuk melakukan iterasi atas seluruh sampel sebanyak 50 kali dan menggunakan 50% sampel untuk tujuan validasi.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Define sequential model
____

# Define the first layer
____

# Add activation function to classifier
model.add(keras.layers.Dense(4, activation='softmax'))

# Finish the model compilation
model.compile(optimizer=keras.optimizers.Adam(lr=____), 
              loss='categorical_crossentropy', metrics=['accuracy'])

# Complete the model fit operation
model.fit(sign_language_features, sign_language_labels, epochs=____, validation_split=____)

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: 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: Mendefinisikan jaringan neural dengan Keras Exercise 2: Model sequential di Keras Exercise 3: Mengompilasi model sequential Exercise 4: Mendefinisikan model dengan banyak masukan Exercise 5: Pelatihan dan validasi dengan Keras Exercise 6: Pelatihan dengan Keras Exercise 7: Metrik dan validasi dengan Keras Exercise 8: Deteksi overfitting

Latihan Saat Ini

Exercise 9: Mengevaluasi model Exercise 10: Melatih model dengan Estimators API Exercise 11: Mempersiapkan pelatihan dengan Estimators Exercise 12: Mendefinisikan Estimator Exercise 13: Selamat!