Mendefinisikan model dengan banyak masukan

Dalam beberapa kasus, API sequential tidak cukup fleksibel untuk mengakomodasi arsitektur model yang Anda inginkan, sehingga Anda perlu menggunakan API functional. Misalnya, jika Anda ingin melatih dua model dengan arsitektur berbeda secara bersama-sama, Anda perlu menggunakan API functional untuk melakukannya. Pada latihan ini, kita akan melihat caranya. Kita juga akan menggunakan metode .summary() untuk meninjau arsitektur model gabungan.

Perhatikan bahwa keras telah diimpor dari tensorflow untuk Anda. Selain itu, layer masukan dari model pertama dan kedua telah didefinisikan sebagai m1_inputs dan m2_inputs. Perhatikan bahwa kedua model memiliki arsitektur yang sama, tetapi salah satunya menggunakan aktivasi sigmoid pada layer pertama dan yang lainnya menggunakan relu.

Latihan ini adalah bagian dari kursus

Pendahuluan TensorFlow di Python

Petunjuk latihan

Teruskan layer masukan model 1 ke layer pertamanya dan layer pertama model 1 ke layer keduanya.
Teruskan layer masukan model 2 ke layer pertamanya dan layer pertama model 2 ke layer keduanya.
Gunakan operasi add() untuk menggabungkan layer kedua dari model 1 dan model 2.
Lengkapi definisi model functional.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# For model 1, pass the input layer to layer 1 and layer 1 to layer 2
m1_layer1 = keras.layers.Dense(12, activation='sigmoid')(____)
m1_layer2 = keras.layers.Dense(4, activation='softmax')(____)

# For model 2, pass the input layer to layer 1 and layer 1 to layer 2
m2_layer1 = keras.layers.Dense(12, activation='relu')(____)
m2_layer2 = keras.layers.Dense(4, activation='softmax')(____)

# Merge model outputs and define a functional model
merged = keras.layers.add([m1_layer2, ____])
model = keras.Model(inputs=[____, m2_inputs], outputs=____)

# Print a model summary
print(model.summary())

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

Latihan Saat Ini

Exercise 5: Pelatihan dan validasi dengan Keras Exercise 6: Pelatihan dengan Keras Exercise 7: Metrik dan validasi dengan Keras Exercise 8: Deteksi overfitting Exercise 9: Mengevaluasi model Exercise 10: Melatih model dengan Estimators API Exercise 11: Mempersiapkan pelatihan dengan Estimators Exercise 12: Mendefinisikan Estimator Exercise 13: Selamat!