Bir model belirtme

Bir meteorun yörüngesini tahmin etmek için basit bir regresyon modeli kuracaksın!

Eğitim verilerin, çarpışma bölgesinden -10 dakika önceden +10 dakika sonrasına kadar olan zaman adımlarında alınan ölçümlerden oluşuyor. Her zaman adımı, grafiğimizde bir X koordinatı olarak düşünülebilir ve o anda meteor yörüngesinin buna karşılık gelen Y konumu vardır.

Bu problemi, sinir ağlarını kullanarak bir ikinci dereceden fonksiyonu yaklaştırma olarak görebileceğini unutma.

Bu veriler iki numpy dizisinde saklanır: özellikler dediğimiz time_steps ve etiketler dediğimiz y_positions. Hadi modelini kur! Gelecek zaman adımlarında meteor yörüngesinin y konumlarını tahmin edebilmeli.

Kullanman için Keras Sequential modeli ve Dense katmanları hazır.

Bu egzersiz

Keras ile Deep Learning'e Giriş

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

Bir Sequential model başlat.
Girdi şekli 1 nöron olan, 50 nöronlu bir Dense katman ekle.
Her biri 50 nöronlu ve 'relu' aktivasyonlu iki Dense katman ekle.
Modelini, tek nöronlu ve aktivasyonsuz bir Dense katmanla bitir.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Instantiate a Sequential model
model = ____

# Add a Dense layer with 50 neurons and an input of 1 neuron
model.add(____(____, input_shape=(____,), activation='relu'))

# Add two Dense layers with 50 neurons and relu activation
model.add(____(____,____=____))
model.____

# End your model with a Dense layer and no activation
model.____

Kodu Düzenle ve Çalıştır

Bu egzersiz

Keras ile Deep Learning'e Giriş

kursunun bir parçasıdır

IntermediárioNível de habilidade

4.8+

Kursa Ücretsiz Başlayın

In this first chapter, you will get introduced to neural networks, understand what kind of problems they can solve, and when to use them. You will also build several networks and save the earth by training a regression model that approximates the orbit of a meteor that is approaching us!

Exercise 1: Keras nedir?Exercise 2: Keras'ı tanımlama Exercise 3: Deep learning kullanır mıydın?Exercise 4: İlk sinir ağın Exercise 5: Merhaba ağlar!Exercise 6: Parametreleri sayma Exercise 7: Aynısını inşa et!Exercise 8: Bir meteor çarpmasından sağ çıkmak Exercise 9: Bir model belirtme

Geçerli Egzersiz

Exercise 10: Eğitim Exercise 11: Yörüngeyi tahmin et!

By the end of this chapter, you will know how to solve binary, multi-class, and multi-label problems with neural networks. All of this by solving problems like detecting fake dollar bills, deciding who threw which dart at a board, and building an intelligent system to water your farm. You will also be able to plot model training metrics and to stop training and save your models when they no longer improve.

Exercise 1: Binary classification Exercise 2: Exploring dollar bills Exercise 3: A binary classification model Exercise 4: Is this dollar bill fake ?Exercise 5: Multi-class classification Exercise 6: A multi-class model Exercise 7: Prepare your dataset Exercise 8: Training on dart throwers Exercise 9: Softmax predictions Exercise 10: Multi-label classification Exercise 11: An irrigation machine Exercise 12: Training with multiple labels Exercise 13: Keras callbacks Exercise 14: The history callback Exercise 15: Early stopping your model Exercise 16: A combination of callbacks

In the previous chapters, you've trained a lot of models! You will now learn how to interpret learning curves to understand your models as they train. You will also visualize the effects of activation functions, batch-sizes, and batch-normalization. Finally, you will learn how to perform automatic hyperparameter optimization to your Keras models using sklearn.

Exercise 1: Learning curves Exercise 2: Learning the digits Exercise 3: Is the model overfitting?Exercise 4: Do we need more data?Exercise 5: Activation functions Exercise 6: Different activation functions Exercise 7: Comparing activation functions Exercise 8: Comparing activation functions II Exercise 9: Batch size and batch normalization Exercise 10: Changing batch sizes Exercise 11: Batch normalizing a familiar model Exercise 12: Batch normalization effects Exercise 13: Hyperparameter tuning Exercise 14: Preparing a model for tuning Exercise 15: Tuning the model parameters Exercise 16: Training with cross-validation

It's time to get introduced to more advanced architectures! You will create an autoencoder to reconstruct noisy images, visualize convolutional neural network activations, use deep pre-trained models to classify images and learn more about recurrent neural networks and working with text as you build a network that predicts the next word in a sentence.

Exercise 1: Tensors, layers, and autoencoders Exercise 2: It's a flow of tensors Exercise 3: Neural separation Exercise 4: Building an autoencoder Exercise 5: De-noising like an autoencoder Exercise 6: Intro to CNNs Exercise 7: Building a CNN model Exercise 8: Looking at convolutions Exercise 9: Preparing your input image Exercise 10: Using a real world model Exercise 11: Intro to LSTMs Exercise 12: Text prediction with LSTMs Exercise 13: Build your LSTM model Exercise 14: Decode your predictions Exercise 15: Test your model!Exercise 16: You're done!