History geri çağırımı

History geri çağırımı, .fit() metoduyla her model eğittiğinde varsayılan olarak döndürülür. Bu metriklere erişmek için dönen h_callback nesnesinin içindeki history sözlüğü parametresini ilgili anahtarlarla kullanabilirsin.

Önceki derste kurduğun sulama makinesi modeli, özellikleri ve etiketleriyle birlikte senin için yüklendi: X_train, y_train, X_test, y_test. Bu kez modelin history geri çağırımını saklayacak ve eğitirken validation_data parametresini kullanacaksın.

history içinde saklanan sonuçları iki basit matplotlib fonksiyonuyla çizeceksin: plot_accuracy() ve plot_loss(). Kodlarına konsolda show_code(plot_loss) yazarak bakabilirsin.

Haydi eğitimin perde arkasına bakalım!

Bu egzersiz

Keras ile Deep Learning'e Giriş

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

Modelini X_train ve y_train üzerinde eğit, her dönemi X_test ve y_test üzerinde doğrula.
h_callback içinden loss ve val_loss değerlerini çıkarıp plot_loss kullan.
h_callback içinden accuracy ve val_accuracy değerlerini çıkarıp plot_accuracy kullan.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Train your model and save its history
h_callback = ____.____(____, ____, epochs = 25,
               validation_data=(____, ____))

# Plot train vs test loss during training
plot_loss(h_callback.history[____], h_callback.history[____])

# Plot train vs test accuracy during training
plot_accuracy(h_callback.history[____], h_callback.history[____])

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: What is Keras?Exercise 2: Describing Keras Exercise 3: Would you use deep learning?Exercise 4: Your first neural network Exercise 5: Hello nets!Exercise 6: Counting parameters Exercise 7: Build as shown!Exercise 8: Surviving a meteor strike Exercise 9: Specifying a model Exercise 10: Training Exercise 11: Predicting the orbit!

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: İkili sınıflandırma Exercise 2: Dolar banknotlarını keşfet Exercise 3: İkili sınıflandırma modeli Exercise 4: Bu dolar sahte mi?Exercise 5: Çok sınıflı sınıflandırma Exercise 6: Çok sınıflı bir model Exercise 7: Veri kümeni hazırlama Exercise 8: Dart atanları eğitme Exercise 9: Softmax tahminleri Exercise 10: Çok etiketli sınıflandırma Exercise 11: Bir sulama makinesi Exercise 12: Birden çok etiketle eğitim Exercise 13: Keras geri çağırımları Exercise 14: History geri çağırımı

Geçerli Egzersiz

Exercise 15: Modelini erken durdurma Exercise 16: Callback'lerin bir kombinasyonu

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!