Een model uit de echte wereld gebruiken

Oké, Ivy’s foto is klaar voor ResNet50. Hij staat in img_ready en ziet er nu zo uit:

ResNet50 is een model dat is getraind op de Imagenet dataset en kan 1000 verschillende gelabelde objecten onderscheiden. ResNet50 is een diep model met 50 lagen; je kunt het in 3D bekijken hier.

ResNet50 en decode_predictions zijn allebei voor je geïmporteerd uit tensorflow.keras.applications.resnet50.

Tijd om dit getrainde model te gebruiken om Ivy’s ras te achterhalen!

Deze oefening maakt deel uit van de cursus

Introductie tot Deep Learning met Keras

Cursus bekijken

Oefeninstructies

Maak een instantie van een ResNet50-model en zet de parameter weights op 'imagenet'.
Gebruik het model om te voorspellen op je bewerkte afbeelding.
Decodeer de eerste 3 voorspellingen met decode_predictions().

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Instantiate a ResNet50 model with 'imagenet' weights
model = ____(weights=____)

# Predict with ResNet50 on your already processed img
preds = ____.____(____)

# Decode the first 3 predictions
print('Predicted:', ____(____, top=____)[0])

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Introductie tot Deep Learning met Keras

SkillTag.level.intermediateSkillTag.label

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Begin de cursus gratis

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: 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, lagen en auto-encoders Exercise 2: Het is een stroom van tensors Exercise 3: Neurale scheiding Exercise 4: Een autoencoder bouwen Exercise 5: Ontruisen als een autoencoder Exercise 6: Introductie tot CNN's Exercise 7: Een CNN-model bouwen Exercise 8: Kijken naar convoluties Exercise 9: Je invoerafbeelding voorbereiden Exercise 10: Een model uit de echte wereld gebruiken

Huidige oefening

Exercise 11: Introductie tot LSTM's Exercise 12: Tekstvoorspelling met LSTM's Exercise 13: Bouw je LSTM-model Exercise 14: Decodeer je voorspellingen Exercise 15: Test je model!Exercise 16: Je bent klaar!