Metrieken en validatie met Keras

In de vorige oefening hebben we een model getraind om letters in gebarentaal te voorspellen, maar het is niet duidelijk hoe succesvol dat was. In deze oefening proberen we de interpreteerbaarheid van onze resultaten te verbeteren. Omdat we geen validatiesplit hebben gebruikt, zagen we alleen prestatieverbeteringen binnen de trainingsset; het is echter onduidelijk hoeveel daarvan door overfitting kwam. Daarnaast hebben we geen metriek opgegeven, waardoor we alleen dalingen in de verliesfunctie zagen, die niet eenduidig te interpreteren zijn.

Let op: keras is al voor je geïmporteerd vanuit tensorflow.

Deze oefening maakt deel uit van de cursus

Introductie tot TensorFlow in Python

Cursus bekijken

Oefeninstructies

Stel de eerste dense-laag in op 32 nodes, gebruik een sigmoid-activatiefunctie en een inputvorm van (784,).
Gebruik de root mean square propagation-optimizer, een categorical crossentropy-verlies en de accuracy-metriek.
Stel het aantal epochs in op 10 en gebruik 10% van de gegevensset voor validatie.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Define sequential model
model = keras.Sequential()

# Define the first layer
model.add(keras.layers.Dense(____, ____, ____))

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

# Set the optimizer, loss function, and metrics
model.compile(optimizer='____', loss='____', metrics=['____'])

# Add the number of epochs and the validation split
model.fit(sign_language_features, sign_language_labels, epochs=____, validation_split=____)

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Introductie tot TensorFlow in Python

SkillTag.level.intermediateSkillTag.label

4.8+

Begin de cursus 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: Neurale netwerken definiëren met Keras Exercise 2: Het sequentiële model in Keras Exercise 3: Een sequentieel model compileren Exercise 4: Een model met meerdere invoeren definiëren Exercise 5: Trainen en valideren met Keras Exercise 6: Trainen met Keras Exercise 7: Metrieken en validatie met Keras

Huidige oefening

Exercise 8: Overfitting detecteren Exercise 9: Modellen evalueren Exercise 10: Modellen trainen met de Estimators-API Exercise 11: Voorbereiden op trainen met Estimators Exercise 12: Estimators definiëren Exercise 13: Gefeliciteerd!