Question

Plot the training and validation accuracy curves over the 10 epochs of training. You can use the history.history attribute which contains information about the training history of the model, including the values of the training and validation metrics at each epoch.

Answer 1

To plot the training and validation accuracy curves, you can use the information stored in the history.history attribute of the model, and use a plotting library like Matplotlib in Python.

Step-by-step explanation:

The training and validation accuracy curves can be plotted using the information stored in the history.history attribute of the model. This attribute contains the values of the training and validation metrics at each epoch. To plot the curves, you can use a plotting library like Matplotlib in Python. Here is an example code snippet:

import matplotlib.pyplot as plt
history = model.fit(x_train, y_train, validation_data=(x_val, y_val), epochs=10)
plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.title('Training and Validation Accuracy')
plt.xlabel('Epochs')
plt.ylabel('Accuracy')
plt.legend(['Training', 'Validation'])
plt.show()

This code will create a line plot showing the training and validation accuracy values at each epoch.