listing 3.10 should history key be "accuracy" and "val_accuracy" instead of "acc" and "val_acc"?

[warrenbocphet]

On the book:

acc = history.history['acc']
val_acc = history.history['val_acc']

But if I typed so, it would result in an error where keys of history are incorrect. But when I did history.history.keys(), it showed "accuracy" and "val_accuracy" instead. Is this a mistake on my part?

Here's the full code:

from keras.datasets import reuters
from tensorflow.keras import models
from tensorflow.keras import layers
import numpy as np
import matplotlib.pyplot as plt

def vectorize_sequences(sequences, dimension = 10000):
    results = np.zeros((len(sequences),dimension))
    for i, sequence in enumerate(sequences):
        results[i, sequence] = 1
    return results

def to_one_hot(labels, dimension=46):
    results = np.zeros((len(labels), dimension))
    for i, label in enumerate(labels):
        results[i, label] = 1
    return results

(train_data, train_labels), (test_data, test_labels) = reuters.load_data(num_words=10000)

x_train = vectorize_sequences(train_data)
x_test = vectorize_sequences(test_data)

one_hot_train_label = to_one_hot(train_labels)
one_hot_test_label = to_one_hot(test_labels)

# one_hot_train_label = to_categorical(train_labels)
# one_hot_test_label = to_categorical(test_labels)

model = models.Sequential()
model.add(layers.Dense(64, activation='relu', input_shape=(10000,)))
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(46, activation='softmax'))

model.compile(optimizer='rmsprop',
              loss='categorical_crossentropy',
              metrics=['accuracy'])

x_val = x_train[:1000]
partial_x_train = x_train[1000:]

y_val = one_hot_train_label[:1000]
partial_y_train = one_hot_train_label[1000:]

history = model.fit(partial_x_train,
                    partial_y_train,
                    epochs=20,
                    batch_size=512,
                    validation_data=(x_val, y_val))

print("History key: ", history.history.keys())

loss = history.history['loss']
val_loss = history.history['val_loss']

epochs = range(1, len(loss) + 1)

plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.legend()

plt.show()

plt.clf()   # clear figure

acc = history.history['accuracy']
val_acc = history.history['val_accuracy']

plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.legend()

plt.show()

[warrenbocphet]

Nevermind, I figured it out. It turns out if I set metrics=['accuracy'] then history attribution will use 'accuracy' instead of 'acc'.