精度图和损失图都包含噪声。可将每个损失和精度都替换为指数移动平均值，从而让曲线变得平滑。

# 使曲线变的平滑
def smooth_curve(points, factor=0.8):
    smoothed_points = []
    for point in points:
        if smoothed_points:
            previous = smoothed_points[-1]
            smoothed_points.append(previous * factor + point * (1 - factor))
        else:
            smoothed_points.append(point)
    return  smoothed_points

plt.plot(epochs, smooth_curve(acc), 'bo', label='Smoothed training acc')
plt.plot(epochs, smooth_curve(val_acc), 'b', label='Smoothed validation acc')
plt.title('Training and validation accuracy')
plt.legend()

plt.figure()

plt.plot(epochs, smooth_curve(loss), 'bo', label='Smoothed training loss')
plt.plot(epochs, smooth_curve(val_loss), 'b', label='Smoothed validation loss')
plt.title('Training and validation loss')
plt.legend()

plt.show()

在测试数据上评估模型

# 在测试数据上评估模型
test_generator = test_datagen.flow_from_directory(
    test_dir,
    target_size=(150, 150),
    batch_size=20,
    class_mode='binary'
)

test_loss, test_acc = model.evaluate_generator(test_generator, steps=50)
print('test acc:', test_acc)