保存没折的验证结果

import numpy as np

k = 4 # 4折
num_val_samples = len(train_data) // 4
num_epochs = 500
all_mae_histories = []
for i in range(k):
    print('processing fold #', i)
    # 准备验证数据，第k个分区的数据
    val_data = train_data[i * num_val_samples: (i + 1) * num_val_samples]
    val_targets = train_targets[i * num_val_samples: (i + 1) * num_val_samples]

    # 准备训练数据：其它所有分区的数据
    partial_train_data = np.concatenate([train_data[:i * num_val_samples],
                                         train_data[(i + 1) * num_val_samples:]],
                                        axis=0)
    partial_train_targets = np.concatenate([train_targets[:i * num_val_samples],
                                            train_targets[
                                            (i + 1) * num_val_samples:]],
                                           axis=0)

    model = build_model() # 构建keras模型（已编译）
    history = model.fit(partial_train_data, partial_train_targets,
                        validation_data=(val_data, val_targets),
                        epochs=num_epochs, batch_size=1, verbose=0)
    mae_history = history.history['val_mean_absolute_error']
    all_mae_histories.append(mae_history)

计算所有轮次中的K折验证分数平均值

average_mae_history = [
    np.mean([x[i] for x in all_mae_histories]) for i in range(num_epochs)
]

绘制验证分数

import matplotlib.pyplot as plt
plt.plot(range(1, len(average_mae_history) + 1), average_mae_history)
plt.xlabel('Epochs')
plt.ylabel('Validation MAE')
plt.show()

每轮的验证MAE.png

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