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训练图的过程如下:
训练结果:
训练的loss很低,而且正确率已经很高了,但是在验证集上面,loss比较高,而且准确率特别低,还一直不稳定。
产生原因:
将每个类别的数据集中的放在一起,而且数据标签也是很集中的
model.fit(train_data, train_label, batch_size = 32, epochs = 100,
validation_split = 0.2, shuffle = True)
在module的fit函数里面,虽然有 shuffle ,但是感觉不太对啊,所以还是要提前对数据进行打乱。
解决方法:
打乱数据,但是注意,在打乱的时候,一定要连标签一起打乱,保证打乱后的数据和标签是一一对应 的。
np.random.seed(200) np.random.shuffle(train_data) np.random.seed(200)
np.random.shuffle(train_label) np.random.seed(200) np.random.shuffle(test_data)
np.random.seed(200) np.random.shuffle(test_label)
解决结果:
论 shuffle 的重要性!!!