38 lines
1.1 KiB
Markdown
38 lines
1.1 KiB
Markdown
如果想用同時打亂x_train與y_train，可以參考這2個方法。
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## 1. 用 `tf.random.shuffle`
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```python
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indices = tf.range(start=0, limit=tf.shape(x_data)[0], dtype=tf.int32)
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idx = tf.random.shuffle(indices)
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x_data = tf.gather(x_data, idx)
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y_data = tf.gather(y_data, idx)
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```
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先建立一個跟array一樣大的list，然後打亂它，再用這個已打亂的list當作索引來建立一個新的data list。
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## 2. 用 `Dataset.shuffle`
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^832c8c
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```python
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x_train = tf.data.Dataset.from_tensor_slices(x)
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y_train = tf.data.Dataset.from_tensor_slices(y)
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x_train, y_train = x_train.shuffle(buffer_size=2, seed=2), y_train.shuffle(buffer_size=2, seed=2)
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dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train))
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```
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或者
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```python
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BF = 2
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SEED = 2
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def shuffling(dataset, bf, seed_number):
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return dataset.shuffle(buffer_size=bf, seed=seed_number)
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x_train, y_train = shuffling(x_train, BF, SEED), shuffling(y_train, BF, SEED)
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dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train))
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```
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概念跟第一點是一樣的，但是這是先轉成 `tf.data.Dataset`，然後把x_train跟y_train都用同樣的seed打亂。
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