擅长:python、mysql、java
<p>在使用<code>sklearn.model_selection.train_test_split</code>拟合模型之前,可以手动将数据拆分为训练和测试数据集。然后,分别规范化训练和测试数据,并使用<code>validation_data</code>参数调用<code>model.fit</code>。在</p>
<p><strong>代码示例</strong></p>
<pre><code>import numpy as np
from sklearn.model_selection import train_test_split
data = np.random.randint(0,100,200).reshape(20,10)
target = np.random.randint(0,1,20)
X_train, X_test, y_train, y_test = train_test_split(data, target, test_size=0.2)
X_train = Normalize(X_train)
X_test = Normalize(X_test)
model.fit(X_train, y_train, validation_data=(X_test,y_test), batch_size=15, callbacks=[early_stopping], verbose=1)
</code></pre>