autoencoderModel.fit
时间: 2024-05-03 13:22:49 浏览: 17
The `fit()` method in an autoencoder model is used to train the model by minimizing the loss function. It takes in the input data and the target data (which is the same as the input data in an autoencoder) and iteratively adjusts the weights of the model to minimize the difference between the input and output data.
The syntax for `fit()` method in an autoencoder model is:
```
autoencoderModel.fit(x_train, x_train, epochs=10, batch_size=32, validation_data=(x_test, x_test))
```
Here, `x_train` is the input training data, `x_train` is the target training data (which is the same as the input data in an autoencoder), `epochs` is the number of times to iterate over the entire dataset, `batch_size` is the number of samples per gradient update, and `validation_data` is the data on which to evaluate the loss and any model metrics at the end of each epoch.
During training, the `fit()` method will output the loss and any metrics being tracked for both the training and validation sets. Once training is complete, the autoencoder model will be able to encode and decode data with lower dimensionality while preserving important features.
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