Yes, there are a number of ways to automate the trial-and-error process of finding the optimal parameters of a deep learning network. Some of these methods include:

* **Grid search:** This is a brute-force approach that simply tries all possible combinations of parameters and selects the one that produces the best results.
* **Random search:** This is a more efficient approach that randomly selects combinations of parameters and evaluates them.
* **Bayesian optimization:** This is a more sophisticated approach that uses Bayesian inference to estimate the optimal parameters.

These methods can be used to find the optimal number of layers, nodes, and other parameters for a deep learning network. They can also be used to find the optimal learning rate, batch size, and other hyperparameters.

Using these methods can save a lot of time and effort in the process of training a deep learning network. However, it is important to note that they are not guaranteed to find the optimal parameters. It is still necessary to experiment with different settings and evaluate the results manually.

In addition to these methods, there are a number of other tools and resources that can be used to automate the training of deep learning networks. These include:

* **Hyperparameter tuning libraries:** These libraries provide a number of tools for automating the process of finding the optimal hyperparameters for a deep learning network.
* **Cloud computing services:** These services provide access to powerful computing resources that can be used to train deep learning networks quickly and efficiently.

Using these tools and resources can make it much easier to train deep learning networks. They can help you to save time and effort, and they can help you to get better results.
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