self growth neural network in python

To build a self-growing neural network, we can use a technique called neural architecture search (NAS) which automatically searches for the optimal architecture of a neural network. There are different approaches to NAS, and one popular method is using reinforcement learning.

Here is an example code using the AutoKeras library, which implements NAS using reinforcement learning:

main.py
# install AutoKeras
!pip install autokeras

# load a dataset
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split

digits = load_digits()
x_train, x_test, y_train, y_test = train_test_split(digits.data, digits.target, test_size=0.2)

# use AutoKeras to search for the best neural network architecture
import autokeras as ak

clf = ak.StructuredDataClassifier(max_trials=10)  # search for 10 different architectures
clf.fit(x_train, y_train)

# evaluate the best neural network found by AutoKeras
score = clf.evaluate(x_test, y_test)
print(score)
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In this example code, we first load a dataset and split it into training and testing data. Then we create an instance of StructuredDataClassifier from AutoKeras with max_trials=10, which means we will search for 10 different architectures. We fit the classifier on the training data, and AutoKeras will automatically search for the best neural network architecture using reinforcement learning.

Finally, we evaluate the best neural network found on the testing data using the evaluate method of the classifier. The score variable will contain the evaluation result (e.g. accuracy).

Using AutoKeras, we can easily build a self-growing neural network without manually designing the architecture. We only need to specify the type of problem (e.g. classification, regression, etc.) and the input/output format, and AutoKeras will do the rest.