[Python] Retrieve final hidden activation layer output from sklearn's MLPClassifier

I would like to do some tests with neural network final hidden activation layer outputs using sklearn's MLPClassifier after fitting the data.

for example, If I create a classifier, assuming data X_train with labels y_train and two hidden layers of sizes (300,100)

clf = MLPClassifier(hidden_layer_sizes=(300,100))
clf.fit(X_train,y_train)


I would like to be able to call a function somehow to retrieve the final hidden activation layer vector of length 100 for use in additional tests.

Assuming a test set X_test, y_test, normal prediction would be:

preds = clf.predict(X_test)


But, I would like to do something like:

activation_layers_for_all_X_test = clf.get_final_activation_output(X_test)


Functions such as get_weights exist, but that would only help me on a per layer basis. Short of doing the transformation myself, is there another methodology to retrieve these final hidden layer activated outputs for the final hidden layer?

Looking at this diagram as an example:



The output I would like is the Out Layer, i.e. the final activated output from the final hidden layer.

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