Hi Thomas,
it is possible to obtain the activation values of any hidden layer, but the
procedure is not completely straight forward. If you look at the code of
the `_predict` method of MLPs you can see the following:
```python
def _predict(self, X):
"""Predict using the trained model
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
The input data.
Returns
-------
y_pred : array-like, shape (n_samples,) or (n_samples, n_outputs)
The decision function of the samples for each class in the
model.
"""
X = check_array(X, accept_sparse=['csr', 'csc', 'coo'])
# Make sure self.hidden_layer_sizes is a list
hidden_layer_sizes = self.hidden_layer_sizes
if not hasattr(hidden_layer_sizes, "__iter__"):
hidden_layer_sizes = [hidden_layer_sizes]
hidden_layer_sizes = list(hidden_layer_sizes)
layer_units = [X.shape[1]] + hidden_layer_sizes + \
[self.n_outputs_]
# Initialize layers
activations = [X]
for i in range(self.n_layers_ - 1):
activations.append(np.empty((X.shape[0],
layer_units[i + 1])))
# forward propagate
self._forward_pass(activations)
y_pred = activations[-1]
return y_pred
```
the line `y_pred = activations[-1]` is responsible for extracting the
values for the last layer,
but the `activations` variable contains the values for all the neurons.
You can make this function into your own external method (changing the
`self` attribute by
a proper parameter) and add an extra argument which specifies the layer(s)
that you want.
I have done this myself in order to make an AutoEncoderNetwork out of the
MLP
implementation.
This makes me wonder, would it be worth adding this to sklearn?
A very simple way would be to refactor the `_predict` method, with the
additional layer
argument, to a new method `_predict_layer`, then we can have the `_predict`
method
simply call `_predict_layer(..., layer=-1)` and add a new method (perhaps a
`transform`?)
that allows to get (raveled) values for an arbitrary subset of the layers.
I'd be happy to submit a PR if you guys think it would be interesting for
the project.
Javier
On Thu, Dec 7, 2017 at 12:51 AM Thomas Evangelidis <teva...@gmail.com>
wrote:
> Greetings,
>
> I want to train a MLPClassifier with one hidden layer and use it as a
> feature selector for an MLPRegressor.
> Is it possible to get the values of the neurons from the last hidden layer
> of the MLPClassifier to pass them as input to the MLPRegressor?
>
> If it is not possible with scikit-learn, is anyone aware of any
> scikit-compatible NN library that offers this functionality? For example
> this one:
>
> http://scikit-neuralnetwork.readthedocs.io/en/latest/index.html
>
> I wouldn't like to do this in Tensorflow because the MLP there is much
> slower than scikit-learn's implementation.
>
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