.. note::
    :class: sphx-glr-download-link-note

    Click :ref:`here <sphx_glr_download_auto_examples_neural_networks_plot_mnist_filters.py>` to download the full example code
.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_neural_networks_plot_mnist_filters.py:


=====================================
Visualization of MLP weights on MNIST
=====================================

Sometimes looking at the learned coefficients of a neural network can provide
insight into the learning behavior. For example if weights look unstructured,
maybe some were not used at all, or if very large coefficients exist, maybe
regularization was too low or the learning rate too high.

This example shows how to plot some of the first layer weights in a
MLPClassifier trained on the MNIST dataset.

The input data consists of 28x28 pixel handwritten digits, leading to 784
features in the dataset. Therefore the first layer weight matrix have the shape
(784, hidden_layer_sizes[0]).  We can therefore visualize a single column of
the weight matrix as a 28x28 pixel image.

To make the example run faster, we use very few hidden units, and train only
for a very short time. Training longer would result in weights with a much
smoother spatial appearance.




.. code-block:: pytb

    Traceback (most recent call last):
      File "/build/scikit-learn-qZGLk4/scikit-learn-0.20.2+dfsg/examples/neural_networks/plot_mnist_filters.py", line 30, in <module>
        X, y = fetch_openml('mnist_784', version=1, return_X_y=True)
      File "/build/scikit-learn-qZGLk4/scikit-learn-0.20.2+dfsg/.pybuild/cpython3_3.7/build/sklearn/datasets/openml.py", line 526, in fetch_openml
        data_info = _get_data_info_by_name(name, version, data_home)
      File "/build/scikit-learn-qZGLk4/scikit-learn-0.20.2+dfsg/.pybuild/cpython3_3.7/build/sklearn/datasets/openml.py", line 314, in _get_data_info_by_name
        data_home)
      File "/build/scikit-learn-qZGLk4/scikit-learn-0.20.2+dfsg/.pybuild/cpython3_3.7/build/sklearn/datasets/openml.py", line 164, in _get_json_content_from_openml_api
        return _load_json()
      File "/build/scikit-learn-qZGLk4/scikit-learn-0.20.2+dfsg/.pybuild/cpython3_3.7/build/sklearn/datasets/openml.py", line 62, in wrapper
        return f()
      File "/build/scikit-learn-qZGLk4/scikit-learn-0.20.2+dfsg/.pybuild/cpython3_3.7/build/sklearn/datasets/openml.py", line 160, in _load_json
        with closing(_open_openml_url(url, data_home)) as response:
      File "/build/scikit-learn-qZGLk4/scikit-learn-0.20.2+dfsg/.pybuild/cpython3_3.7/build/sklearn/datasets/openml.py", line 109, in _open_openml_url
        with closing(urlopen(req)) as fsrc:
      File "/usr/lib/python3.7/urllib/request.py", line 222, in urlopen
        return opener.open(url, data, timeout)
      File "/usr/lib/python3.7/urllib/request.py", line 525, in open
        response = self._open(req, data)
      File "/usr/lib/python3.7/urllib/request.py", line 543, in _open
        '_open', req)
      File "/usr/lib/python3.7/urllib/request.py", line 503, in _call_chain
        result = func(*args)
      File "/usr/lib/python3.7/urllib/request.py", line 1360, in https_open
        context=self._context, check_hostname=self._check_hostname)
      File "/usr/lib/python3.7/urllib/request.py", line 1319, in do_open
        raise URLError(err)
    urllib.error.URLError: <urlopen error [Errno 111] Connection refused>





.. code-block:: python

    import matplotlib.pyplot as plt
    from sklearn.datasets import fetch_openml
    from sklearn.neural_network import MLPClassifier

    print(__doc__)

    # Load data from https://www.openml.org/d/554
    X, y = fetch_openml('mnist_784', version=1, return_X_y=True)
    X = X / 255.

    # rescale the data, use the traditional train/test split
    X_train, X_test = X[:60000], X[60000:]
    y_train, y_test = y[:60000], y[60000:]

    # mlp = MLPClassifier(hidden_layer_sizes=(100, 100), max_iter=400, alpha=1e-4,
    #                     solver='sgd', verbose=10, tol=1e-4, random_state=1)
    mlp = MLPClassifier(hidden_layer_sizes=(50,), max_iter=10, alpha=1e-4,
                        solver='sgd', verbose=10, tol=1e-4, random_state=1,
                        learning_rate_init=.1)

    mlp.fit(X_train, y_train)
    print("Training set score: %f" % mlp.score(X_train, y_train))
    print("Test set score: %f" % mlp.score(X_test, y_test))

    fig, axes = plt.subplots(4, 4)
    # use global min / max to ensure all weights are shown on the same scale
    vmin, vmax = mlp.coefs_[0].min(), mlp.coefs_[0].max()
    for coef, ax in zip(mlp.coefs_[0].T, axes.ravel()):
        ax.matshow(coef.reshape(28, 28), cmap=plt.cm.gray, vmin=.5 * vmin,
                   vmax=.5 * vmax)
        ax.set_xticks(())
        ax.set_yticks(())

    plt.show()

**Total running time of the script:** ( 0 minutes  0.000 seconds)


.. _sphx_glr_download_auto_examples_neural_networks_plot_mnist_filters.py:


.. only :: html

 .. container:: sphx-glr-footer
    :class: sphx-glr-footer-example



  .. container:: sphx-glr-download

     :download:`Download Python source code: plot_mnist_filters.py <plot_mnist_filters.py>`



  .. container:: sphx-glr-download

     :download:`Download Jupyter notebook: plot_mnist_filters.ipynb <plot_mnist_filters.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.readthedocs.io>`_
