.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/neural_networks/plot_mnist_filters.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. 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 or to run this example in your browser via Binder

.. 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. The example will throw a warning because it
doesn't converge, in this case this is what we want because of CI's time
constraints.

.. GENERATED FROM PYTHON SOURCE LINES 25-67



.. image:: /auto_examples/neural_networks/images/sphx_glr_plot_mnist_filters_001.png
    :alt: plot mnist filters
    :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none


    Iteration 1, loss = 0.32009978
    Iteration 2, loss = 0.15347534
    Iteration 3, loss = 0.11544755
    Iteration 4, loss = 0.09279764
    Iteration 5, loss = 0.07889367
    Iteration 6, loss = 0.07170497
    Iteration 7, loss = 0.06282111
    Iteration 8, loss = 0.05530788
    Iteration 9, loss = 0.04960484
    Iteration 10, loss = 0.04645355
    Training set score: 0.986800
    Test set score: 0.970000






|

.. code-block:: default


    import warnings

    import matplotlib.pyplot as plt
    from sklearn.datasets import fetch_openml
    from sklearn.exceptions import ConvergenceWarning
    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=(50,), max_iter=10, alpha=1e-4,
                        solver='sgd', verbose=10, random_state=1,
                        learning_rate_init=.1)

    # this example won't converge because of CI's time constraints, so we catch the
    # warning and are ignore it here
    with warnings.catch_warnings():
        warnings.filterwarnings("ignore", category=ConvergenceWarning,
                                module="sklearn")
        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()


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 1 minutes  10.754 seconds)


.. _sphx_glr_download_auto_examples_neural_networks_plot_mnist_filters.py:


.. only :: html

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


  .. container:: binder-badge

    .. image:: images/binder_badge_logo.svg
      :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/0.24.X?urlpath=lab/tree/notebooks/auto_examples/neural_networks/plot_mnist_filters.ipynb
      :alt: Launch binder
      :width: 150 px


  .. container:: sphx-glr-download sphx-glr-download-python

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



  .. container:: sphx-glr-download sphx-glr-download-jupyter

     :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.github.io>`_