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

.. only:: html

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

        Click :ref:`here <sphx_glr_download_auto_examples_misc_plot_lstm.py>`
        to download the full example code

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_misc_plot_lstm.py:


Long short-term memory (LSTM) networks
======================================

Hello world

.. GENERATED FROM PYTHON SOURCE LINES 8-15

.. code-block:: default


    import numpy as np
    import matplotlib.pyplot as plt

    from mpl_toolkits.mplot3d import Axes3D
    from tensorflow.keras.models import Sequential
    from tensorflow.keras.layers import LSTM, Bidirectional, Dense, RepeatVector, TimeDistributed







.. GENERATED FROM PYTHON SOURCE LINES 17-24

.. code-block:: default


    num_seqs = 5
    seq_len = 25
    num_features = 1

    seed = 42  # set random seed for reproducibility
    random_state = np.random.RandomState(seed)







.. GENERATED FROM PYTHON SOURCE LINES 25-27

Many-to-many LSTM
-----------------

.. GENERATED FROM PYTHON SOURCE LINES 27-32

.. code-block:: default


    # generate random walks in Euclidean space
    inputs = np.cumsum(random_state.randn(num_seqs, seq_len, num_features), axis=1)
    lstm = LSTM(units=1, return_sequences=True)
    output = lstm(inputs)







.. GENERATED FROM PYTHON SOURCE LINES 33-35

.. code-block:: default


    print(output.shape)




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

 Out:

 .. code-block:: none

    (5, 25, 1)




.. GENERATED FROM PYTHON SOURCE LINES 36-45

.. code-block:: default


    fig, ax = plt.subplots()

    ax.plot(inputs[..., 0].T)

    ax.set_xlabel(r"$t$")
    ax.set_ylabel(r"$x(t)$")

    plt.show()



.. image:: /auto_examples/misc/images/sphx_glr_plot_lstm_001.png
    :alt: plot lstm
    :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 46-55

.. code-block:: default


    fig, ax = plt.subplots()

    ax.plot(output.numpy()[..., 0].T)

    ax.set_xlabel(r"$t$")
    ax.set_ylabel(r"$h(t)$")

    plt.show()



.. image:: /auto_examples/misc/images/sphx_glr_plot_lstm_002.png
    :alt: plot lstm
    :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 56-67

.. code-block:: default


    fig, ax = plt.subplots()

    ax.plot(inputs[..., 0].T,
            output.numpy()[..., 0].T)

    ax.set_xlabel(r"$x(t)$")
    ax.set_ylabel(r"$h(t)$")

    plt.show()




.. image:: /auto_examples/misc/images/sphx_glr_plot_lstm_003.png
    :alt: plot lstm
    :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 68-70

One-to-many LSTM
----------------

.. GENERATED FROM PYTHON SOURCE LINES 70-74

.. code-block:: default

    seq_len = 100
    num_index_points = 128
    xmin, xmax = -10.0, 10.0
    X_grid = np.linspace(xmin, xmax, num_index_points).reshape(-1, num_features)







.. GENERATED FROM PYTHON SOURCE LINES 75-82

.. code-block:: default


    model = Sequential([
        RepeatVector(seq_len, input_shape=(num_features,)),
        Bidirectional(LSTM(units=32, return_sequences=True), merge_mode="concat"),
        TimeDistributed(Dense(1))
    ])
    model.summary()




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

 Out:

 .. code-block:: none

    Model: "sequential_5"
    _________________________________________________________________
    Layer (type)                 Output Shape              Param #   
    =================================================================
    repeat_vector (RepeatVector) (None, 100, 1)            0         
    _________________________________________________________________
    bidirectional (Bidirectional (None, 100, 64)           8704      
    _________________________________________________________________
    time_distributed (TimeDistri (None, 100, 1)            65        
    =================================================================
    Total params: 8,769
    Trainable params: 8,769
    Non-trainable params: 0
    _________________________________________________________________




.. GENERATED FROM PYTHON SOURCE LINES 83-86

.. code-block:: default


    Z = model(X_grid)
    Z




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

 Out:

 .. code-block:: none


    <tf.Tensor: shape=(128, 100, 1), dtype=float32, numpy=
    array([[[-0.18354933],
            [-0.30141646],
            [-0.37728903],
            ...,
            [-0.33238253],
            [-0.30502355],
            [-0.28547618]],

           [[-0.17893629],
            [-0.29478052],
            [-0.36996403],
            ...,
            [-0.32376558],
            [-0.29686114],
            [-0.2786622 ]],

           [[-0.17418236],
            [-0.28793848],
            [-0.3623963 ],
            ...,
            [-0.31536895],
            [-0.28893828],
            [-0.27210516]],

           ...,

           [[-0.6189441 ],
            [-0.6935143 ],
            [-0.72257364],
            ...,
            [-0.6948264 ],
            [-0.63015556],
            [-0.5438851 ]],

           [[-0.61907387],
            [-0.6935504 ],
            [-0.7228407 ],
            ...,
            [-0.6987864 ],
            [-0.63389534],
            [-0.54702204]],

           [[-0.6191223 ],
            [-0.6934863 ],
            [-0.7229916 ],
            ...,
            [-0.70253503],
            [-0.63743615],
            [-0.5499795 ]]], dtype=float32)>



.. GENERATED FROM PYTHON SOURCE LINES 87-101

.. code-block:: default


    T_grid = np.arange(seq_len)

    fig, ax = plt.subplots(subplot_kw=dict(projection="3d"))

    ax.plot_wireframe(T_grid, X_grid, Z.numpy().squeeze(axis=-1), alpha=0.6)
    # ax.plot_surface(T_grid, X_grid, Z.numpy().squeeze(axis=-1),
    #                 edgecolor='k', linewidth=0.5, cmap="Spectral_r")

    ax.set_xlabel(r'$t$')
    ax.set_ylabel(r'$x$')
    ax.set_zlabel(r"$h(x, t)$")

    plt.show()



.. image:: /auto_examples/misc/images/sphx_glr_plot_lstm_004.png
    :alt: plot lstm
    :class: sphx-glr-single-img






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

   **Total running time of the script:** ( 0 minutes  4.398 seconds)


.. _sphx_glr_download_auto_examples_misc_plot_lstm.py:


.. only :: html

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



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

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



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

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


.. only:: html

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

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