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

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_auto_examples_DeepLearning_TimeSeriesAnalysis.py>`
        to download the full example code.

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

.. _sphx_glr_auto_examples_DeepLearning_TimeSeriesAnalysis.py:


Recurrent networks for time series analysis
============================================

This script implements a model to predict values of a simple sine function. It uses recurrent layers 
to handle the sequential nature of the sine function.

.. GENERATED FROM PYTHON SOURCE LINES 8-51



.. image-sg:: /auto_examples/DeepLearning/images/sphx_glr_TimeSeriesAnalysis_001.png
   :alt: TimeSeriesAnalysis
   :srcset: /auto_examples/DeepLearning/images/sphx_glr_TimeSeriesAnalysis_001.png
   :class: sphx-glr-single-img


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

 .. code-block:: none

    torch.Size([280, 20, 1]) torch.Size([280, 1])
    Model summary:
    Input - Output: ((20, 1))
    RNN - (Input, Output): ((20, 1), 1) - Parameters: 131
        Tanh - Output: (1)
    Total number of parameters: 131
    Epoch: 1 - Metrics: {'loss': '0.0127'}
    Epoch: 2 - Metrics: {'loss': '0.0057'}
    Epoch: 3 - Metrics: {'loss': '0.0051'}
    Epoch: 4 - Metrics: {'loss': '0.0020'}
    Epoch: 5 - Metrics: {'loss': '0.0009'}
    Epoch: 6 - Metrics: {'loss': '0.0007'}
    Epoch: 7 - Metrics: {'loss': '0.0020'}
    Epoch: 8 - Metrics: {'loss': '0.0007'}
    Epoch: 9 - Metrics: {'loss': '0.0006'}
    Epoch: 10 - Metrics: {'loss': '0.0010'}
    Epoch: 11 - Metrics: {'loss': '0.0016'}
    Epoch: 12 - Metrics: {'loss': '0.0013'}
    Epoch: 13 - Metrics: {'loss': '0.0009'}
    Epoch: 14 - Metrics: {'loss': '0.0005'}
    Epoch: 15 - Metrics: {'loss': '0.0007'}
    Epoch: 16 - Metrics: {'loss': '0.0004'}
    Epoch: 17 - Metrics: {'loss': '0.0010'}
    Epoch: 18 - Metrics: {'loss': '0.0003'}
    Epoch: 19 - Metrics: {'loss': '0.0005'}
    Epoch: 20 - Metrics: {'loss': '0.0009'}






|

.. code-block:: Python

    import torch
    import matplotlib.pyplot as plt

    from DLL.DeepLearning.Layers import RNN, LSTM
    from DLL.DeepLearning.Model import Model
    from DLL.DeepLearning.Layers.Activations import Tanh
    from DLL.DeepLearning.Optimisers import ADAM
    from DLL.DeepLearning.Losses import MSE


    device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")

    size = 300
    sequence_length = 20
    data = torch.sin(torch.arange(size, dtype=torch.float32))
    x = []
    y = []
    for start in range(size - sequence_length):
        x.append(data[start:start + sequence_length])
        y.append(data[start + sequence_length])
    x = torch.stack(x).reshape(len(x), sequence_length, 1).to(device=device)
    y = torch.stack(y).reshape(len(y), 1).to(device=device)
    print(x.shape, y.shape)

    model = Model((sequence_length, 1), device=device)
    model.add(RNN((1,), 10, activation=Tanh()))
    model.compile(optimiser=ADAM(), loss=MSE(), metrics=["loss", "val_loss"])
    model.summary()

    model.fit(x, y, epochs=20, batch_size=1, verbose=True)

    data = torch.sin(torch.arange(size, dtype=torch.float32, device=device))
    prediction = []
    y_true = []
    for start in range(30):
        prediction.append(model.predict(data[start:start + sequence_length].reshape(1, sequence_length, 1)))
        y_true.append(data[start + sequence_length])

    plt.figure(figsize=(8, 8))
    plt.plot(torch.stack(y_true).cpu().numpy(), label="Sin(x)")
    plt.plot(torch.stack(prediction).cpu().flatten().numpy(), label="Prediction")
    plt.legend()
    plt.show()


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

   **Total running time of the script:** (0 minutes 9.473 seconds)


.. _sphx_glr_download_auto_examples_DeepLearning_TimeSeriesAnalysis.py:

.. only:: html

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

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

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

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

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

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: TimeSeriesAnalysis.zip <TimeSeriesAnalysis.zip>`


.. only:: html

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

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