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objax.functional package

.. currentmodule:: objax.functional

objax.functional

.. currentmodule:: objax.functional

Due to the large number of APIs in this section, we organized it into the following sub-sections:

Activation

.. autosummary::

    celu
    elu
    leaky_relu
    log_sigmoid
    log_softmax
    logsumexp
    relu
    selu
    sigmoid
    softmax
    softplus
    tanh


.. autofunction:: celu

.. autofunction:: elu

.. autofunction:: leaky_relu

.. autofunction:: log_sigmoid

.. autofunction:: log_softmax

.. autofunction:: logsumexp

.. autofunction:: relu

.. autofunction:: selu

.. autofunction:: sigmoid

.. autofunction:: softmax

.. autofunction:: softplus

.. autofunction:: tanh

Pooling

.. autosummary::

    average_pool_2d
    batch_to_space2d
    channel_to_space2d
    max_pool_2d
    space_to_batch2d
    space_to_channel2d


.. autofunction:: average_pool_2d

    For a definition of pooling, including examples see
    `Pooling Layer <https://cs231n.github.io/convolutional-networks/#pool>`_.


.. autofunction:: batch_to_space2d

.. autofunction:: channel_to_space2d

.. autofunction:: max_pool_2d

    For a definition of pooling, including examples see
    `Pooling Layer <https://cs231n.github.io/convolutional-networks/#pool>`_.


.. autofunction:: space_to_batch2d

.. autofunction:: space_to_channel2d

Misc

.. autosummary::

    dynamic_slice
    flatten
    one_hot
    pad
    stop_gradient
    top_k
    rsqrt
    upscale_nn


.. autofunction:: dynamic_slice

.. autofunction:: flatten

.. autofunction:: one_hot

.. autofunction:: pad

.. autofunction:: stop_gradient

.. autofunction:: top_k

.. autofunction:: rsqrt

.. autofunction:: upscale_nn

objax.functional.divergence

.. currentmodule:: objax.functional.divergence


.. autosummary::

   kl


.. autofunction:: kl

   .. math::
      kl(p,q) = p \cdot \log{\frac{p + \epsilon}{q + \epsilon}}

   The :math:`\epsilon` term is added to ensure that neither :code:`p` nor :code:`q` are zero.

objax.functional.loss

.. currentmodule:: objax.functional.loss


.. autosummary::

    cross_entropy_logits
    cross_entropy_logits_sparse
    l2
    sigmoid_cross_entropy_logits


.. autofunction:: cross_entropy_logits

Calculates the cross entropy loss, defined as follows:

\begin{eqnarray}
l(y,\hat{y}) & = & - \sum_{j=1}^{q} y_j \log \frac{e^{o_j}}{\sum_{k=1}^{q} e^{o_k}} \nonumber \\
& = & \log \sum_{k=1}^{q} e^{o_k} - \sum_{j=1}^{q} y_j o_j \nonumber
\end{eqnarray}

where o_k are the logits and y_k are the labels.

.. autofunction:: cross_entropy_logits_sparse


.. autofunction:: l2

Calculates the l2 loss, as:

l_2 = \frac{\sum_{i} x_{i}^2}{2}

.. autofunction:: sigmoid_cross_entropy_logits

objax.functional.parallel

.. currentmodule:: objax.functional.parallel


.. autosummary::

   pmax
   pmean
   pmin
   psum


.. autofunction:: pmax

.. autofunction:: pmean

.. autofunction:: pmin

.. autofunction:: psum