Name	Size	Mode	Actions
adadelta.cpython-36.pyc	4829	0644	editdlrm
adagrad.cpython-36.pyc	4767	0644	editdlrm
adam.cpython-36.pyc	6250	0644	editdlrm
adamax.cpython-36.pyc	4869	0644	editdlrm
adamw.cpython-36.pyc	6258	0644	editdlrm
asgd.cpython-36.pyc	2848	0644	editdlrm
lbfgs.cpython-36.pyc	8802	0644	editdlrm
lr_scheduler.cpython-36.pyc	63792	0644	editdlrm
nadam.cpython-36.pyc	5727	0644	editdlrm
optimizer.cpython-36.pyc	11827	0644	editdlrm
radam.cpython-36.pyc	5712	0644	editdlrm
rmsprop.cpython-36.pyc	6543	0644	editdlrm
rprop.cpython-36.pyc	5182	0644	editdlrm
sgd.cpython-36.pyc	6267	0644	editdlrm
sparse_adam.cpython-36.pyc	3329	0644	editdlrm
swa_utils.cpython-36.pyc	11370	0644	editdlrm
_functional.cpython-36.pyc	10407	0644	editdlrm
__init__.cpython-36.pyc	1049	0644	editdlrm

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Z�ZS) �Adadeltaa� Implements Adadelta algorithm. .. math:: \begin{aligned} &\rule{110mm}{0.4pt} \\ &\textbf{input} : \gamma \text{ (lr)}, \: \theta_0 \text{ (params)}, \: f(\theta) \text{ (objective)}, \: \rho \text{ (decay)}, \: \lambda \text{ (weight decay)} \\ &\textbf{initialize} : v_0 \leftarrow 0 \: \text{ (square avg)}, \: u_0 \leftarrow 0 \: \text{ (accumulate variables)} \\[-1.ex] &\rule{110mm}{0.4pt} \\ &\textbf{for} \: t=1 \: \textbf{to} \: \ldots \: \textbf{do} \\ &\hspace{5mm}g_t \leftarrow \nabla_{\theta} f_t (\theta_{t-1}) \\ &\hspace{5mm}if \: \lambda \neq 0 \\ &\hspace{10mm} g_t \leftarrow g_t + \lambda \theta_{t-1} \\ &\hspace{5mm} v_t \leftarrow v_{t-1} \rho + g^2_t (1 - \rho) \\ &\hspace{5mm}\Delta x_t \leftarrow \frac{\sqrt{u_{t-1} + \epsilon }}{ \sqrt{v_t + \epsilon} }g_t \hspace{21mm} \\ &\hspace{5mm} u_t \leftarrow u_{t-1} \rho + \Delta x^2_t (1 - \rho) \\ &\hspace{5mm}\theta_t \leftarrow \theta_{t-1} - \gamma \Delta x_t \\ &\rule{110mm}{0.4pt} \\[-1.ex] &\bf{return} \: \theta_t \\[-1.ex] &\rule{110mm}{0.4pt} \\[-1.ex] \end{aligned} For further details regarding the algorithm we refer to `ADADELTA: An Adaptive Learning Rate Method`_. Args: params (iterable): iterable of parameters to optimize or dicts defining parameter groups rho (float, optional): coefficient used for computing a running average of squared gradients (default: 0.9) eps (float, optional): term added to the denominator to improve numerical stability (default: 1e-6) lr (float, optional): coefficient that scale delta before it is applied to the parameters (default: 1.0) weight_decay (float, optional): weight decay (L2 penalty) (default: 0) .. _ADADELTA\: An Adaptive Learning Rate Method: https://arxiv.org/abs/1212.5701 ��?��������?�����ư>r
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