class torch.optim.Rprop(params, lr=0.01, etas=(0.5, 1.2), step_sizes=(1e-06, 50))[source]

Implements the resilient backpropagation algorithm.

input:θ0Rd (params),f(θ) (objective),η+/ (etaplus, etaminus),Γmax/min (step sizes)initialize:gprev00,η0lr (learning rate)fort=1todogtθft(θt1)for i=0,1,,d1doifgprevigti>0ηtimin(ηt1iη+,Γmax)else ifgprevigti<0ηtimax(ηt1iη,Γmin)elseηtiηt1iθtθt1ηtsign(gt)gprevgtreturnθt\begin{aligned} &\rule{110mm}{0.4pt} \\ &\textbf{input} : \theta_0 \in \mathbf{R}^d \text{ (params)},f(\theta) \text{ (objective)}, \\ &\hspace{13mm} \eta_{+/-} \text{ (etaplus, etaminus)}, \Gamma_{max/min} \text{ (step sizes)} \\ &\textbf{initialize} : g^0_{prev} \leftarrow 0, \: \eta_0 \leftarrow \text{lr (learning rate)} \\ &\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} \textbf{for} \text{ } i = 0, 1, \ldots, d-1 \: \mathbf{do} \\ &\hspace{10mm} \textbf{if} \: g^i_{prev} g^i_t > 0 \\ &\hspace{15mm} \eta^i_t \leftarrow \mathrm{min}(\eta^i_{t-1} \eta_{+}, \Gamma_{max}) \\ &\hspace{10mm} \textbf{else if} \: g^i_{prev} g^i_t < 0 \\ &\hspace{15mm} \eta^i_t \leftarrow \mathrm{max}(\eta^i_{t-1} \eta_{-}, \Gamma_{min}) \\ &\hspace{10mm} \textbf{else} \: \\ &\hspace{15mm} \eta^i_t \leftarrow \eta^i_{t-1} \\ &\hspace{5mm}\theta_t \leftarrow \theta_{t-1}- \eta_t \mathrm{sign}(g_t) \\ &\hspace{5mm}g_{prev} \leftarrow g_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 the paper A Direct Adaptive Method for Faster Backpropagation Learning: The RPROP Algorithm.

  • params (iterable) – iterable of parameters to optimize or dicts defining parameter groups

  • lr (float, optional) – learning rate (default: 1e-2)

  • etas (Tuple[float, float], optional) – pair of (etaminus, etaplis), that are multiplicative increase and decrease factors (default: (0.5, 1.2))

  • step_sizes (Tuple[float, float], optional) – a pair of minimal and maximal allowed step sizes (default: (1e-6, 50))


Add a param group to the Optimizer s param_groups.

This can be useful when fine tuning a pre-trained network as frozen layers can be made trainable and added to the Optimizer as training progresses.


param_group (dict) – Specifies what Tensors should be optimized along with group specific optimization options.


Loads the optimizer state.


state_dict (dict) – optimizer state. Should be an object returned from a call to state_dict().


Returns the state of the optimizer as a dict.

It contains two entries:

  • state - a dict holding current optimization state. Its content

    differs between optimizer classes.

  • param_groups - a list containing all parameter groups where each

    parameter group is a dict


Performs a single optimization step.


closure (callable, optional) – A closure that reevaluates the model and returns the loss.


Sets the gradients of all optimized torch.Tensor s to zero.


set_to_none (bool) – instead of setting to zero, set the grads to None. This will in general have lower memory footprint, and can modestly improve performance. However, it changes certain behaviors. For example: 1. When the user tries to access a gradient and perform manual ops on it, a None attribute or a Tensor full of 0s will behave differently. 2. If the user requests zero_grad(set_to_none=True) followed by a backward pass, .grads are guaranteed to be None for params that did not receive a gradient. 3. torch.optim optimizers have a different behavior if the gradient is 0 or None (in one case it does the step with a gradient of 0 and in the other it skips the step altogether).


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