RMSprop¶
- class
dragon.vm.tensorflow.keras.optimizers.
RMSprop
(
learning_rate=0.001,
rho=0.9,
momentum=0.0,
epsilon=1e-07,
name=None,
**kwargs
)[source]¶ The optimizer to apply RMSprop algorithm. [Hinton et.al, 2013].
The RMSprop update is defined as:
\[\text{RMSprop}(g) = \text{lr} * m_{t} \\ \quad \\ \text{where}\quad \begin{cases} v_{t} = \alpha * v_{t-1} + (1 - \alpha) * g^{2} \\ m_{t} = \text{momentum} * m_{t-1} + \frac{g}{\sqrt{v_{t}} + \epsilon} \end{cases} \]
__init__¶
RMSprop.
__init__
(
learning_rate=0.001,
rho=0.9,
momentum=0.0,
epsilon=1e-07,
name=None,
**kwargs
)[source]¶Create a
RMSprop
optimizer.- Parameters:
- learning_rate (float, optional, default=0.001) – The initial value to \(\text{lr}\).
- rho (float, optional, default=0.9) – The initial value to \(\alpha\).
- momentum (float, optional, default=0) – The initial value to \(\text{momentum}\).
- epsilon (float, optional, default=1e-7) – The initial value to \(\epsilon\).
- name (str, optional) – The optional optimizer name.
Properties¶
iterations¶
Optimizer.
iterations
Return the number of steps has run.
- Returns:
- int – The iterations.
Methods¶
apply_gradients¶
Optimizer.
apply_gradients
(grads_and_vars)[source]Apply the gradients to update variables.
- Parameters:
- grads_and_vars (Sequence[Sequence[dragon.Tensor]]) – The gradients and variables.
- Returns:
dragon.vm.tensorflow.keras.optimizers.Optimizer – The self to generate the update operations.