![]() The wrapped scheduler states will also be saved. index: 38-3 from keras.models import Sequential from keras.layers import Dense. It contains an entry for every variable in self._dict_ which Character-level RNN to classify words (PyTorch) author: Robertson. Returns the state of the scheduler as a dict. ![]() print_lr ( is_verbose, group, lr, epoch = None ) ¶ĭisplay the current learning rate. Should be an object returnedįrom a call to state_dict(). Return last computed learning rate by current scheduler. opt, schedulers =, milestones = ) > for epoch in range ( 100 ): > train (. opt, gamma = 0.9 ) > scheduler = SequentialLR ( self. opt, factor = 0.1, total_iters = 2 ) > scheduler2 = ExponentialLR ( self. for all groups > # lr = 0.1 if epoch = 0 > # lr = 0.1 if epoch = 1 > # lr = 0.9 if epoch = 2 > # lr = 0.81 if epoch = 3 > # lr = 0.729 if epoch = 4 > scheduler1 = ConstantLR ( self. Sequential torch.nn.Sequential 1Sequential import torch from torch import nn from torch.nn import Conv2d, MaxPool2d, Flatten, Linear class Model (nn.Module): def init ( self ): super (Model, self).
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