Python torch.nn.RReLU() Examples
The following are 9
code examples of torch.nn.RReLU().
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Example #1
| Source File: supervised_topic_model.py From causal-text-embeddings with MIT License | 7 votes |
|
def get_activation(self, act):
if act == 'tanh':
act = nn.Tanh()
elif act == 'relu':
act = nn.ReLU()
elif act == 'softplus':
act = nn.Softplus()
elif act == 'rrelu':
act = nn.RReLU()
elif act == 'leakyrelu':
act = nn.LeakyReLU()
elif act == 'elu':
act = nn.ELU()
elif act == 'selu':
act = nn.SELU()
elif act == 'glu':
act = nn.GLU()
else:
print('Defaulting to tanh activations...')
act = nn.Tanh()
return act
Example #2
| Source File: utils.py From pnn.pytorch.update with MIT License | 7 votes |
|
def act_fn(act):
if act == 'relu':
act_ = nn.ReLU(inplace=False)
elif act == 'lrelu':
act_ = nn.LeakyReLU(inplace=True)
elif act == 'prelu':
act_ = nn.PReLU()
elif act == 'rrelu':
act_ = nn.RReLU(inplace=True)
elif act == 'elu':
act_ = nn.ELU(inplace=True)
elif act == 'selu':
act_ = nn.SELU(inplace=True)
elif act == 'tanh':
act_ = nn.Tanh()
elif act == 'sigmoid':
act_ = nn.Sigmoid()
else:
print('\n\nActivation function {} is not supported/understood\n\n'.format(act))
act_ = None
return act_
Example #3
| Source File: etm.py From ETM with MIT License | 7 votes |
|
def get_activation(self, act):
if act == 'tanh':
act = nn.Tanh()
elif act == 'relu':
act = nn.ReLU()
elif act == 'softplus':
act = nn.Softplus()
elif act == 'rrelu':
act = nn.RReLU()
elif act == 'leakyrelu':
act = nn.LeakyReLU()
elif act == 'elu':
act = nn.ELU()
elif act == 'selu':
act = nn.SELU()
elif act == 'glu':
act = nn.GLU()
else:
print('Defaulting to tanh activations...')
act = nn.Tanh()
return act
Example #4
| Source File: test_cli.py From skorch with BSD 3-Clause "New" or "Revised" License | 6 votes |
|
def test_parse_net_kwargs(self, parse_net_kwargs):
kwargs = {
'lr': 0.05,
'max_epochs': 5,
'module__num_units': 10,
'module__nonlin': 'torch.nn.RReLU(0.123, upper=0.456)',
}
parsed_kwargs = parse_net_kwargs(kwargs)
assert len(parsed_kwargs) == 4
assert np.isclose(parsed_kwargs['lr'], 0.05)
assert parsed_kwargs['max_epochs'] == 5
assert parsed_kwargs['module__num_units'] == 10
assert isinstance(parsed_kwargs['module__nonlin'], RReLU)
assert np.isclose(parsed_kwargs['module__nonlin'].lower, 0.123)
assert np.isclose(parsed_kwargs['module__nonlin'].upper, 0.456)
Example #5
| Source File: fc.py From Attention-on-Attention-for-VQA with MIT License | 6 votes |
|
def get_act(act):
if act == 'ReLU':
act_layer = nn.ReLU
elif act == 'LeakyReLU':
act_layer = nn.LeakyReLU
elif act == 'PReLU':
act_layer = nn.PReLU
elif act == 'RReLU':
act_layer = nn.RReLU
elif act == 'ELU':
act_layer = nn.ELU
elif act == 'SELU':
act_layer = nn.SELU
elif act == 'Tanh':
act_layer = nn.Tanh
elif act == 'Hardtanh':
act_layer = nn.Hardtanh
elif act == 'Sigmoid':
act_layer = nn.Sigmoid
else:
print("Invalid activation function")
raise Exception("Invalid activation function")
return act_layer
Example #6
| Source File: Base_Network.py From nn_builder with MIT License | 5 votes |
|
def create_str_to_activations_converter(self):
"""Creates a dictionary which converts strings to activations"""
str_to_activations_converter = {"elu": nn.ELU(), "hardshrink": nn.Hardshrink(), "hardtanh": nn.Hardtanh(),
"leakyrelu": nn.LeakyReLU(), "logsigmoid": nn.LogSigmoid(), "prelu": nn.PReLU(),
"relu": nn.ReLU(), "relu6": nn.ReLU6(), "rrelu": nn.RReLU(), "selu": nn.SELU(),
"sigmoid": nn.Sigmoid(), "softplus": nn.Softplus(), "logsoftmax": nn.LogSoftmax(),
"softshrink": nn.Softshrink(), "softsign": nn.Softsign(), "tanh": nn.Tanh(),
"tanhshrink": nn.Tanhshrink(), "softmin": nn.Softmin(), "softmax": nn.Softmax(dim=1),
"none": None}
return str_to_activations_converter
Example #7
| Source File: test_cli.py From skorch with BSD 3-Clause "New" or "Revised" License | 5 votes |
|
def test_resolve_dotted_name_instantiated(self, resolve_dotted_name):
result = resolve_dotted_name('torch.nn.RReLU(0.123, upper=0.456)')
assert isinstance(result, RReLU)
assert np.isclose(result.lower, 0.123)
assert np.isclose(result.upper, 0.456)
Example #8
| Source File: unet.py From elektronn3 with MIT License | 5 votes |
|
def get_activation(activation):
if isinstance(activation, str):
if activation == 'relu':
return nn.ReLU()
elif activation == 'leaky':
return nn.LeakyReLU(negative_slope=0.1)
elif activation == 'prelu':
return nn.PReLU(num_parameters=1)
elif activation == 'rrelu':
return nn.RReLU()
elif activation == 'lin':
return nn.Identity()
else:
# Deep copy is necessary in case of paremtrized activations
return copy.deepcopy(activation)
Example #9
| Source File: base_utils.py From pt-ranking.github.io with MIT License | 4 votes |
|
def get_AF(af_str):
"""
Given the string identifier, get PyTorch-supported activation function.
"""
if af_str == 'R':
return nn.ReLU() # ReLU(x)=max(0,x)
elif af_str == 'LR':
return nn.LeakyReLU() # LeakyReLU(x)=max(0,x)+negative_slope∗min(0,x)
elif af_str == 'RR':
return nn.RReLU() # the randomized leaky rectified liner unit function
elif af_str == 'E': # ELU(x)=max(0,x)+min(0,α∗(exp(x)−1))
return nn.ELU()
elif af_str == 'SE': # SELU(x)=scale∗(max(0,x)+min(0,α∗(exp(x)−1)))
return nn.SELU()
elif af_str == 'CE': # CELU(x)=max(0,x)+min(0,α∗(exp(x/α)−1))
return nn.CELU()
elif af_str == 'S':
return nn.Sigmoid()
elif af_str == 'SW':
#return SWISH()
raise NotImplementedError
elif af_str == 'T':
return nn.Tanh()
elif af_str == 'ST': # a kind of normalization
return F.softmax() # Applies the Softmax function to an n-dimensional input Tensor rescaling them so that the elements of the n-dimensional output Tensor lie in the range (0,1) and sum to 1
elif af_str == 'EP':
#return Exp()
raise NotImplementedError
else:
raise NotImplementedError
