Module: Torch::NN::Init
- Defined in:
- lib/torch/nn/init.rb
Class Method Summary collapse
-
._calculate_fan_in_and_fan_out(tensor) ⇒ Object
TODO move to C++ when released.
- .calculate_gain(nonlinearity, param: 0.01) ⇒ Object
- .kaiming_normal!(tensor, a: 0, mode: "fan_in", nonlinearity: "leaky_relu") ⇒ Object
- .kaiming_uniform!(tensor, a: 0, mode: "fan_in", nonlinearity: "leaky_relu") ⇒ Object
- .normal!(tensor, mean: 0.0, std: 1.0) ⇒ Object
- .orthogonal!(tensor, gain: 1) ⇒ Object
- .sparse!(tensor, sparsity, std: 0.01) ⇒ Object
- .uniform!(tensor, a: 0.0, b: 1.0) ⇒ Object
- .xavier_normal!(tensor, gain: 1.0) ⇒ Object
- .xavier_uniform!(tensor, gain: 1.0) ⇒ Object
Class Method Details
._calculate_fan_in_and_fan_out(tensor) ⇒ Object
TODO move to C++ when released
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# File 'lib/torch/nn/init.rb', line 48 def _calculate_fan_in_and_fan_out(tensor) dimensions = tensor.dim if dimensions < 2 raise Error, "Fan in and fan out can not be computed for tensor with fewer than 2 dimensions" end if dimensions == 2 fan_in = tensor.size(1) fan_out = tensor.size(0) else num_input_fmaps = tensor.size(1) num_output_fmaps = tensor.size(0) receptive_field_size = 1 if tensor.dim > 2 receptive_field_size = tensor[0][0].numel end fan_in = num_input_fmaps * receptive_field_size fan_out = num_output_fmaps * receptive_field_size end [fan_in, fan_out] end |
.calculate_gain(nonlinearity, param: 0.01) ⇒ Object
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# File 'lib/torch/nn/init.rb', line 5 def calculate_gain(nonlinearity, param: 0.01) _calculate_gain(nonlinearity, param) end |
.kaiming_normal!(tensor, a: 0, mode: "fan_in", nonlinearity: "leaky_relu") ⇒ Object
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# File 'lib/torch/nn/init.rb', line 35 def kaiming_normal!(tensor, a: 0, mode: "fan_in", nonlinearity: "leaky_relu") _kaiming_normal!(tensor, a, mode, nonlinearity) end |
.kaiming_uniform!(tensor, a: 0, mode: "fan_in", nonlinearity: "leaky_relu") ⇒ Object
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# File 'lib/torch/nn/init.rb', line 31 def kaiming_uniform!(tensor, a: 0, mode: "fan_in", nonlinearity: "leaky_relu") _kaiming_uniform!(tensor, a, mode, nonlinearity) end |
.normal!(tensor, mean: 0.0, std: 1.0) ⇒ Object
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# File 'lib/torch/nn/init.rb', line 13 def normal!(tensor, mean: 0.0, std: 1.0) _normal!(tensor, mean, std) end |
.orthogonal!(tensor, gain: 1) ⇒ Object
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# File 'lib/torch/nn/init.rb', line 39 def orthogonal!(tensor, gain: 1) _orthogonal!(tensor, gain) end |
.sparse!(tensor, sparsity, std: 0.01) ⇒ Object
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# File 'lib/torch/nn/init.rb', line 43 def sparse!(tensor, sparsity, std: 0.01) _sparse!(tensor, sparsity, std) end |
.uniform!(tensor, a: 0.0, b: 1.0) ⇒ Object
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# File 'lib/torch/nn/init.rb', line 9 def uniform!(tensor, a: 0.0, b: 1.0) _uniform!(tensor, a, b) end |
.xavier_normal!(tensor, gain: 1.0) ⇒ Object
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# File 'lib/torch/nn/init.rb', line 27 def xavier_normal!(tensor, gain: 1.0) _xavier_normal!(tensor, gain) end |
.xavier_uniform!(tensor, gain: 1.0) ⇒ Object
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# File 'lib/torch/nn/init.rb', line 23 def xavier_uniform!(tensor, gain: 1.0) _xavier_uniform!(tensor, gain) end |