*Memos:
- My post explains how to set and get dtype.
- My post explains how to set and get device.
- My post explains how to set requires_grad and get grad.
- My post explains
keepdimargument. - My post explains
outargument.
You can set out as shown below:
*Memos:
- I selected some popular
biasargument classes such as Linear(), Conv1d() ConvTranspose1d() and LayerNorm(). - Basically,
bias(Optional-Default:True-Type:bool) can add a bias for calculation. *Trueadds a bias whileFalsedoesn't so it'sNone. - Basically,
bias=can be omitted. - My post explains manual_seed().
Linear(). *My post explains Linear():
import torch from torch import nn my_tensor = torch.tensor([8., -3., 0., 1., 5., -2.]) torch.manual_seed(42) linear = nn.Linear(in_features=6, out_features=3, bias=True) linear.bias # Parameter containing: # tensor([-0.1906, 0.1041, -0.1881], requires_grad=True) linear(input=my_tensor) # tensor([1.0529, -0.8833, 3.4542], grad_fn=<ViewBackward0>) torch.manual_seed(42) linear = nn.Linear(in_features=6, out_features=3, bias=False) print(linear.bias) # None linear(input=my_tensor) # tensor([1.2434, -0.9874, 3.6423], grad_fn=<SqueezeBackward4>) Conv1d(). *My post explains Conv1d():
import torch from torch import nn my_tensor = torch.tensor([[8., -3., 0., 1., 5., -2.]]) torch.manual_seed(42) conv1d = nn.Conv1d(in_channels=1, out_channels=3, kernel_size=1, bias=True) conv1d.bias # Parameter containing: # tensor([0.9186, -0.2191, 0.2018], requires_grad=True) conv1d(input=my_tensor) # tensor([[7.0349, -1.3750, 0.9186, 1.6831, 4.7413, -0.6105], # [6.4210, -2.7091, -0.2191, 0.6109, 3.9309, -1.8791], # [-1.6724, 0.9046, 0.2018, -0.0325, -0.9696, 0.6703]], # grad_fn=<SqueezeBackward1>) torch.manual_seed(42) conv1d = nn.Conv1d(in_channels=1, out_channels=3, kernel_size=1, bias=False) print(conv1d.bias) # None conv1d(input=my_tensor) # tensor([[6.1163, -2.2936, 0.0000, 0.7645, 3.8227, -1.5291], # [6.6401, -2.4900, 0.0000, 0.8300, 4.1500, -1.6600], # [-1.8742, 0.7028, -0.0000, -0.2343, -1.1714, 0.4685]], # grad_fn=<SqueezeBackward1>) ConvTranspose1d(). *My post explains ConvTranspose1d():
import torch from torch import nn my_tensor = torch.tensor([[8., -3., 0., 1., 5., -2.]]) torch.manual_seed(42) convtran1d = nn.ConvTranspose1d(in_channels=1, out_channels=3, kernel_size=1, bias=True) convtran1d.bias # Parameter containing: # tensor([0.5304, -0.1265, 0.1165], requires_grad=True) convtran1d(input=my_tensor) # tensor([[4.0616, -0.7939, 0.5304, 0.9718, 2.7374, -0.3525], # [3.7071, -1.5641, -0.1265, 0.3527, 2.2695, -1.0849], # [-0.9656, 0.5223, 0.1165, -0.0188, -0.5598, 0.3870]], # grad_fn=<SqueezeBackward1>) torch.manual_seed(42) convtran1d = nn.ConvTranspose1d(in_channels=1, out_channels=3, kernel_size=1, bias=False) print(convtran1d.bias) # None convtran1d(input=my_tensor) # tensor([[3.5313, -1.3242, 0.0000, 0.4414, 2.2070, -0.8828], # [3.8336, -1.4376, 0.0000, 0.4792, 2.3960, -0.9584], # [-1.0821, 0.4058, 0.0000, -0.1353, -0.6763, 0.2705]], # grad_fn=<SqueezeBackward1>) LayerNorm(). *My post explains LayerNorm():
import torch from torch import nn my_tensor = torch.tensor([8., -3., 0., 1., 5., -2.]) torch.manual_seed(42) layernorm = nn.LayerNorm(normalized_shape=6, bias=True) layernorm.bias # Parameter containing: # tensor([0., 0., 0., 0., 0., 0.], requires_grad=True) layernorm(input=my_tensor) # tensor([1.6830, -1.1651, -0.3884, -0.1295, 0.9062, -0.9062], # grad_fn=<NativeLayerNormBackward0>) torch.manual_seed(42) layernorm = nn.LayerNorm(normalized_shape=6, bias=False) print(layernorm.bias) # None layernorm(input=my_tensor) # tensor([1.6830, -1.1651, -0.3884, -0.1295, 0.9062, -0.9062], # grad_fn=<NativeLayerNormBackward0>) 
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