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90 lines
2.6 KiB
Python
90 lines
2.6 KiB
Python
#!/bin/python
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import torch
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import torch.nn as nn
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class MLP(nn.Module):
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def __init__(self
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, in_features
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, out_features
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, hidden_layers
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, actv_func
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, pre_module_list=None
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, use_dropout=False
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, use_batch_norm=False
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, use_softmax=True
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, device="cpu"
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):
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super(MLP, self).__init__()
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self.in_features = in_features
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self.out_features = out_features
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self.num_hidden_layers = len(hidden_layers)
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self.hidden_layers = hidden_layers
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self.use_dropout = use_dropout
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self.use_batch_norm = use_batch_norm
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self.actv_func = actv_func
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self.use_softmax = use_softmax
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self.device = device
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# Add on to another model
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if pre_module_list:
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self.module_list = pre_module_list
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else:
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self.module_list = nn.ModuleList()
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self.build_()
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# Send to gpu
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self.to(self.device)
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def build_(self):
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# Activation Functions for Fully connected layers #
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# Start with input dimensions
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dim = self.in_features
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for i in range(self.num_hidden_layers):
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# Create a fully connected layer between the last layer
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# and the current hidden layer
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self.module_list.append(nn.Linear(dim, self.hidden_layers[i]))
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# Update the current dimension
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dim = self.hidden_layers[i]
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if self.use_batch_norm:
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self.module_list.append( nn.BatchNorm1d(dim, affine=True) )
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# Add the Activation function
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self.module_list.append( self.GetActivation(name=self.actv_func[i]) )
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if self.use_dropout:
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self.module_list.append( nn.Dropout(p=0.10) )
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# Fully connect to output dimensions
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if dim != self.out_features:
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self.module_list.append( nn.Linear(dim, self.out_features) )
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def forward(self, x):
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# Flatten the 2d image into 1d
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# Also convert into float for FC layer
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x = torch.flatten(x.float(), start_dim=1)
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# Apply each layer in the module list
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for i in range( len(self.module_list) ):
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x = self.module_list[i](x)
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return x
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def GetActivation(self, name="relu"):
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if name == "relu":
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return nn.ReLU()
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elif name == "leakyrelu":
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return nn.LeakyReLU()
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elif name == "Sigmoid":
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return nn.Sigmoid()
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elif name == "Tanh":
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return nn.Tanh()
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elif name == "Identity":
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return nn.Identity()
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else:
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return nn.ReLU() |