init mlp

Author: redone17

models/__init__.py

@@ -4,4 +4,5 @@
 from .lenet import *
 from .vgg import *
 from .dcnn import *
+# from .mlp import *
 

models/dcnn.py

@@ -1,4 +1,7 @@
 '''
+mport torch
+import torch.nn as nn
+from torch.autograd import Variable
 DCNN for one-dimensional signals in Pytorch.
 '''
 
@@ -47,7 +50,7 @@ def _make_layers(self, cfg):
         return nn.Sequential(*layers)
 
 '''
-net = Net('DCNN09',1,5)
+net = Net('DCNN08',1,5)
 x = torch.randn(10,1,2048)
 print(net(Variable(x)).size())
 '''

models/mlp.py

@@ -0,0 +1,22 @@
+'''
+Multi-Layer Perceptron
+a baseline
+'''
+import torch
+import torch.nn as nn
+from torch.autograd import Variable
+
+class Net(nn.Mudule):
+    def __init__(self, in_features, n_class, n_hiddens=[500, 100], use_batchnorm=False, use_dropout=False):
+        super(Net, self).__init__()
+        self.name = vgg_name
+        self.in_features = in_features
+        self.use_batchnorm = use_batchnorm
+        self.use_dropout = use_dropout
+        self.n_layers = len(n_hiddens) + 1
+        self.n_hiddens = n_hiddens
+        self.mlp = self._make_layers(in_features, n_hiddens, n_class)
+
+    def _make_layers(in_features, n_hiddens, n_class):
+        TODO
+

run.py

@@ -40,11 +40,11 @@
 print( )
 
 ## make models
-model = dcnn.Net('DCNN11', 1, 5)
+model = dcnn.Net('DCNN13', 1, 5)
 
 ## train
 criterion = nn.CrossEntropyLoss()
-optimizer = optim.Adam(model.parameters(), weight_decay=0.0001)
+optimizer = optim.Adam(model.parameters(), weight_decay=0.001)
 best_model, loss_curve = iter_utils.train(model, train_loader, criterion, optimizer,
     init_lr=0.0001, decay_epoch=5, n_epoch=10)