Merge pull request apache#1170 from dcslin/feature/ms_model_mlp

update msmlp

examples/ms_model_mlp/model.py

@@ -83,21 +83,30 @@ def forward(self, x):
 
 class MSMLP(model.Model):
 
-    def __init__(self, data_size=10, perceptron_size=100, num_classes=10):
+    def __init__(self, data_size=10, perceptron_size=100, num_classes=10, layer_hidden_list=[10,10,10,10]):
         super(MSMLP, self).__init__()
         self.num_classes = num_classes
         self.dimension = 2
 
         self.relu = layer.ReLU()
-        self.linear1 = layer.Linear(perceptron_size)
-        self.linear2 = layer.Linear(num_classes)
+        self.linear1 = layer.Linear(layer_hidden_list[0])
+        self.linear2 = layer.Linear(layer_hidden_list[1])
+        self.linear3 = layer.Linear(layer_hidden_list[2])
+        self.linear4 = layer.Linear(layer_hidden_list[3])
+        self.linear5 = layer.Linear(num_classes)
         self.softmax_cross_entropy = layer.SoftMaxCrossEntropy()
         self.sum_error = SumErrorLayer()
-
+    
     def forward(self, inputs):
         y = self.linear1(inputs)
         y = self.relu(y)
         y = self.linear2(y)
+        y = self.relu(y)
+        y = self.linear3(y)
+        y = self.relu(y)
+        y = self.linear4(y)
+        y = self.relu(y)
+        y = self.linear5(y)
         return y
 
     def train_one_batch(self, x, y, dist_option, spars, synflow_flag):
@@ -144,6 +153,7 @@ def set_optimizer(self, optimizer):
 
 def create_model(pretrained=False, **kwargs):
     """Constructs a CNN model.
+
     Args:
         pretrained (bool): If True, returns a pre-trained model.