(self, data_size=10, perceptron_size=100, num_classes=10, layer_hidden_list=[10,10,10,10])
| 84 | class MSMLP(model.Model): |
| 85 | |
| 86 | def __init__(self, data_size=10, perceptron_size=100, num_classes=10, layer_hidden_list=[10,10,10,10]): |
| 87 | super(MSMLP, self).__init__() |
| 88 | self.num_classes = num_classes |
| 89 | self.dimension = 2 |
| 90 | |
| 91 | self.relu = layer.ReLU() |
| 92 | self.linear1 = layer.Linear(layer_hidden_list[0]) |
| 93 | self.linear2 = layer.Linear(layer_hidden_list[1]) |
| 94 | self.linear3 = layer.Linear(layer_hidden_list[2]) |
| 95 | self.linear4 = layer.Linear(layer_hidden_list[3]) |
| 96 | self.linear5 = layer.Linear(num_classes) |
| 97 | self.softmax_cross_entropy = layer.SoftMaxCrossEntropy() |
| 98 | self.sum_error = SumErrorLayer() |
| 99 | |
| 100 | def forward(self, inputs): |
| 101 | y = self.linear1(inputs) |
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