| 22 | |
| 23 | |
| 24 | class AlexNet(model.Model): |
| 25 | |
| 26 | def __init__(self, num_classes=10, num_channels=1): |
| 27 | super(AlexNet, self).__init__() |
| 28 | self.num_classes = num_classes |
| 29 | self.input_size = 224 |
| 30 | self.dimension = 4 |
| 31 | self.conv1 = layer.Conv2d(num_channels, 64, 11, stride=4, padding=2) |
| 32 | self.conv2 = layer.Conv2d(64, 192, 5, padding=2) |
| 33 | self.conv3 = layer.Conv2d(192, 384, 3, padding=1) |
| 34 | self.conv4 = layer.Conv2d(384, 256, 3, padding=1) |
| 35 | self.conv5 = layer.Conv2d(256, 256, 3, padding=1) |
| 36 | self.linear1 = layer.Linear(4096) |
| 37 | self.linear2 = layer.Linear(4096) |
| 38 | self.linear3 = layer.Linear(num_classes) |
| 39 | self.pooling1 = layer.MaxPool2d(2, 2, padding=0) |
| 40 | self.pooling2 = layer.MaxPool2d(2, 2, padding=0) |
| 41 | self.pooling3 = layer.MaxPool2d(2, 2, padding=0) |
| 42 | self.avg_pooling1 = layer.AvgPool2d(3, 2, padding=0) |
| 43 | self.relu1 = layer.ReLU() |
| 44 | self.relu2 = layer.ReLU() |
| 45 | self.relu3 = layer.ReLU() |
| 46 | self.relu4 = layer.ReLU() |
| 47 | self.relu5 = layer.ReLU() |
| 48 | self.relu6 = layer.ReLU() |
| 49 | self.relu7 = layer.ReLU() |
| 50 | self.flatten = layer.Flatten() |
| 51 | self.dropout1 = layer.Dropout() |
| 52 | self.dropout2 = layer.Dropout() |
| 53 | self.softmax_cross_entropy = layer.SoftMaxCrossEntropy() |
| 54 | |
| 55 | def forward(self, x): |
| 56 | y = self.conv1(x) |
| 57 | y = self.relu1(y) |
| 58 | y = self.pooling1(y) |
| 59 | y = self.conv2(y) |
| 60 | y = self.relu2(y) |
| 61 | y = self.pooling2(y) |
| 62 | y = self.conv3(y) |
| 63 | y = self.relu3(y) |
| 64 | y = self.conv4(y) |
| 65 | y = self.relu4(y) |
| 66 | y = self.conv5(y) |
| 67 | y = self.relu5(y) |
| 68 | y = self.pooling3(y) |
| 69 | y = self.avg_pooling1(y) |
| 70 | y = self.flatten(y) |
| 71 | y = self.dropout1(y) |
| 72 | y = self.linear1(y) |
| 73 | y = self.relu6(y) |
| 74 | y = self.dropout2(y) |
| 75 | y = self.linear2(y) |
| 76 | y = self.relu7(y) |
| 77 | y = self.linear3(y) |
| 78 | return y |
| 79 | |
| 80 | def train_one_batch(self, x, y, dist_option, spars): |
| 81 | out = self.forward(x) |