| 4 | |
| 5 | |
| 6 | class DeepLOB(pl.LightningModule): |
| 7 | def __init__(self, lighten): |
| 8 | super().__init__() |
| 9 | self.name = "deeplob" |
| 10 | if lighten: |
| 11 | self.name += "-lighten" |
| 12 | |
| 13 | # Convolution blocks. |
| 14 | self.conv1 = nn.Sequential( |
| 15 | nn.Conv2d( |
| 16 | in_channels=1, out_channels=32, kernel_size=(1, 2), stride=(1, 2) |
| 17 | ), |
| 18 | nn.LeakyReLU(negative_slope=0.01), |
| 19 | nn.BatchNorm2d(32), |
| 20 | nn.Conv2d(in_channels=32, out_channels=32, kernel_size=(4, 1)), |
| 21 | nn.LeakyReLU(negative_slope=0.01), |
| 22 | nn.BatchNorm2d(32), |
| 23 | nn.Conv2d(in_channels=32, out_channels=32, kernel_size=(4, 1)), |
| 24 | nn.LeakyReLU(negative_slope=0.01), |
| 25 | nn.BatchNorm2d(32), |
| 26 | ) |
| 27 | self.conv2 = nn.Sequential( |
| 28 | nn.Conv2d( |
| 29 | in_channels=32, out_channels=32, kernel_size=(1, 2), stride=(1, 2) |
| 30 | ), |
| 31 | nn.LeakyReLU(negative_slope=0.01), |
| 32 | nn.BatchNorm2d(32), |
| 33 | nn.Conv2d(in_channels=32, out_channels=32, kernel_size=(4, 1)), |
| 34 | nn.LeakyReLU(negative_slope=0.01), |
| 35 | nn.BatchNorm2d(32), |
| 36 | nn.Conv2d(in_channels=32, out_channels=32, kernel_size=(4, 1)), |
| 37 | nn.LeakyReLU(negative_slope=0.01), |
| 38 | nn.BatchNorm2d(32), |
| 39 | ) |
| 40 | |
| 41 | if lighten: |
| 42 | conv3_kernel_size = 5 |
| 43 | else: |
| 44 | conv3_kernel_size = 10 |
| 45 | |
| 46 | self.conv3 = nn.Sequential( |
| 47 | nn.Conv2d( |
| 48 | in_channels=32, out_channels=32, kernel_size=(1, conv3_kernel_size) |
| 49 | ), |
| 50 | nn.LeakyReLU(negative_slope=0.01), |
| 51 | nn.BatchNorm2d(32), |
| 52 | nn.Conv2d(in_channels=32, out_channels=32, kernel_size=(4, 1)), |
| 53 | nn.LeakyReLU(negative_slope=0.01), |
| 54 | nn.BatchNorm2d(32), |
| 55 | nn.Conv2d(in_channels=32, out_channels=32, kernel_size=(4, 1)), |
| 56 | nn.LeakyReLU(negative_slope=0.01), |
| 57 | nn.BatchNorm2d(32), |
| 58 | ) |
| 59 | |
| 60 | # Inception modules. |
| 61 | self.inp1 = nn.Sequential( |
| 62 | nn.Conv2d( |
| 63 | in_channels=32, out_channels=64, kernel_size=(1, 1), padding="same" |