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Diffstat (limited to 'neurobench_testing/examples/gsc/ANN.py')
| -rw-r--r-- | neurobench_testing/examples/gsc/ANN.py | 49 |
1 files changed, 0 insertions, 49 deletions
diff --git a/neurobench_testing/examples/gsc/ANN.py b/neurobench_testing/examples/gsc/ANN.py deleted file mode 100644 index 7607833..0000000 --- a/neurobench_testing/examples/gsc/ANN.py +++ /dev/null @@ -1,49 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F -import torch.optim as optim -import torchaudio -import sys - -from tqdm import tqdm - -class M5(nn.Module): - def __init__(self, n_input=1, n_output=35, stride=16, n_channel=32): - super().__init__() - self.conv1 = nn.Conv1d(n_input, n_channel, kernel_size=80, stride=stride) - self.bn1 = nn.BatchNorm1d(n_channel) - self.pool1 = nn.MaxPool1d(4) - self.conv2 = nn.Conv1d(n_channel, n_channel, kernel_size=3) - self.bn2 = nn.BatchNorm1d(n_channel) - self.pool2 = nn.MaxPool1d(4) - self.conv3 = nn.Conv1d(n_channel, 2 * n_channel, kernel_size=3) - self.bn3 = nn.BatchNorm1d(2 * n_channel) - self.pool3 = nn.MaxPool1d(4) - self.conv4 = nn.Conv1d(2 * n_channel, 2 * n_channel, kernel_size=3) - self.bn4 = nn.BatchNorm1d(2 * n_channel) - self.pool4 = nn.MaxPool1d(4) - self.fc1 = nn.Linear(2 * n_channel, n_output) - - # these need to be different ReLU objects so that they can be individually hooked - self.act1 = nn.ReLU() - self.act2 = nn.ReLU() - self.act3 = nn.ReLU() - self.act4 = nn.ReLU() - - def forward(self, x): - x = self.conv1(x) - x = self.act1(self.bn1(x)) - x = self.pool1(x) - x = self.conv2(x) - x = self.act2(self.bn2(x)) - x = self.pool2(x) - x = self.conv3(x) - x = self.act3(self.bn3(x)) - x = self.pool3(x) - x = self.conv4(x) - x = self.act4(self.bn4(x)) - x = self.pool4(x) - x = F.avg_pool1d(x, x.shape[-1]) - x = x.permute(0, 2, 1) - x = self.fc1(x) - return F.log_softmax(x, dim=2)
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