--- a
+++ b/model.py
@@ -0,0 +1,111 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+class ConvBlock(nn.Module):
+	
+	def __init__(self, in_channels, out_channels, kernel, stride):
+		super(ConvBlock, self).__init__()
+
+		self.layer = nn.Sequential(
+					nn.Conv1d(in_channels = in_channels, out_channels = out_channels, kernel_size = kernel, stride = stride),
+					nn.BatchNorm1d(out_channels),
+					nn.ReLU()
+					)
+
+	def forward(self, x):
+
+		out = self.layer(x)
+
+		return out
+
+
+class CNN1D(nn.Module):
+	
+	def __init__(self, num_classes):
+		super(CNN1D, self).__init__()
+
+		self.layer = nn.Sequential(
+					ConvBlock(16, 16, 10, 4),
+					ConvBlock(16, 16, 5, 2),
+					ConvBlock(16, 16, 5, 2),
+					ConvBlock(16, 32, 5, 2),
+					nn.MaxPool1d(kernel_size = 2, stride = 2),
+					ConvBlock(32, 32, 4, 1),
+					ConvBlock(32, 32, 4, 1),
+					ConvBlock(32, 64, 4, 1),
+					nn.MaxPool1d(kernel_size = 2, stride = 2),
+					ConvBlock(64, 64, 3, 1),
+					ConvBlock(64, 64, 3, 1),
+					ConvBlock(64, 128, 3, 1),
+					nn.MaxPool1d(kernel_size = 2, stride = 2),
+					ConvBlock(128, 128, 2, 1),
+					ConvBlock(128, 128, 2, 1),
+					ConvBlock(128, 256, 2, 1),
+					nn.MaxPool1d(kernel_size = 2, stride = 2)
+					)
+
+		self.linear = nn.Sequential(
+					nn.Linear(1280, 512),
+					nn.ReLU(),
+					nn.Dropout(0.5),
+					nn.Linear(512, 128),
+					nn.ReLU(),
+					nn.Linear(128, num_classes)
+					)
+
+	def forward(self, x):
+
+		batch_size = x.shape[0]
+		out = self.layer(x)
+		out = out.view(batch_size, -1)
+		out = self.linear(out)
+
+		return out
+
+
+class CNN1D_F(nn.Module):
+	
+	def __init__(self, num_classes):
+		super(CNN1D_F, self).__init__()
+
+		self.layer = nn.Sequential(
+					ConvBlock(16, 16, 10, 4),
+					ConvBlock(16, 16, 5, 2),
+					ConvBlock(16, 16, 5, 2),
+					nn.MaxPool1d(kernel_size = 2, stride = 2),
+					ConvBlock(16, 16, 5, 2),
+					ConvBlock(16, 16, 5, 2),
+					ConvBlock(16, 32, 5, 2),
+					nn.MaxPool1d(kernel_size = 2, stride = 2),
+					ConvBlock(32, 32, 4, 1),
+					ConvBlock(32, 32, 4, 1),
+					ConvBlock(32, 64, 4, 1),
+					nn.MaxPool1d(kernel_size = 2, stride = 2),
+					ConvBlock(64, 64, 3, 1),
+					ConvBlock(64, 64, 3, 1),
+					ConvBlock(64, 128, 3, 1),
+					nn.MaxPool1d(kernel_size = 2, stride = 2),
+					ConvBlock(128, 128, 2, 1),
+					ConvBlock(128, 128, 2, 1),
+					ConvBlock(128, 256, 2, 1),
+					nn.MaxPool1d(kernel_size = 2, stride = 2)
+					)
+
+		self.linear = nn.Sequential(
+					nn.Linear(1280, 512),
+					nn.ReLU(),
+					nn.Dropout(0.5),
+					nn.Linear(512, 128),
+					nn.ReLU(),
+					nn.Linear(128, num_classes)
+					)
+
+	def forward(self, x):
+
+		batch_size = x.shape[0]
+		out = self.layer(x)
+		out = out.view(batch_size, -1)
+		out = self.linear(out)
+
+		return out
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