--- a
+++ b/4-Models/CRNN-pytorch/CRNN.py
@@ -0,0 +1,61 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import math
+
+class CRNN(nn.Module):
+    def __init__(self, hidR = 256, layerR = 1, hidC = 256):
+        super(CRNN, self).__init__()
+        # len of input (time domain)
+        self.P = 2500
+        # width of input
+        self.m = 12
+        # hidden size of RNN
+        self.hidR = hidR
+        self.layerR = layerR
+        # hidden size of CNN
+        self.hidC = hidC
+        # kernel size of CNN
+        self.Ck = 5;
+
+        self.conv1 = nn.Sequential(
+            nn.Conv2d(1, self.hidC, kernel_size = (self.Ck, self.m)),
+            nn.BatchNorm2d(self.hidC),
+            nn.ReLU(),
+            nn.Dropout(0.5),
+        )
+    
+        self.GRU1 = nn.GRU(self.hidC, self.hidR, num_layers=self.layerR, bidirectional=False);
+        
+        self.fc1 = nn.Sequential(
+            nn.Flatten(),
+            nn.Linear(self.hidR * self.layerR * 1, 256),
+            nn.BatchNorm1d(256),
+            nn.ReLU(),
+            nn.Dropout(0.5),
+            nn.Linear(256, 128),
+            nn.BatchNorm1d(128),
+            nn.ReLU(),
+            nn.Dropout(0.5),
+            nn.Linear(128, 32),
+            nn.ReLU(),
+            nn.BatchNorm1d(32),
+            nn.Linear(32, 1)
+        )
+        
+    def forward(self, x):
+        batch_size = x.size(0);
+        
+        #CNN
+        c = x.view(-1, 1, self.P, self.m);
+        c = self.conv1(c);
+        c = torch.squeeze(c, 3);
+        
+        # RNN 
+        r = c.permute(2, 0, 1).contiguous();
+        _, r = self.GRU1(r);
+        
+        r = r.view(batch_size, -1)
+
+        res = self.fc1(r);
+        return res.view(-1)
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