Increased layer count of model

discriminate.py

@@ -8,6 +8,7 @@ import numpy as np
 import random
 
 import shark
+from model import Model
 
 bs = int(256/8)
 

model.py

@@ -0,0 +1,26 @@
+import torch
+from torch import nn
+from torch import nn, optim
+from torch.utils.data import DataLoader
+
+class Model(nn.Module):
+    def __init__(self):
+        super(Model, self).__init__()
+        self.lstm = nn.LSTM(
+            input_size=8,
+            hidden_size=16,
+            num_layers=5,
+            dropout=0.01,
+        )
+        self.fc = nn.Linear(16, 1)
+        self.out = nn.Sigmoid()
+
+    def forward(self, x, prev_state):
+        output, state = self.lstm(x, prev_state)
+        logits = self.fc(output)
+        val = self.out(logits)
+        return val, state
+
+    def init_state(self, sequence_length):
+        return (torch.zeros(5, 1, 16),
+                torch.zeros(5, 1, 16))

train.py

@@ -7,29 +7,7 @@ import random
 import math
 
 import shark
-
+from model import Model
-class Model(nn.Module):
-    def __init__(self):
-        super(Model, self).__init__()
-        self.lstm = nn.LSTM(
-            input_size=8,
-            hidden_size=16,
-            num_layers=3,
-            dropout=0.1,
-        )
-        self.fc = nn.Linear(16, 1)
-        self.out = nn.Sigmoid()
-
-    def forward(self, x, prev_state):
-        output, state = self.lstm(x, prev_state)
-        logits = self.fc(output)
-        val = self.out(logits)
-        #print(str(logits.item())+" > "+str(val.item()))
-        return val, state
-
-    def init_state(self, sequence_length):
-        return (torch.zeros(3, 1, 16),
-                torch.zeros(3, 1, 16))
 
 def train(model, seq_len=16*64):
     tid = str(int(random.random()*99999)).zfill(5)