diff --git a/em.pth b/em.pth
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Binary files /dev/null and b/em.pth differ
diff --git a/load_estimation_model.py b/load_estimation_model.py
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--- /dev/null
+++ b/load_estimation_model.py
@@ -0,0 +1,58 @@
+import torch
+import torch.nn as nn
+from functools import reduce
+
+class LambdaBase(nn.Sequential):
+    def __init__(self, fn, *args):
+        super(LambdaBase, self).__init__(*args)
+        self.lambda_func = fn
+
+    def forward_prepare(self, input):
+        output = []
+        for module in self._modules.values():
+            output.append(module(input))
+        return output if output else input
+
+class Lambda(LambdaBase):
+    def forward(self, input):
+        return self.lambda_func(self.forward_prepare(input))
+
+class LambdaMap(LambdaBase):
+    def forward(self, input):
+        return list(map(self.lambda_func,self.forward_prepare(input)))
+
+class LambdaReduce(LambdaBase):
+    def forward(self, input):
+        return reduce(self.lambda_func,self.forward_prepare(input))
+
+
+def load_estimation_model(inputfilename, outputfilename):
+	with open(inputfilename, "r") as rf:
+        	contents = rf.read()
+        	contents = contents.split(",")
+
+	data = torch.Tensor(1,350)
+	name = ""
+	for i in range(len(contents)):
+        	if i == 0:
+                	name = contents[i].strip()
+        	else:
+                	val = float(contents[i].strip())
+                	data[0][i-1] = val
+
+	model = nn.Sequential( # Sequential,
+        	nn.Sequential(Lambda(lambda x: x.view(1,-1) if 1==len(x.size()) else x ),nn.Linear(350,1024)), # Linear,
+       		nn.Sigmoid(),
+        	nn.Sequential(Lambda(lambda x: x.view(1,-1) if 1==len(x.size()) else x ),nn.Linear(1024,512)), # Linear,
+        	nn.Sigmoid(),
+        	nn.Sequential(Lambda(lambda x: x.view(1,-1) if 1==len(x.size()) else x ),nn.Linear(512,256)), # Linear,
+        	nn.Sigmoid(),
+        	nn.Sequential(Lambda(lambda x: x.view(1,-1) if 1==len(x.size()) else x ),nn.Linear(256,4)), # Linear,
+	)
+
+	model.load_state_dict(torch.load("em.pth"))
+	my_prediction = model.forward(data)
+
+	with open(outputfilename, "w") as wf:
+		wf.write("NAME,F1,F2,F3,F4\n")
+		wf.write(name + "," + str(1000 * float(my_prediction[0][0])) + "," + str(1000 * float(my_prediction[0][1])) + "," + str(1000 * float(my_prediction[0][2])) + "," + str(1000 * float(my_prediction[0][3])))