cleaned up FeedForward class to pass test

classifier/nn_ff.py

@@ -24,6 +24,7 @@ class FeedForward(torch.nn.Module):
         self.fc2 = torch.nn.Linear(self.hidden_size, self.output_size)
         self.sigmoid = torch.nn.Sigmoid()
         self.softmax = torch.nn.Softmax(dim=1)
+        self.read_data()
 
     def forward(self, x):
         """ Computes output from a given input x. """
@@ -35,7 +36,8 @@ class FeedForward(torch.nn.Module):
 
     def read_data(self):
         """" Reads in training and test data and converts it to proper format. """
-        self.X_train_, self.y_train_, self.X_test_ = read_csv_nn()
+        self.X_train_, self.y_train_, self.X_test = read_csv_nn()
+        self.X_test = torch.FloatTensor(self.X_test)
         yclass = np.array([(x[1] == 1) + 2 * (x[2] == 1) for x in self.y_train_])
         is0 = yclass == 0
         is1 = yclass == 1
@@ -118,7 +120,7 @@ class FeedForward(torch.nn.Module):
         p0 = torch.randperm(self.l0)
         p1 = torch.randperm(self.l1)
         p2 = torch.randperm(self.l2)
-        n = self.l0 + self.l1 + self.l2
+        n = self.samples0 + self.samples1 + self.samples2
         p = torch.randperm(n)
 
         # sample and shuffle data

testing/ff_model_testing.py

@@ -1,3 +1,6 @@
+import sys
+import os
+sys.path.append(os.getcwd())
 from classifier.nn_ff import FeedForward