Out files with prediction results

main.py

@@ -47,9 +47,6 @@ def read_data():
 
 x_labels, y_labels, x_train, y_train, x_dev, x_test = read_data()
 
-print(len(y_train))
-print(len(x_train))
-
 x_train = x_train[x_labels[0]].str.lower()
 x_dev = x_dev[x_labels[0]].str.lower()
 x_test = x_test[x_labels[0]].str.lower()
@@ -88,5 +85,35 @@ for epoch in range(5):
         loss.backward()
         optimizer.step()
 
+y_dev = []
+y_test = []
 
-print(Y_predictions)
+nn_model.eval()
+
+with torch.no_grad():
+    for i in range(0, len(x_dev), BATCH_SIZE):
+        X = x_dev[i:i+BATCH_SIZE]
+        X = torch.tensor(X)
+
+        outputs = nn_model(X.float())
+
+        y = (outputs > 0.5)
+        y_dev.extend(y)
+
+    for i in range(0, len(x_test), BATCH_SIZE):
+        X = x_test[i:i+BATCH_SIZE]
+        X = torch.tensor(X)
+
+        outputs = nn_model(X.float())
+
+        y = (outputs > 0.5)
+        y_test.extend(y)
+
+y_dev = np.asarray(y_dev, dtype=np.int32)
+y_test = np.asarray(y_test, dtype=np.int32)
+
+Y_dev = pd.DataFrame({'label': y_dev})
+Y_test = pd.DataFrame({'label': y_test})
+
+Y_dev.to_csv(r'dev-0/out.tsv', sep='\t', index=False,  header=False)
+Y_test.to_csv(r'test-A/out.tsv', sep='\t', index=False,  header=False)