diff --git a/lab_1.py b/lab_1.py
index 3a04dec..30fbdb4 100644
--- a/lab_1.py
+++ b/lab_1.py
@@ -1,17 +1,37 @@
 import numpy as np
 from tensorflow.keras import Sequential, Input, layers, optimizers
 
-x = np.array([[0, 0, 0], [0, 0, 1], [0, 1, 0], [1, 0, 0], [0, 1, 1], [1, 0, 1], [1, 1, 0], [1, 1, 1]])
-y = np.array([0, 1, 1, 1, 0, 0, 0, 1])
+x = np.array([
+    [0, 0, 0, 0],
+    [0, 0, 0, 1],
+    [0, 0, 1, 0],
+    [0, 0, 1, 1],
+
+    [0, 1, 0, 0],
+    [0, 1, 0, 1],
+    [0, 1, 1, 0],
+    [0, 1, 1, 1],
+
+    [1, 0, 0, 0],
+    [1, 0, 0, 1],
+    [1, 0, 1, 0],
+    [1, 0, 1, 1],
+
+    [1, 1, 0, 0],
+    [1, 1, 0, 1],
+    [1, 1, 1, 0],
+    [1, 1, 1, 1],
+])
+y = np.array([0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0])
 
 model = Sequential([
-    Input(shape=(3,)),
+    Input(shape=(4,)),
     layers.Dense(3, activation="tanh"),
     layers.Dense(1, activation="sigmoid"),
 ])
 
 model.compile(optimizer=optimizers.Adam(learning_rate=0.05), loss="binary_crossentropy", metrics=["accuracy"])
-model.fit(x, y, epochs=100)
+model.fit(x, y, epochs=200)
 
 loss, accuracy = model.evaluate(x, y)
 print(f"Loss: {loss}")
@@ -19,4 +39,4 @@ print(f"Accuracy: {accuracy}")
 
 prediction = model.predict(x)
 for inp, pred in zip(x, prediction):
-    print(inp, round(pred[0]))
\ No newline at end of file
+    print(inp, round(pred[0]))