commit c256f2913f4f21db9bfe3908bcd2df4705e3111b
Author: hasslesstech <hasslesstech@tutanota.com>
Date:   Fri Sep 12 18:53:55 2025 +0300

    initial commit

diff --git a/train.py b/train.py
new file mode 100644
index 0000000..146dce3
--- /dev/null
+++ b/train.py
@@ -0,0 +1,39 @@
+import tensorflow as tf
+import numpy as np
+
+x = np.array([
+    [0, 0, 0],
+    [0, 0, 1],
+    [0, 1, 0],
+    [0, 1, 1],
+    [1, 0, 0],
+    [1, 0, 1],
+    [1, 1, 0],
+    [1, 1, 1],
+])
+
+y = np.array([sum(i) % 2 for i in x])
+
+model = tf.keras.Sequential([
+    tf.keras.layers.Input(shape = (3,)),
+    tf.keras.layers.Dense(3, activation = "tanh"),
+    tf.keras.layers.Dense(1, activation = "sigmoid")
+])
+
+model.compile(
+    optimizer = tf.keras.optimizers.Adam(learning_rate = 0.05),
+    loss = "binary_crossentropy",
+    metrics = ["accuracy"]
+)
+
+for _ in range(100):
+    model.fit(x, y, epochs = 10, verbose = 0)
+    loss, accuracy = model.evaluate(x, y, verbose = 0)
+
+    print(f"\rAchieved accuracy={accuracy}, loss={loss}", end = '')
+
+    if accuracy == 1.0:
+        print()
+        break
+
+model.save_weights("mod1_final.weights.h5")
diff --git a/verify.py b/verify.py
new file mode 100644
index 0000000..2c60208
--- /dev/null
+++ b/verify.py
@@ -0,0 +1,34 @@
+import tensorflow as tf
+import numpy as np
+
+x = np.array([
+    [0, 0, 0],
+    [0, 0, 1],
+    [0, 1, 0],
+    [0, 1, 1],
+    [1, 0, 0],
+    [1, 0, 1],
+    [1, 1, 0],
+    [1, 1, 1],
+])
+
+y = np.array([sum(i) % 2 for i in x])
+
+model = tf.keras.Sequential([
+    tf.keras.layers.Input(shape = (3,)),
+    tf.keras.layers.Dense(3, activation = "tanh"),
+    tf.keras.layers.Dense(1, activation = "sigmoid")
+])
+
+model.compile(
+    optimizer = tf.keras.optimizers.Adam(learning_rate = 0.05),
+    loss = "binary_crossentropy",
+    metrics = ["accuracy"]
+)
+
+model.load_weights("mod1_final.weights.h5")
+
+prediction = model.predict(x)
+
+for i, o, t in zip(x, prediction, y):
+    print(f"i = {i}, o = {round(o[0])}, t = {t} : {'Good' if t == round(o[0]) else 'Bad'}")