Added 'Python/nnadd.py'

Python/nnadd.py

@@ -0,0 +1,60 @@
+#!/bin/python3
+import numpy as np
+from tensorflow import keras
+
+# ---------------
+# --- Dataset ---
+# ---------------
+def gen_data():
+	# Addition in the finite field of 0..10
+	r = {'in': [], 'out': []}
+	for i in range(10):
+		for h in range(10):
+			r['in'].append((i, h))
+			r['out'].append((i + h) % 10)
+	r['in'] = np.array(r['in'])
+	r['out'] = np.array(r['out'])
+	return r
+dataset = gen_data()
+
+# -------------
+# --- Model ---
+# -------------
+model = keras.Sequential()	# Stock feedforward network
+
+hidden_layers = [2, 8, 4, 10, 8] # Overkill is the best kind of kill
+for i in hidden_layers: model.add(keras.layers.Dense(i, activation='relu'))
+model.add(keras.layers.Dense(1))	# output layer
+
+model.compile(
+	optimizer='adam',
+	loss='mse',
+	metrics=['accuracy']
+)
+
+# Training
+model.fit(dataset['in'], dataset['out'],
+	verbose=2,
+	batch_size=10,
+	epochs=1000,	# Repetition count on the whole dataset
+	shuffle=True,
+)
+
+# ------------------------------
+# --- Interactive playground ---
+# ------------------------------
+#NOTE: importing will work too
+def main():
+	while True:
+		try:
+			a = int(input("Enter the first integer (a): "))
+			b = int(input("Enter the second integer (b): "))
+			
+			r1 = model.predict(np.array([(a, b)]))[0][0]
+			r2 = np.round(r1)
+			print(f"The sum of {a} and {b} is {r2} ({r1})")
+		except ValueError:
+			pass
+
+if __name__ == '__main__':
+	main()