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import numpy as np
from keras.models import Sequential
from keras.layers import Dense
 
# Генерация данных
X = np.random.rand(1000, 2) * 100  # Два входных числа
y = np.sum(X, axis=1)  # Сумма двух чисел
 
# Создание модели
model = Sequential([
    Dense(10, activation='relu', input_dim=2),  # Скрытый слой
    Dense(1)  # Выходной слой
])
 
# Компиляция модели
model.compile(optimizer='adam', loss='mse', metrics=['mae'])
 
# Обучение модели
model.fit(X, y, epochs=50, batch_size=32, verbose=1)
 
# Тестирование модели
test_data = np.array([[10, 20], [30, 40]])  # Тестовые данные
predictions = model.predict(test_data)
print("Входные данные:", test_data)
print("Предсказания суммы:", predictions)

Success #stdin #stdout 3.89s 348820KB

stdin

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Standard input is empty

stdout

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Epoch 1/50

 1/32 [..............................] - ETA: 12s - loss: 17994.6836 - mae: 123.9936
32/32 [==============================] - 0s 992us/step - loss: 15043.3994 - mae: 113.7642
Epoch 2/50

 1/32 [..............................] - ETA: 0s - loss: 12715.4141 - mae: 106.2181
32/32 [==============================] - 0s 745us/step - loss: 12456.8154 - mae: 103.3911
Epoch 3/50

 1/32 [..............................] - ETA: 0s - loss: 12651.3545 - mae: 102.6296
32/32 [==============================] - 0s 768us/step - loss: 10291.4541 - mae: 93.6963
Epoch 4/50

 1/32 [..............................] - ETA: 0s - loss: 7915.7515 - mae: 80.7487
32/32 [==============================] - 0s 775us/step - loss: 8356.4893 - mae: 84.0192
Epoch 5/50

 1/32 [..............................] - ETA: 0s - loss: 7286.2598 - mae: 78.4910
32/32 [==============================] - 0s 748us/step - loss: 6445.7681 - mae: 73.2368
Epoch 6/50

 1/32 [..............................] - ETA: 0s - loss: 4720.6709 - mae: 62.4819
32/32 [==============================] - 0s 886us/step - loss: 4527.2329 - mae: 60.6766
Epoch 7/50

 1/32 [..............................] - ETA: 0s - loss: 3132.5527 - mae: 50.9019
32/32 [==============================] - 0s 749us/step - loss: 2977.2766 - mae: 48.3118
Epoch 8/50

 1/32 [..............................] - ETA: 0s - loss: 2079.7888 - mae: 38.7474
32/32 [==============================] - 0s 742us/step - loss: 1864.7711 - mae: 36.9307
Epoch 9/50

 1/32 [..............................] - ETA: 0s - loss: 1695.5894 - mae: 35.7451
32/32 [==============================] - 0s 765us/step - loss: 1132.0267 - mae: 27.6227
Epoch 10/50

 1/32 [..............................] - ETA: 0s - loss: 1061.7100 - mae: 26.5246
32/32 [==============================] - 0s 828us/step - loss: 704.4185 - mae: 21.1626
Epoch 11/50

 1/32 [..............................] - ETA: 0s - loss: 701.6003 - mae: 20.8608
32/32 [==============================] - 0s 741us/step - loss: 469.2602 - mae: 17.1379
Epoch 12/50

 1/32 [..............................] - ETA: 0s - loss: 477.9725 - mae: 17.7739
32/32 [==============================] - 0s 792us/step - loss: 343.8763 - mae: 14.8384
Epoch 13/50

 1/32 [..............................] - ETA: 0s - loss: 293.9790 - mae: 12.7346
32/32 [==============================] - 0s 722us/step - loss: 277.1958 - mae: 13.3708
Epoch 14/50

 1/32 [..............................] - ETA: 0s - loss: 380.3908 - mae: 15.8154
32/32 [==============================] - 0s 791us/step - loss: 236.1098 - mae: 12.4043
Epoch 15/50

 1/32 [..............................] - ETA: 0s - loss: 211.4024 - mae: 11.4865
32/32 [==============================] - 0s 771us/step - loss: 207.7698 - mae: 11.6930
Epoch 16/50

 1/32 [..............................] - ETA: 0s - loss: 211.9588 - mae: 12.4728
32/32 [==============================] - 0s 753us/step - loss: 184.7477 - mae: 11.0577
Epoch 17/50

 1/32 [..............................] - ETA: 0s - loss: 115.9901 - mae: 8.8368
32/32 [==============================] - 0s 748us/step - loss: 164.3435 - mae: 10.4618
Epoch 18/50

 1/32 [..............................] - ETA: 0s - loss: 167.8185 - mae: 9.6286
32/32 [==============================] - 0s 753us/step - loss: 146.5209 - mae: 9.8822
Epoch 19/50

 1/32 [..............................] - ETA: 0s - loss: 135.8714 - mae: 9.6453
32/32 [==============================] - 0s 766us/step - loss: 129.5890 - mae: 9.3017
Epoch 20/50

 1/32 [..............................] - ETA: 0s - loss: 150.2193 - mae: 9.7654
32/32 [==============================] - 0s 744us/step - loss: 114.1228 - mae: 8.7286
Epoch 21/50

 1/32 [..............................] - ETA: 0s - loss: 122.4537 - mae: 8.9737
32/32 [==============================] - 0s 759us/step - loss: 99.7815 - mae: 8.1558
Epoch 22/50

 1/32 [..............................] - ETA: 0s - loss: 79.7344 - mae: 7.6491
32/32 [==============================] - 0s 729us/step - loss: 86.7438 - mae: 7.5868
Epoch 23/50

 1/32 [..............................] - ETA: 0s - loss: 122.7631 - mae: 9.0030
32/32 [==============================] - 0s 795us/step - loss: 74.6562 - mae: 7.0145
Epoch 24/50

 1/32 [..............................] - ETA: 0s - loss: 99.3830 - mae: 8.2459
32/32 [==============================] - 0s 758us/step - loss: 63.3396 - mae: 6.4152
Epoch 25/50

 1/32 [..............................] - ETA: 0s - loss: 41.2305 - mae: 5.1827
32/32 [==============================] - 0s 761us/step - loss: 52.8558 - mae: 5.7890
Epoch 26/50

 1/32 [..............................] - ETA: 0s - loss: 47.1080 - mae: 5.5305
32/32 [==============================] - 0s 736us/step - loss: 42.9401 - mae: 5.1128
Epoch 27/50

 1/32 [..............................] - ETA: 0s - loss: 28.0925 - mae: 3.6100
32/32 [==============================] - 0s 777us/step - loss: 33.8389 - mae: 4.2919
Epoch 28/50

 1/32 [..............................] - ETA: 0s - loss: 23.1631 - mae: 3.6190
32/32 [==============================] - 0s 753us/step - loss: 26.7396 - mae: 3.6471
Epoch 29/50

 1/32 [..............................] - ETA: 0s - loss: 35.9374 - mae: 4.0932
32/32 [==============================] - 0s 755us/step - loss: 21.2876 - mae: 3.1713
Epoch 30/50

 1/32 [..............................] - ETA: 0s - loss: 16.2571 - mae: 2.6051
32/32 [==============================] - 0s 746us/step - loss: 17.1589 - mae: 2.7530
Epoch 31/50

 1/32 [..............................] - ETA: 0s - loss: 9.4529 - mae: 2.0940
32/32 [==============================] - 0s 726us/step - loss: 13.9412 - mae: 2.4092
Epoch 32/50

 1/32 [..............................] - ETA: 0s - loss: 9.8611 - mae: 1.8823
32/32 [==============================] - 0s 774us/step - loss: 11.5823 - mae: 2.1792
Epoch 33/50

 1/32 [..............................] - ETA: 0s - loss: 12.7286 - mae: 2.2153
32/32 [==============================] - 0s 753us/step - loss: 9.7004 - mae: 1.9811
Epoch 34/50

 1/32 [..............................] - ETA: 0s - loss: 3.5636 - mae: 1.3453
32/32 [==============================] - 0s 758us/step - loss: 8.3049 - mae: 1.8778
Epoch 35/50

 1/32 [..............................] - ETA: 0s - loss: 3.0787 - mae: 1.3620
32/32 [==============================] - 0s 715us/step - loss: 7.1936 - mae: 1.7975
Epoch 36/50

 1/32 [..............................] - ETA: 0s - loss: 12.4507 - mae: 2.3491
32/32 [==============================] - 0s 737us/step - loss: 6.3439 - mae: 1.7322
Epoch 37/50

 1/32 [..............................] - ETA: 0s - loss: 8.3348 - mae: 1.9189
32/32 [==============================] - 0s 750us/step - loss: 5.6764 - mae: 1.6680
Epoch 38/50

 1/32 [..............................] - ETA: 0s - loss: 6.3518 - mae: 1.8144
32/32 [==============================] - 0s 750us/step - loss: 5.1587 - mae: 1.6251
Epoch 39/50

 1/32 [..............................] - ETA: 0s - loss: 2.8955 - mae: 1.4158
32/32 [==============================] - 0s 742us/step - loss: 4.7277 - mae: 1.5947
Epoch 40/50

 1/32 [..............................] - ETA: 0s - loss: 4.2506 - mae: 1.4462
32/32 [==============================] - 0s 721us/step - loss: 4.3650 - mae: 1.5655
Epoch 41/50

 1/32 [..............................] - ETA: 0s - loss: 3.4752 - mae: 1.4980
32/32 [==============================] - 0s 763us/step - loss: 4.0796 - mae: 1.5281
Epoch 42/50

 1/32 [..............................] - ETA: 0s - loss: 6.1971 - mae: 1.7984
32/32 [==============================] - 0s 782us/step - loss: 3.8431 - mae: 1.5078
Epoch 43/50

 1/32 [..............................] - ETA: 0s - loss: 5.2304 - mae: 1.7992
32/32 [==============================] - 0s 757us/step - loss: 3.6554 - mae: 1.4820
Epoch 44/50

 1/32 [..............................] - ETA: 0s - loss: 4.3824 - mae: 1.7548
32/32 [==============================] - 0s 726us/step - loss: 3.4867 - mae: 1.4668
Epoch 45/50

 1/32 [..............................] - ETA: 0s - loss: 2.0130 - mae: 1.1654
32/32 [==============================] - 0s 730us/step - loss: 3.3444 - mae: 1.4501
Epoch 46/50

 1/32 [..............................] - ETA: 0s - loss: 2.7753 - mae: 1.4331
32/32 [==============================] - 0s 743us/step - loss: 3.2247 - mae: 1.4346
Epoch 47/50

 1/32 [..............................] - ETA: 0s - loss: 3.4632 - mae: 1.4531
32/32 [==============================] - 0s 756us/step - loss: 3.1118 - mae: 1.4156
Epoch 48/50

 1/32 [..............................] - ETA: 0s - loss: 3.8059 - mae: 1.6296
32/32 [==============================] - 0s 753us/step - loss: 3.0175 - mae: 1.4064
Epoch 49/50

 1/32 [..............................] - ETA: 0s - loss: 2.5673 - mae: 1.2272
32/32 [==============================] - 0s 720us/step - loss: 2.9358 - mae: 1.3876
Epoch 50/50

 1/32 [..............................] - ETA: 0s - loss: 3.3745 - mae: 1.4896
32/32 [==============================] - 0s 758us/step - loss: 2.8625 - mae: 1.3846
Входные данные: [[10 20]
 [30 40]]
Предсказания суммы: [[30.918484]
 [71.48034 ]]

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