Нейросеть на keras и tensorflow

import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
import tensorflow as tf
from tensorflow import keras
import numpy as np
import matplotlib.pyplot as plt
from tensorflow import keras
from tensorflow.keras.layers import Dense
 
c = np.array([[2, 4, 5, 9, 14, 21, 22, 27, 29, 39, 41, 42, 43, 51, 54, 60, 71, 72, 74, 76],
             [2, 9, 11, 18, 21, 30, 33, 36, 42, 45, 49, 59, 62, 63, 64, 72, 73, 75, 76, 80],
             [1, 2, 3, 4, 13, 17, 18, 20, 26, 30, 33, 34, 44, 45, 48, 55, 68, 69, 72, 76],
             [3, 6, 7, 12, 19, 20, 29, 30, 32, 42, 44, 49, 62, 64, 67, 70, 75, 76, 78, 79],
             [1, 4, 8, 19, 20, 26, 37, 38, 39, 40, 44, 46, 50, 55, 61, 66, 67, 68, 71, 78],
             [3, 4, 10, 13, 14, 16, 18, 19, 21, 24, 28, 29, 48, 52, 56, 60, 68, 70, 72, 80],
             [3, 5, 7, 8, 12, 14, 21, 27, 30, 41, 42, 50, 56, 57, 64, 67, 68, 70, 76, 80],
             [6, 11, 12, 13, 20, 28, 31, 38, 41, 42, 43, 44, 50, 65, 66, 67, 68, 72, 74, 75],
             [3, 5, 8, 13, 19, 20, 21, 24, 30, 36, 37, 38, 40, 44, 51, 55, 63, 64, 75, 78],
             [5, 7, 12, 16, 19, 24, 25, 31, 36, 39, 40, 47, 50, 53, 56, 57, 58, 59, 69, 75],
             [1, 2, 5, 9, 11, 12, 16, 20, 23, 29, 31, 34, 38, 44, 47, 52, 60, 62, 70, 79],
             [1, 11, 13, 17, 25, 27, 31, 34, 36, 39, 40, 44, 46, 56, 57, 66, 67, 68, 76, 80],
             [5, 9, 10, 11, 13, 19, 21, 24, 30, 32, 43, 46, 53, 55, 62, 67, 69, 72, 74, 76],
             [5, 7, 8, 14, 19, 23, 28, 31, 35, 38, 40, 43, 46, 57, 64, 68, 71, 72, 75, 78],
             [1, 7, 9, 16, 29, 33, 34, 38, 40, 41, 49, 56, 60, 65, 66, 70, 72, 74, 77, 79],
             [4, 5, 6, 11, 14, 15, 17, 19, 21, 24, 26, 30, 38, 40, 47, 57, 68, 70, 72, 76],
             [1, 3, 4, 5, 7, 8, 9, 10, 11, 13, 21, 26, 35, 38, 39, 52, 65, 69, 78, 79],
             [1, 5, 13, 15, 20, 23, 24, 25, 31, 45, 46, 49, 50, 53, 62, 67, 70, 75, 76, 80],
             [3, 4, 11, 14, 16, 21, 23, 24, 31, 34, 48, 50, 51, 53, 54, 56, 60, 66, 71, 76],
             [5, 7, 9, 11, 13, 15, 17, 26, 27, 32, 35, 36, 45, 51, 55, 60, 61, 69, 70, 80],
             [6, 10, 13, 15, 16, 24, 25, 26, 38, 41, 42, 43, 46, 48, 56, 61, 64, 68, 72, 77],
             [3, 4, 6, 7, 9, 17, 25, 26, 31, 34, 36, 55, 58, 65, 67, 69, 70, 71, 72, 73],
             [1, 9, 11, 13, 23, 24, 25, 28, 32, 35, 38, 40, 41, 47, 61, 65, 70, 73, 77, 79],
             [3, 15, 17, 29, 33, 38, 39, 41, 49, 50, 54, 58, 60, 63, 67, 70, 72, 75, 79, 80],
             [1, 3, 5, 6, 15, 18, 28, 34, 37, 39, 40, 43, 53, 54, 60, 64, 67, 71, 72, 75],
             [4, 5, 16, 18, 19, 21, 27, 28, 36, 39, 40, 41, 42, 51, 52, 54, 61, 62, 65, 71]])
 
 
f = np.array([[2, 72, 9, 42, 76, 21], [33, 2, 72, 76, 45, 18, 30], [3, 76, 44, 20, 30], [67, 44, 78, 19, 20], [19, 4, 68], [3, 68, 70, 14, 80, 21, 56], [67, 68, 41, 42, 12, 50], [38, 75, 44, 13, 20], [36, 5, 40, 75, 19, 24], [5, 12, 47, 16, 31], [1, 34, 11, 44, 31], [67, 11, 76, 13, 46], [5, 72, 43, 46, 19], [40, 72, 38, 7], [40, 70, 72, 38], [4, 5, 38, 11, 21, 26], [1, 13, 5], [76, 50, 53, 23, 24, 31], [51, 11, 60], [26, 61, 13, 15], [72, 25, 26, 6], [65, 70, 9, 73, 25], [41, 79, 38, 70], [3, 67, 39, 72, 75, 15, 54, 60], [5, 39, 40, 71, 18, 54, 28]])
 
model = keras.Sequential()
 
model.add(Dense(units=20, input_shape=(25,), activation='linear'))
 
model.compile(loss='mean_squared_error', optimizer=keras.optimizers.Adam(0.1))
 
log = model.fit(c, f, epochs=500, verbose=False)
 
 
plt.plot(log.history['loss'])
plt.grid(True)
plt.show()
 
print(model.predict([4, 12, 14, 35, 38, 39, 41, 42, 45, 46, 47, 49, 54, 55, 61, 62, 64, 65, 73, 80]))

import os
 
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
import tensorflow as tf
from tensorflow import keras
import numpy as np
import matplotlib.pyplot as plt
from tensorflow import keras
from tensorflow.keras.layers import Dense
from collections import deque
import re
 
len_numbers = 1000
with open("result.txt") as f:
    text, a, b = [], [], []
    for row_txt in deque(f, len_numbers):
        row_lst = [int(x) for x in re.findall('\d+', row_txt)]
        a.append(row_lst)
a = a[:-1]
 
with open("result.txt") as f:
    for row_txt in deque(f, (len_numbers - 1)):
        row_lst = [int(x) for x in re.findall('\d+', row_txt)]
        b.append(row_lst)
 
a1 = np.array(a)
b1 = np.array(b)
 
model = keras.Sequential()
 
model.add(Dense(units=20, input_shape=(20,), activation='linear'))
 
model.compile(loss='mean_squared_error', optimizer=keras.optimizers.Adam(0.1))
 
log = model.fit(a1, b1, epochs=500, verbose=False)
 
plt.plot(log.history['loss'])
plt.grid(True)
# plt.show()
 
print(model.predict([[4, 12, 14, 35, 38, 39, 41, 42, 45, 46, 47, 49, 54, 55, 61, 62, 64, 65, 73, 80]]))