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lab 1: 4 variables fix

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rhinemann 2025-09-12 19:04:16 +03:00
parent ea3668ea54
commit d5017dc61a
1 changed files with 25 additions and 5 deletions
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30
lab_1.py
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@ -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]))
print(inp, round(pred[0]))