📊 tensorflow로 회귀식 구하기

📌 tensorflow로 회귀식 구하기

1. 랜덤 가중치, 편향값 추출

tf.random.set_seed(2)
w = tf.Variable(tf.random.normal((1,)))
b = tf.Variable(tf.random.normal((1,)))
print(w.numpy(), ' ', b.numpy())
# [0.7488816]   [0.899998]

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2. 옵티마이저 선택

from keras.optimizers import SGD, RMSprop, Adam

opti = SGD()

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3. 최적의 회귀선 구하기

@tf.function
def train_func(x, y):
    with tf.GradientTape() as tape:
        hypo = tf.add(tf.multiply(w, x), b)     # y = w*x + b
        loss = tf.reduce_mean(tf.square(hypo - y))

    grad = tape.gradient(loss, [w, b])      # 미분을 자동으로 계산
    opti.apply_gradients(zip(grad, [w, b])) # 자동 경사하강법 계산
    return loss

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4. 각 epoch마다 loss값 구하기

w_vals = []
cost_vals = []
epochs = 100
for i in range(1, epochs+1):
    loss_val = train_func(x, y)
    cost_vals.append(loss_val.numpy())
    w_vals.append(w.numpy())
    if i % 10 == 0:
        print(loss_val)

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• 5. w, cost를 시각화

plt.plot(w_vals, cost_vals, 'o--')
plt.xlabel('weight')
plt.ylabel('cost')
plt.show()

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• 6. 선형회귀식 시각화

plt.scatter(x, y, label='real')
plt.plot(x, y_pred, '--', c='b', label='pred')
plt.xlabel('x')
plt.ylabel('y')
plt.legend()
plt.show()