Keras/TensorFlow中的自定义损失功能

Question

我正在尝试创建一个无监督的神经网络，该网络可以建模此功能：F（x1，x2）= x1+x2^2。为此，我需要一个自定义损失功能。这是我所拥有的：

import keras
import numpy as np
import pandas as pd
import tensorflow as tf

def custom_loss(X1,X2):
    def loss(y_true,y_pred):
        # I don't have any values for y_true
        z = tf.math.add(X1,tf.math.multiply(X2,X2))
        return tf.math.square(tf.math.subtract(z,y_pred))
        # The "loss" should be [(X1+X2^2)-y_pred]^2
    return loss

df = pd.read_csv('inputs.csv')
INPUT = df.drop(columns=['OUTPUT']).astype(np.float32)
# "INPUT" contains the values for X1 and X2
X1 = df['X1'].astype(np.float32)
# "X1" contains the values for X1 only
X2 = df['X2'].astype(np.float32)
# "X2" contains the values for X2 only
y_true = df['OUTPUT'].astype(np.float32)
# I don't have a y_true. So I made an extra column full of zeros.

model = keras.models.Sequential()
model.add(keras.layers.Dense(20, activation='tanh', input_shape=(2,)))
model.add(keras.layers.Dense(20, activation='tanh'))
model.add(keras.layers.Dense(1, activation='tanh'))

model.compile(optimizer='adam', loss=custom_loss(X1, X2))
model.fit(INPUT, y_true, epochs=400)

test_data = np.array([0.5, 0.6])
print(model.predict(test_data.reshape(1, 2), batch_size=1))

如果代码正确，测试输出应为0.5+（0.6）^2 = 0.86，但完全关闭。我为我的编程差而道歉。我仍然没有经验。非常感谢您的任何帮助。