我正在按照此处的说明创建一个自定义损失函数。当我添加validation_data时，我在ValueError上收到一条错误消息。当我将validation_data设置为None时，此错误将消失。我在Stackoverflow上发现了一个类似的问题，但我认为我的问题不同，因为我正在尝试使用自定义丢失函数。
这是我的代码：
from tensorflow.keras.layers import *
from tensorflow.keras.models import Model
import numpy as np
import tensorflow.keras.backend as K
from tensorflow.keras import regularizers
def loss_fcn(y_true, y_pred, w):
loss = K.mean(K.square((y_true-y_pred)*w))
return loss
# since tensor flow sets the batch_size default to 32. The number of samples have to be a multiple of 32 when it is great than 32.
data_x = np.random.rand(32, 51)
data_w = np.random.rand(32, 5)
data_y = np.random.rand(32, 5)
val_x = np.random.rand(4, 51)
val_w = np.random.rand(4, 5)
val_y = np.random.rand(4, 5)
input_x = Input(shape=(51,), name="input")
y_true = Input(shape=(5,), name="true_y")
w = Input(shape=(5,), name="weights")
out = Dense(128, kernel_regularizer=regularizers.l2(0.001), name="HL1")(input_x)
y = Dense(5, name="HL2", activation="tanh")(out)
model = Model(inputs=[input_x, y_true, w], outputs=y)
model.add_loss(loss_fcn(y_true, y, w))
model.compile()
model.fit((data_x, data_y, data_w), validation_data=(val_x, val_y, val_w))
错误消息：
ValueError: Error when checking model input: the list of Numpy arrays that you are passing to your model is not the size the model expected. Expected to see 3 array(s), but instead got the following list of 1 arrays: [array([[0.74785946, 0.63599707, 0.45929641, 0.98855504, 0.84815295,
0.28217452, 0.93502174, 0.23942027, 0.11885888, 0.32092279,
0.47407394, 0.19737623, 0.85962504, 0.35906666, 0.22262...
不要使用元组，而是将训练和验证数据作为列表：
model.fit([data_x, data_y, data_w], validation_data=[val_x, val_y, val_w])
您的模型有3个输入和一个输出。
模型拟合的论据应该是：
- x=3个张量/数组的列表(或元组(
- y=输出值
- validation_data=元组(3个输入、输出值的列表(