我正在使用TensorFlow 2.2和Keras训练二进制检测架构。以前，如果我在训练模型的同一脚本中加载数据，我就可以正常工作。但是，当我使用更大的数据集时，（多6个样本，阳性与阴性样本的比例相同），我现在得到了这组误差（在给出此误差之前，它运行了几个时间点5-10（我运行了多次））：

tensorflow.python.framework.errors_impl.InvalidArgumentError: 2 root error(s) found.
  (0) Invalid argument:  assertion failed: [predictions must be >= 0] [Condition x >= y did not hold element-wise:] [x (dense_1/Sigmoid:0) = ] [[[nan][nan][nan]]...] [y (Cast_4/x:0) = ] [0]
     [[{{node assert_greater_equal/Assert/AssertGuard/else/_1/Assert}}]]
     [[gradient_tape/point_conv_fp_1/ScatterNd/_192]]
  (1) Invalid argument:  assertion failed: [predictions must be >= 0] [Condition x >= y did not hold element-wise:] [x (dense_1/Sigmoid:0) = ] [[[nan][nan][nan]]...] [y (Cast_4/x:0) = ] [0]
     [[{{node assert_greater_equal/Assert/AssertGuard/else/_1/Assert}}]]
0 successful operations.
0 derived errors ignored. [Op:__inference_train_function_14820]

架构如下：
Architecture
下面是与出现错误的层相关的代码：

# initialisation
..
# point_conv_sa layers
..
self.dense4 = keras.layers.Dense(128, activation=tf.nn.elu)
self.bn4 = keras.layers.BatchNormalization()
self.dropout4 = keras.layers.Dropout(0.5)

# This line corresponds to 'dense_1' in the image
self.dense_fin = keras.layers.Dense(self.num_classes, activation=tf.nn.sigmoid, bias_initializer=self.initial_bias)

# training step
..
# point_conv_fp layers
..
net = self.dense4(points)
net = self.bn4(net)
net = self.dropout4(net)

pred = self.dense_fin(net)

return pred

这和我使用的损失函数有关吗？我使用keras. loss.BinaryCrossentropy（），大小数据集都没有问题。然后我改为基于https://github.com/mkocabas/focal-loss-keras的焦点损失，大数据集失败：

def focal_loss(gamma=2., alpha=.25):
    def focal_loss_fixed(y_true, y_pred):
        pt_1 = tf.where(tf.equal(y_true, 1), y_pred, tf.ones_like(y_pred))
        pt_0 = tf.where(tf.equal(y_true, 0), y_pred, tf.zeros_like(y_pred))
        return -K.mean(alpha * K.pow(1. - pt_1, gamma) * K.log(pt_1)) - K.mean((1 - alpha) * K.pow(pt_0, gamma) * K.log(1. - pt_0))
    return focal_loss_fixed

....
model.compile(
optimizer=keras.optimizers.Adam(config['lr']),
loss = focal_loss(alpha=config['fl_alpha'], gamma=config['fl_gamma']),
metrics=[Precision(),
         Recall(), 
         AUC()]
)

如果需要更多信息，请告诉我。
干杯