python valueerror函数使用_python – ValueError：Model的输出张量必须是...

我正在使用最后一层中的一些tensorflow函数(reduce_sum和l2_normalize)在Keras中构建模型,同时遇到此问题.我已经搜索了一个解决方案,但所有这些都与“Keras tensor”有关.

这是我的代码：

import tensorflow as tf;

from tensorflow.python.keras import backend as K

vgg16_model = VGG16(weights = 'imagenet', include_top = False, input_shape = input_shape);

fire8 = extract_layer_from_model(vgg16_model, layer_name = 'block4_pool');

pool8 = MaxPooling2D((3,3), strides = (2,2), name = 'pool8')(fire8.output);

fc1 = Conv2D(64, (6,6), strides= (1, 1), padding = 'same', name = 'fc1')(pool8);

fc1 = Dropout(rate = 0.5)(fc1);

fc2 = Conv2D(3, (1, 1), strides = (1, 1), padding = 'same', name = 'fc2')(fc1);

fc2 = Activation('relu')(fc2);

fc2 = Conv2D(3, (15, 15), padding = 'valid', name = 'fc_pooling')(fc2);

fc2_norm = K.l2_normalize(fc2, axis = 3);

est = tf.reduce_sum(fc2_norm, axis = (1, 2));

est = K.l2_normalize(est);

FC_model = Model(inputs = vgg16_model.input, outputs = est);

然后是错误：

ValueError: Output tensors to a Model must be the output of a

TensorFlow Layer (thus holding past layer metadata). Found:

Tensor(“l2_normalize_3:0”, shape=(?, 3), dtype=float32)

我注意到没有将fc2层传递给这些函数,模型工作正常：

FC_model = Model(inputs = vgg16_model.input, outputs = fc2);

有人可以向我解释这个问题以及如何解决这个问题的一些建议吗？