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

Question

我在 Keras 中使用一些张量流函数（reduce_sum 和 l2_normalize）在最后一层构建模型时遇到了这个问题。我已经搜索了一个解决方案，但所有解决方案都与 "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)

我注意到在没有将 fc2 层传递给这些函数的情况下，该模型工作正常：

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

有人可以向我解释这个问题并提供一些解决方法吗？

Answer 1

我找到了解决问题的方法。对于遇到相同问题的任何人，您可以使用 Lambda 层来包装您的 tensorflow 操作，这就是我所做的：

from tensorflow.python.keras.layers import Lambda;

def norm(fc2):

    fc2_norm = K.l2_normalize(fc2, axis = 3);
    illum_est = tf.reduce_sum(fc2_norm, axis = (1, 2));
    illum_est = K.l2_normalize(illum_est);

    return illum_est;

illum_est = Lambda(norm)(fc2);

Answer 2

我遇到这个问题是因为我在模型中的某处添加了 2 个张量作为 x1+x2，而不是使用 Add()([x1,x2])。

问题解决了。

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

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

python

machine-learning

keras

tensorflow

tensor