Python Keras：将现有权重加载到Batchnormalization层中

我在

.h5

文件中得到了一个完全卷积的预训练模型。 现在我想更改输入分辨率并再次训练

我目前的方法是遍历所有层，创建一个新层并指定预训练权重

以下是一个最小的示例：

from keras.layers import Input
from keras.layers import BatchNormalization
from keras.layers.convolutional import Conv2D
from keras.models import Model

# this would be the pretrained model
input_layer = Input((10, 10, 3))
conv = Conv2D(16, 3)(input_layer)
bnorm = BatchNormalization()(conv)
model = Model(inputs = input_layer, outputs = bnorm)


# now I want to create a new model with the same architecture but different sizes
new_input = Input((100,100,3))
prev_layer = new_input

for old_layer in model.layers[1:]:
    weights = old_layer.weights

    if type(old_layer) == Conv2D:
        filters = old_layer.filters
        kernel_size = old_layer.kernel_size

        conv_layer = Conv2D(filters = filters, 
                           kernel_size = kernel_size,
                           )(prev_layer)
        prev_layer = conv_layer

    elif type(old_layer) == BatchNormalization:
        bn_layer = BatchNormalization(weights=weights)
        prev_layer = bn_layer(prev_layer)

批处理规范化的代码失败。错误消息相当长，关键问题似乎是：

ValueError:形状的秩必须相等，但其值为1和0 带有输入形状[16]的“批处理规格化3/Assign”（op:“Assign”）， […]

完整的错误消息位于pastebin上：

如果我删除batchnormalization构造函数中的weights参数，代码就可以正常工作。 我已经查看了我试图在构造函数中提供的权重，以及在没有提供权重的情况下分配的权重。形状相同

[<tf.Variable 'batch_normalization_1/gamma:0' shape=(16,) dtype=float32_ref>,
 <tf.Variable 'batch_normalization_1/beta:0' shape=(16,) dtype=float32_ref>,
 <tf.Variable 'batch_normalization_1/moving_mean:0' shape=(16,) dtype=float32_ref>,
 <tf.Variable 'batch_normalization_1/moving_variance:0' shape=(16,) dtype=float32_ref>]

[,，
,
,
]

如何将权重加载到批处理中