Question

我试图将预处理层添加到预先训练好的网络中。这是我正在处理的代码：

orig_model = applications.vgg16.VGG16(include_top=True, weights=None, input_tensor=None, input_shape=None, pooling=None, classes=1000)
orig_model.load_weights(weights_path)

preproc_layer = Lambda(preprocess, input_shape=(3,224,224), output_shape=(3,224,224))

model = Sequential()
model.add(preproc_layer)

all_layers = orig_model.layers
for l in all_layers:
    config = l.get_config()
    copy = layers.deserialize({'class_name':l.__class__.__name__, 'config': config})
    weights = l.get_weights()
    copy.set_weights(weights)
    model.add(copy)

预处理的地方是：

preprocess(x):
    x = x[::-1, ...]
    x = K.bias_add(x, vgg_mean, data_format='channels_first')

适用于第一个InputLayer，但在第copy.set_weights(weights)个图层Conv2D时会出现错误：

 You called `set_weights(weights)` on layer "block1_conv1" with a  weight list of length 2, but the layer was expecting 0 weights.

我在Google上发现了类似的内容：https://github.com/keras-team/keras/issues/4812。在这里，他们建议为图层设置trainable = True，但这在我的情况下不起作用。

你有什么建议吗？ Keras版本是2.1.5，Tensorflow 1.6.0

Keras + TensorFlow将处理层添加到训练有素的网络

0 个答案: