如何将keras预训练模型输入计算图？

Question

最近，我一直在玩用TF Keras编写的CNN。 不幸的是，我在这里遇到了一个问题：

在我极力推荐的这些宏伟教程（ https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/vgg16.py ;其余代码在这里）中，大师加载了预先训练的vgg16.tfmodel以非常非常难看的方式

def __init__(self):
    # Now load the model from file. The way TensorFlow
    # does this is confusing and requires several steps.

    # Create a new TensorFlow computational graph.
    self.graph = tf.Graph()

    # Set the new graph as the default.
    with self.graph.as_default():

        # TensorFlow graphs are saved to disk as so-called Protocol Buffers
        # aka. proto-bufs which is a file-format that works on multiple
        # platforms. In this case it is saved as a binary file.

        # Open the graph-def file for binary reading.
        path = os.path.join(data_dir, path_graph_def)
        with tf.gfile.FastGFile(path, 'rb') as file:
            # The graph-def is a saved copy of a TensorFlow graph.
            # First we need to create an empty graph-def.
            graph_def = tf.GraphDef()

            # Then we load the proto-buf file into the graph-def.
            graph_def.ParseFromString(file.read())

            # Finally we import the graph-def to the default TensorFlow graph.
            tf.import_graph_def(graph_def, name='')

            # Now self.graph holds the VGG16 model from the proto-buf file.

        # Get a reference to the tensor for inputting images to the graph.
        self.input = self.graph.get_tensor_by_name(self.tensor_name_input_image)

        # Get references to the tensors for the commonly used layers.
        self.layer_tensors = [self.graph.get_tensor_by_name(name + ":0") for name in self.layer_names]

问题是-我希望以相同/相似的方式加载自己的预训练模型，因此我可以将模型放入我稍后要调用的类的图中，并在可能的情况下使代码的最后几行在这里工作（意思是从图中获取所需层的张量。）

我所有的尝试都是基于从keras和comp导入的load_model 。 图使我失败。 另外，我也不想以完全不同的方式加载它，因为之后我将不得不更改很多代码-对于新手来说是一个大问题。

好的，我希望这个问题能找到合适的人，并且对您来说不是太琐碎：D。

顺便说一句：我要解决的复杂问题，就是在同一个github存储库中进行样式转换 ，以供您制作图片。 （ https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/15_Style_Transfer.ipynb ）

Answer 1

所以你想基本上将keras模型加载到tensorflow中吗？ 可以使用以下代码轻松完成：

import keras.backend as k
from keras.models import load_model
import tensorflow as tf

model = load_model("your model.h5")  # now it's in the memory of keras
with k.get_session() as sess:
    # here you have a tensorflow computational graph, view it by:
    tf.summary.FileWriter("folder name", sess.graph)

    # if you need a certain tensor do:
    sess.graph.get_tensor_by_name("tensor name") 

要了解有关get_session函数的一些信息， 请单击此处。

要查看图形，您需要使用tensorboard从FileWriter加载文件夹， 如下所示 ：

tensorboard --logdir path/to/folder

希望这能提供一些帮助，祝您好运！