Question

我正在尝试在同一Python文件中使用策略网络和CNN。我只训练PN，并将CNN用作前馈特征提取器。我遇到一个问题，即Train-PC不在会话中“构建”导入的Keras图，因此当我尝试在CNN（feature_vector = self.extract_feature_vector([X_img_norm])[0]）上运行前馈功能时，错误发布如下。

我正在开发的家用PC和Train-PC上的文件之间的唯一区别是我导入模型的方式，我认为这可能是导致这种情况的原因（可能无法识别）。我以前发布过有关此错误here的信息。

由于Train-PC不是我自己的，因此无法更改软件包的版本。因此，我正在寻找一种不涉及降级的解决方案，因为导入问题也无法通过此解决。

家用PC（Mac）：

tensorflow==1.11.0
keras 2.2.4

火车PC（Ubuntu）：

tensorflow-gpu==1.10.1
keras 2.1.2

CNN的代码：

class ResNetCNN:

    def __init__(self):
        self.graph = tf.Graph()
        self.sess = tf.Session(graph=self.graph)

        with self.sess.graph.as_default():
            # Load pre-trained model from file
            self.CNN_ResNet_Pascal = models.load_model(CNN_MODEL_DIR)
            # self.CNN_ResNet_Pascal = tf.keras.models.load_model(CNN_MODEL_DIR)

            self.extract_feature_vector = K.function([self.CNN_ResNet_Pascal.layers[0].input],
                                                     [self.CNN_ResNet_Pascal.layers[-4].output])

    def feed_forward(self, img):
        """Extract the feature vector of the passed image using the pre-trained CNN."""

        # Resize observable region into input volume
        X_img = np.array(img, dtype=np.float32).reshape(-1, IMG_SIZE, IMG_SIZE, 3)

        # Normalise feature data
        X_img_norm = X_img / 255.0

        # Extract feature vector from the pre-trained CNN (1, 4096)
        feature_vector = self.extract_feature_vector([X_img_norm])[0]

        # Reshape tensor to (4096, )
        feature_vector = np.array(feature_vector).reshape(4096, )

        # Confirm that the correct output layer was used
        assert feature_vector.shape == (4096, ), "Incorrect CNN output layer: shape = {}".format(feature_vector.shape)

        return feature_vector

错误：

Traceback (most recent call last):
  File "keras_pn.py", line 856, in <module>
    s, a, r, d_r, n = rollout(epsilon, RENDER, PolicyNetwork, ResNetCNN)
  File "keras_pn.py", line 682, in rollout
    feature_vector = ResNetCNN.feed_forward(observable_region)
  File "keras_pn.py", line 87, in feed_forward
    feature_vector = self.extract_feature_vector([X_img_norm])[0]
  File "/home/name/anaconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py", line 2357, in __call__
    **self.session_kwargs)
  File "/home/name/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 877, in run
    run_metadata_ptr)
  File "/home/name/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1025, in _run
    raise RuntimeError('The Session graph is empty.  Add operations to the '
RuntimeError: The Session graph is empty.  Add operations to the graph before calling run().

编辑：

def __init__(self):
    self.graph = tf.Graph()

    with self.graph.as_default():
        # Load pre-trained model from file
        self.CNN_ResNet_Pascal = models.load_model(CNN_MODEL_DIR)
        # self.CNN_ResNet_Pascal = tf.keras.models.load_model(CNN_MODEL_DIR)

        self.extract_feature_vector = K.function([self.CNN_ResNet_Pascal.layers[0].input],
                                                 [self.CNN_ResNet_Pascal.layers[-4].output])

    self.sess = tf.Session(graph=self.graph)

会话图表为空-多个图表

0 个答案: