python - Tensorflow 2.1/Keras - 尝试冻结图时出现“输出节点不在图中”错误

问题描述

我正在尝试保存使用 Keras 创建并保存为 .h5 文件的模型，但每次尝试运行 freeze_session 函数时都会收到此错误消息：output_node/Identity is not in graph

这是我的代码（我使用的是 Tensorflow 2.1.0）：

def freeze_session(session, keep_var_names=None, output_names=None, clear_devices=True):
    """
    Freezes the state of a session into a pruned computation graph.

    Creates a new computation graph where variable nodes are replaced by
    constants taking their current value in the session. The new graph will be
    pruned so subgraphs that are not necessary to compute the requested
    outputs are removed.
    @param session The TensorFlow session to be frozen.
    @param keep_var_names A list of variable names that should not be frozen,
                          or None to freeze all the variables in the graph.
    @param output_names Names of the relevant graph outputs.
    @param clear_devices Remove the device directives from the graph for better portability.
    @return The frozen graph definition.
    """
    graph = session.graph
    with graph.as_default():
        freeze_var_names = list(set(v.op.name for v in tf.compat.v1.global_variables()).difference(keep_var_names or []))
        output_names = output_names or []
        output_names += [v.op.name for v in tf.compat.v1.global_variables()]
        input_graph_def = graph.as_graph_def()
        if clear_devices:
            for node in input_graph_def.node:
                node.device = ""
        frozen_graph = tf.compat.v1.graph_util.convert_variables_to_constants(
            session, input_graph_def, output_names, freeze_var_names)
        return frozen_graph

model=kr.models.load_model("model.h5")
model.summary()
# inputs:
print('inputs: ', model.input.op.name)
# outputs: 
print('outputs: ', model.output.op.name)
#layers:
layer_names=[layer.name for layer in model.layers]
print(layer_names)

哪个打印：

inputs: input_node outputs: output_node/Identity ['input_node', 'conv2d_6', 'max_pooling2d_6', 'conv2d_7', 'max_pooling2d_7', 'conv2d_8', 'max_pooling2d_8', 'flatten_2', 'dense_4', 'dense_5', 'output_node'] 正如预期的那样（与我在训练后保存的模型中相同的层名称和输出）。

然后我尝试调用 freeze_session 函数并保存生成的冻结图：

frozen_graph = freeze_session(K.get_session(), output_names=[out.op.name for out in model.outputs])
write_graph(frozen_graph, './', 'graph.pbtxt', as_text=True)
write_graph(frozen_graph, './', 'graph.pb', as_text=False)

但我收到此错误：

AssertionError                            Traceback (most recent call last)
<ipython-input-4-1848000e99b7> in <module>
----> 1 frozen_graph = freeze_session(K.get_session(), output_names=[out.op.name for out in model.outputs])
      2 write_graph(frozen_graph, './', 'graph.pbtxt', as_text=True)
      3 write_graph(frozen_graph, './', 'graph.pb', as_text=False)

<ipython-input-2-3214992381a9> in freeze_session(session, keep_var_names, output_names, clear_devices)
     24                 node.device = ""
     25         frozen_graph = tf.compat.v1.graph_util.convert_variables_to_constants(
---> 26             session, input_graph_def, output_names, freeze_var_names)
     27         return frozen_graph

c:\users\marco\anaconda3\envs\tfv2\lib\site-packages\tensorflow_core\python\util\deprecation.py in new_func(*args, **kwargs)
    322               'in a future version' if date is None else ('after %s' % date),
    323               instructions)
--> 324       return func(*args, **kwargs)
    325     return tf_decorator.make_decorator(
    326         func, new_func, 'deprecated',

c:\users\marco\anaconda3\envs\tfv2\lib\site-packages\tensorflow_core\python\framework\graph_util_impl.py in convert_variables_to_constants(sess, input_graph_def, output_node_names, variable_names_whitelist, variable_names_blacklist)
    275   # This graph only includes the nodes needed to evaluate the output nodes, and
    276   # removes unneeded nodes like those involved in saving and assignment.
--> 277   inference_graph = extract_sub_graph(input_graph_def, output_node_names)
    278 
    279   # Identify the ops in the graph.

c:\users\marco\anaconda3\envs\tfv2\lib\site-packages\tensorflow_core\python\util\deprecation.py in new_func(*args, **kwargs)
    322               'in a future version' if date is None else ('after %s' % date),
    323               instructions)
--> 324       return func(*args, **kwargs)
    325     return tf_decorator.make_decorator(
    326         func, new_func, 'deprecated',

c:\users\marco\anaconda3\envs\tfv2\lib\site-packages\tensorflow_core\python\framework\graph_util_impl.py in extract_sub_graph(graph_def, dest_nodes)
    195   name_to_input_name, name_to_node, name_to_seq_num = _extract_graph_summary(
    196       graph_def)
--> 197   _assert_nodes_are_present(name_to_node, dest_nodes)
    198 
    199   nodes_to_keep = _bfs_for_reachable_nodes(dest_nodes, name_to_input_name)

c:\users\marco\anaconda3\envs\tfv2\lib\site-packages\tensorflow_core\python\framework\graph_util_impl.py in _assert_nodes_are_present(name_to_node, nodes)
    150   """Assert that nodes are present in the graph."""
    151   for d in nodes:
--> 152     assert d in name_to_node, "%s is not in graph" % d
    153 
    154 

**AssertionError: output_node/Identity is not in graph**

我已经尝试过，但我真的不知道如何解决这个问题，所以任何帮助将不胜感激。

标签： pythonpython-3.xtensorflowmachine-learningkeras

python - Tensorflow 2.1/Keras - 尝试冻结图时出现“输出节点不在图中”错误

问题描述

解决方案

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