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我正在尝试使用 tensorflow.keras.applications EfficientNetB0 实现一个 hydranet 架构。该架构的目标是将网络分成两部分（第一部分：主干，第二部分：头部）。然后，输入图像应该只提供一次到主干，并且应该存储它的输出。之后，这个输出应该直接馈送到头部（根据要分类的类的数量可以不止一个）。最佳方法：

  1. 我不想为每个头重绘整个模型。
  2. 主干应该只执行一次。

如果已经查看此论坛帖子：keras-give-input-to-intermediate-layer-and-get-final-output ，但提出的解决方案要么需要重新编码头部，要么不起作用。

我尝试了以下方法：

from tensorflow.keras.applications import EfficientNetB0 as Net
from tensorflow.keras.models import Model
split_idx = 73
input_shape = (250, 250, 3) # use depth=3 because imagenet is trained on RGB images
model = Net(weights="imagenet", include_top = True)

# Approach 1:

# create the full network so we can train on it
model_backbone = keras.models.Model(inputs=model.input, outputs=model.layers[split_idx].output)

# create new model taking the output from backbone as input and creating final output of head
model_head = keras.models.Model(inputs=model.layers[split_idx].output, 
                                outputs=model.layers[-1].output)

# Approach 2:
# create function for feeding input through backbone
# the function takes a normal input image as input and returns the output of the final backbone layer
create_backbone_output = K.function([model.layers[0].input], model.layers[split_idx].output)

# create function for feeding output of backbone through heads
create_heads_output = K.function([model.layers[split_idx].output], 
                                  model.output)

但是，当我尝试执行此操作时，两种方法都会出现“图形断开错误”：

WARNING:tensorflow:Functional model inputs must come from `tf.keras.Input` (thus holding past layer 
metadata), they cannot be the output of a previous non-Input layer. Here, a tensor specified as input 
to "model_5" was not an Input tensor, it was generated by layer block3b_drop.
Note that input tensors are instantiated via `tensor = tf.keras.Input(shape)`.
The tensor that caused the issue was: block3b_drop/Identity:0
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-33-64dd6f6430a1> in <module>
  6 # create function for feeding output of backbone through heads
  7 create_heads_output = K.function([model.layers[split_idx].output], 
----> 8                                  model.output)

~\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\keras\backend.py in 
function(inputs, 
outputs, updates, name, **kwargs)
4067     from tensorflow.python.keras import models  # pylint: disable=g-import-not-at-top
4068     from tensorflow.python.keras.utils import tf_utils  # pylint: disable=g-import-not-at-top
-> 4069     model = models.Model(inputs=inputs, outputs=outputs)
4070 
4071     wrap_outputs = isinstance(outputs, list) and len(outputs) == 1

~\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\training\tracking\base.py in 
_method_wrapper(self, *args, **kwargs)
515     self._self_setattr_tracking = False  # pylint: disable=protected-access
516     try:
--> 517       result = method(self, *args, **kwargs)
518     finally:
519       self._self_setattr_tracking = previous_value  # pylint: disable=protected-access

~\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\keras\engine\functional.py in 
__init__(self, inputs, outputs, name, trainable, **kwargs)
118     generic_utils.validate_kwargs(kwargs, {})
119     super(Functional, self).__init__(name=name, trainable=trainable)
--> 120     self._init_graph_network(inputs, outputs)
121 
122   @trackable.no_automatic_dependency_tracking

~\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\training\tracking\base.py in 
_method_wrapper(self, *args, **kwargs)
515     self._self_setattr_tracking = False  # pylint: disable=protected-access
516     try:
--> 517       result = method(self, *args, **kwargs)
518     finally:
519       self._self_setattr_tracking = previous_value  # pylint: disable=protected-access

~\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\keras\engine\functional.py in 
_init_graph_network(self, inputs, outputs)
202     # Keep track of the network's nodes and layers.
203     nodes, nodes_by_depth, layers, _ = _map_graph_network(
--> 204         self.inputs, self.outputs)
205     self._network_nodes = nodes
206     self._nodes_by_depth = nodes_by_depth

~\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\keras\engine\functional.py in 
_map_graph_network(inputs, outputs)
981                              'The following previous layers '
982                              'were accessed without issue: ' +
--> 983                              str(layers_with_complete_input))
984         for x in nest.flatten(node.outputs):
985           computable_tensors.add(id(x))

ValueError: Graph disconnected: cannot obtain value for tensor 
KerasTensor(type_spec=TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name='input_4'), 
name='input_4', description="created by layer 'input_4'") at layer "rescaling_3". The following 
previous layers were accessed without issue: []

我知道错误源于提供的张量不是输入张量的问题。这个问题有什么解决办法吗？

4

1 回答 1

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1.) 该模型不像您尝试处理它那样顺序 -> split_idx +1 是添加另一层的 add_operation，必须将其添加到第一个输出并添加到第二个输入。

block3b_drop (Dropout)          (None, 28, 28, 40)   0           block3b_project_bn[0][0]         
__________________________________________________________________________________________________
block3b_add (Add)               (None, 28, 28, 40)   0           block3b_drop[0][0]               
                                                                 block3a_project_bn[0][0]         
__________________________________________________________________________________________________

2.）将所有需要的输入与给定的输出相加：

second_input1 = keras.Input(shape=model.layers[split_idx].output.shape[1:])
second_input2 = keras.Input(shape=model.get_layer(name='block3a_project_bn').output.shape[1:])

3.）在这里重新连接模型的其余部分，你需要添加一些东西，但我给你一些片段让你开始：

for sequentially rewiring it it would be:
    tmp = [second_input1,second_input2]
    for l in range(split_idx+1, len(model.layers)):
        layer = model.layers[l]
        print(layer.name, layer.input)
        tmp = layer(tmp)

在您的情况下，这还不够，您需要找到正确的输入，下面的片段就是这样做的。找到正确的输入，将其用于下一个输出（跟踪输出），然后通过图表按自己的方式工作

for l in model.layers:
    # multiple inputs
    if type(l.input) is list:
        for li,lv in enumerate(l.input):
            print('o ', li, lv.name)
    else:
        print('- ', l.input.name)

另一种便宜的方法是 -> 将其保存为 json，添加您的输入节点，在那里删除未使用的节点。加载新的 json 文件，在这种情况下，您不需要重新连接。

于 2020-11-26T16:45:31.860 回答