尝试基于部分预训练模型构建新模型，
这里有一些清理过的代码。
假设我们对model1进行了训练，并想添加在model2中定义的一些层：
import tensorflow.keras as keras
import tensorflow as tf
from tensorflow.keras.layers import Input, Conv2D, Activation
from tensorflow.keras.models import Model, Sequential
model1 = Sequential([
Conv2D(2, (3,3), padding='same', input_shape=(6,6,1)),
Activation('relu')
])
model2 = Sequential([
Conv2D(3, (3,3), padding='same', input_shape=(6,6,2)),
Activation('softmax')
])
model_merge = Model(inputs=model1.input,
outputs=Activation('softmax')(model2(model1.get_layer('conv2d').output)))
看起来有些混乱，但是我想通过在此处添加softmax激活来演示它并未断开连接。
模型1的摘要：
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d (Conv2D) (None, 6, 6, 2) 20
_________________________________________________________________
activation (Activation) (None, 6, 6, 2) 0
=================================================================
Total params: 20
Trainable params: 20
Non-trainable params: 0
_________________________________________________________________
model2的摘要：
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_4 (Conv2D) (None, 6, 6, 3) 57
_________________________________________________________________
activation_4 (Activation) (None, 6, 6, 3) 0
=================================================================
Total params: 57
Trainable params: 57
Non-trainable params: 0
_________________________
以及model_merge的摘要：
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_input (InputLayer) (None, 6, 6, 1) 0
_________________________________________________________________
conv2d (Conv2D) (None, 6, 6, 2) 20
_________________________________________________________________
sequential_2 (Sequential) (None, 6, 6, 3) 57
_________________________________________________________________
activation_4 (Activation) (None, 6, 6, 3) 0
=================================================================
Total params: 77
Trainable params: 77
Non-trainable params: 0
_________________________________________________________________
让我们证明这个合并模型没有断开连接：
layers = [layer.output for layer in model_merge.layers]
test1 = Model(inputs=model_merge.input, outputs=layers[-1])
一切正常。
test1的摘要：
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_input (InputLayer) (None, 6, 6, 1) 0
_________________________________________________________________
conv2d (Conv2D) (None, 6, 6, 2) 20
_________________________________________________________________
sequential_2 (Sequential) (None, 6, 6, 3) 57
_________________________________________________________________
activation_4 (Activation) (None, 6, 6, 3) 0
=================================================================
Total params: 77
Trainable params: 77
Non-trainable params: 0
_________________________________________________________________
这是悲剧：
test2 = Model(inputs=model_merge.input, outputs=layers[-2])
最重要的反馈：
ValueError: Graph disconnected: cannot obtain value for tensor Tensor("conv2d_2_input:0", shape=(?, 6, 6, 2), dtype=float32) at layer "conv2d_2_input". The following previous layers were accessed without issue: []
完整反馈：
ValueErrorTraceback (most recent call last)
<ipython-input-18-946b325081c1> in <module>
----> 1 test = Model(inputs=model_merge.input, outputs=layers[-2])
/usr/local/lib/python3.5/dist-packages/tensorflow/python/keras/engine/training.py in __init__(self, *args, **kwargs)
119
120 def __init__(self, *args, **kwargs):
--> 121 super(Model, self).__init__(*args, **kwargs)
122 # Create a cache for iterator get_next op.
123 self._iterator_get_next = weakref.WeakKeyDictionary()
/usr/local/lib/python3.5/dist-packages/tensorflow/python/keras/engine/network.py in __init__(self, *args, **kwargs)
79 'inputs' in kwargs and 'outputs' in kwargs):
80 # Graph network
---> 81 self._init_graph_network(*args, **kwargs)
82 else:
83 # Subclassed network
/usr/local/lib/python3.5/dist-packages/tensorflow/python/training/checkpointable/base.py in _method_wrapper(self, *args, **kwargs)
440 self._setattr_tracking = False # pylint: disable=protected-access
441 try:
--> 442 method(self, *args, **kwargs)
443 finally:
444 self._setattr_tracking = previous_value # pylint: disable=protected-access
/usr/local/lib/python3.5/dist-packages/tensorflow/python/keras/engine/network.py in _init_graph_network(self, inputs, outputs, name)
219 # Keep track of the network's nodes and layers.
220 nodes, nodes_by_depth, layers, layers_by_depth = _map_graph_network(
--> 221 self.inputs, self.outputs)
222 self._network_nodes = nodes
223 self._nodes_by_depth = nodes_by_depth
/usr/local/lib/python3.5/dist-packages/tensorflow/python/keras/engine/network.py in _map_graph_network(inputs, outputs)
1850 'The following previous layers '
1851 'were accessed without issue: ' +
-> 1852 str(layers_with_complete_input))
1853 for x in node.output_tensors:
1854 computable_tensors.append(x)
ValueError: Graph disconnected: cannot obtain value for tensor Tensor("conv2d_2_input:0", shape=(?, 6, 6, 2), dtype=float32) at layer "conv2d_2_input". The following previous layers were accessed without issue: []
这真的使我发疯，
有什么想法吗？
答案 0 :(得分：0)
您要用作输出的图层有两个输出节点。第一个将model2的输入连接到model2的输出。第二个输出节点连接model1的输出和model2的第一层。默认情况下，图层输出仅返回第一个输出节点。因此，发生的事情是您想将model_merge的输入（model1的输入）与第一个输出节点连接。
以下代码显示了这一点。可以使用层的get_output_at()方法访问层的各个输出节点。
layer_output = model_merge.layers[-2].output # The first output node
layer_output_1 = model_merge.layers[-2].get_output_at(0) # The first output node
layer_output_2 = model_merge.layers[-2].get_output_at(1) # The second output node
现在以下两个代码将引发错误，因为图形已断开连接。
test2 = Model(inputs=model_merge.input, outputs=layer_output)
和
test2 = Model(inputs=model_merge.input, outputs=layer_output_1)
但是下面的代码不会引发错误，因为图形已连接。
test2 = Model(inputs=model_merge.input, outputs=layer_output_2)