我试图融合两个CNN,但当我使用concatenate时,收到以下错误:
Traceback (most recent call last):
File "vggFace_MM.py", line 57, in <module>
fuse_layer = concatenate([stream_1, stream_2])
File "/usr/local/lib/python2.7/dist-packages/keras/layers/merge.py", line 508, in concatenate
return Concatenate(axis=axis, **kwargs)(inputs)
File "/usr/local/lib/python2.7/dist-packages/keras/engine/topology.py", line 596, in __call__
output = self.call(inputs, **kwargs)
File "/usr/local/lib/python2.7/dist-packages/keras/layers/merge.py", line 283, in call
return K.concatenate(inputs, axis=self.axis)
File "/usr/local/lib/python2.7/dist-packages/keras/backend/tensorflow_backend.py", line 1723, in concatenate
return tf.concat([to_dense(x) for x in tensors], axis)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/array_ops.py", line 1075, in concat
dtype=dtypes.int32).get_shape(
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 669, in convert_to_tensor
ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/constant_op.py", line 176, in _constant_tensor_conversion_function
return constant(v, dtype=dtype, name=name)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/constant_op.py", line 165, in constant
tensor_util.make_tensor_proto(value, dtype=dtype, shape=shape, verify_shape=verify_shape))
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/tensor_util.py", line 367, in make_tensor_proto
_AssertCompatible(values, dtype)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/tensor_util.py", line 302, in _AssertCompatible
(dtype.name, repr(mismatch), type(mismatch).__name__))
TypeError: Expected int32, got list containing Tensors of type '_Message' instead.
这是我的代码:
^{pr2}$我使用的是VggFace,所以vgg_模型1和vgg_2是同一个CNN,但是每个都有不同的输入。在
我认为你实际上需要使用
merge
,而不是使用concatenate
按照this link答案获取更多详细信息。在
请跟随this link获取有关
merge
的更多详细信息要详细说明,你需要为模型创建2个头部,而不是CNN的层。 所以}则接受其他类型的输入
Model1
将你的面部情绪数据作为输入,而{因此,要创建这两个头部以提供组合输出,您需要创建第三个模型,该模型将合并这两个模型以给出单个组合输出。在
可以按如下方式进行
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