我是python新手,正在进行一个图像处理项目,并构建了下面的模型。我把错误贴在模型下面。
在我的研究中,我发现了一些关于这个错误的答案:
Merge 2 sequential models in Keras 上面的问题是连接2个模型而不是连接2个层
https://keras.io/getting-started/functional-api-guide/#multi-input-and-multi-output-models 在keras示例中,连接2层,但1层是输入层。我想了解如何连接2个常规层,并使用连接层作为层序列中的层。类似于初始模型的概念。
input
/ | \
a1 b1 c1
| | |
a2 b2 c2
\ | /
concatenate
/ | \
d1 e1 f1
| | |
d2 e2 f2
\ | /
output
以上是我的最终目标:)
def fet_Model():
bnd_input = Input(shape=input_shape)
k31 = Conv2D(128, kernel_size=(3, 3), activation='relu', padding='same')(bnd_input)
k31 = Conv2D(128, kernel_size=(3, 3), activation='relu', padding='same')(k31)
k31 = MaxPooling2D(pool_size=(3, 3), strides=(2, 2))(k31)
in_ly1_cv_1n1 = Conv2D(32, kernel_size=(1, 1), activation='relu', padding='same')(k31)
in_ly1_cv_3n3 = Conv2D(64, kernel_size=(3, 3), activation='relu', padding='same')(in_ly1_cv_1n1)
in_ly1_cv_5n5 = Conv2D(64, kernel_size=(5, 5), activation='relu', padding='same')(in_ly1_cv_1n1)
in_ly1_mx_pl = MaxPooling2D(pool_size=(3, 3), strides=(1, 1), padding='same')(k31)
in_ly1_mxcv_1n1 = Conv2D(32, kernel_size=(1, 1), activation='relu', padding='same')(in_ly1_mx_pl)
filt_concat1 = Concatenate([in_ly1_mxcv_1n1, in_ly1_cv_5n5, in_ly1_cv_3n3, k31])
# when running the below line I receive the error
in_ly2_cv_1n1 = Conv2D(32, kernel_size=(1, 1), activation='relu', padding='same')(filt_concat1)
in_ly2_cv_3n3 = Conv2D(64, kernel_size=(3, 3), activation='relu', padding='same')(in_ly2_cv_1n1)
in_ly2_cv_5n5 = Conv2D(64, kernel_size=(5, 5), activation='relu', padding='same')(in_ly2_cv_1n1)
in_ly2_mx_pl = MaxPooling2D(pool_size=(3, 3), strides=(1, 1))(filt_concat1)
in_ly2_mxcv_1n1 = Conv2D(32, kernel_size=(1, 1), activation='relu', padding='same')(in_ly2_mx_pl)
filt_concat2 = Concatenate([in_ly2_mxcv_1n1, in_ly2_cv_5n5, in_ly2_cv_3n3, k31])
lst_ly_avg_pl = AveragePooling2D(pool_size=(3, 3), strides=(2, 2))(filt_concat2)
lst_ly_cnv2 = Conv2D(32, kernel_size=(1, 1), activation='relu', padding='same')(lst_ly_avg_pl)
lst_ly_den = Dense(1, activation='sigmoid')(lst_ly_cnv2)
model = Model(inputs=bnd_input, outputs=lst_ly_den)
optimizer = Adam(lr= 0.002, beta_1=0.99, beta_2=0.999, epsilon=1e-08, decay=0.01)
model.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy'])
return model
错误:
Traceback (most recent call last):
K.is_keras_tensor(x)
raise ValueError('Unexpectedly found an instance of type `' +
str(type(x)) + '`. '
ValueError: Unexpectedly found an instance of type `<class 'keras.layers.merge.Concatenate'>`. Expected a symbolic tensor instance.
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-194-ebff784b774c>", line 1, in <module>
in_ly2_cv_1n1 = Conv2D(32, kernel_size=(1, 1), activation='relu', padding='same')(filt_concat1)
self.assert_input_compatibility(inputs)
str(inputs) + '. All inputs to the layer '
ValueError: Layer conv2d_41 was called with an input that isn't a symbolic tensor. Received type: <class 'keras.layers.merge.Concatenate'>. Full input: [<keras.layers.merge.Concatenate object at 0x1cee082b0>]. All inputs to the layer should be tensors.
我想你需要
Concatenate(axis=-1)([tensor_1, tensor_2])
。我无法重建你的问题。你的代码对我很有用。 尝试更新keras,然后再次运行。 如果使用水蟒,请执行以下操作:
进一步解释@thepartofspeech answers(https://stackoverflow.com/a/51624786/8096768)。
来自连接层(https://keras.io/layers/merge/#concatenate)上的keras文档
keras.layers.Concatenate(axis=-1)
连接输入列表的层。
它接受一个张量列表作为输入,除了连接轴之外,所有张量都是相同的形状,并返回一个张量,即所有输入的连接。
在这种情况下,应该使用连接层上的
axis=-1
选项直接调用该层,然后是输入张量,例如filt_concat1 = Concatenate(axis=-1)([in_ly1_mxcv_1n1, in_ly1_cv_5n5, in_ly1_cv_3n3, k31])
注意:我没有尝试使用超过2个输入张量-但在2个输入的情况下,它是有效的。
我希望这能有帮助。
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