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<p>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。</p>
<p>在我的研究中,我发现了一些关于这个错误的答案:</p>
<p><a href="https://stackoverflow.com/questions/45979848/merge-2-sequential-models-in-keras">Merge 2 sequential models in Keras</a>
上面的问题是连接2个模型而不是连接2个层</p>
<p><a href="https://keras.io/getting-started/functional-api-guide/#multi-input-and-multi-output-models" rel="nofollow noreferrer" title="Multi-input and multi-output models">https://keras.io/getting-started/functional-api-guide/#multi-input-and-multi-output-models</a>
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。</p>
<pre><code> input
/ | \
a1 b1 c1
| | |
a2 b2 c2
\ | /
concatenate
/ | \
d1 e1 f1
| | |
d2 e2 f2
\ | /
output
</code></pre>
<p>以上是我的最终目标:)</p>
<pre><code>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
</code></pre>
<p>错误:</p>
<pre><code>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.
</code></pre>