Python CNN和keras给出了图形断开错误

我正在试验对双CNN使用多输入

但是，我正在断开连接

图形：无法获取“嵌入”层的张量张量值（“嵌入1\u输入：0”，shape=（？，40），dtype=float32）。访问以下以前的层时没有问题：[]

我没有找到它的修复方法，因为我知道有一些层的

shape=（？，50）

为什么会有这样的维度

我尝试在第二次嵌入后添加展平层，但我得到了一个错误。即使辍学了

我还尝试从L_分支模型中消除最大池

尝试不使用重塑，只扩展输入dims，因为我得到了第二个Conv1D的错误，表示层期望dims=3，但得到dims=2

latent = Conv1D(50, activation='relu', kernel_size=nb_classes, input_shape=(1, input_size, 1))(merged)
File "/root/anaconda3/envs/oea/lib/python3.7/site-packages/keras/engine/base_layer.py", line 414, in __call__
self.assert_input_compatibility(inputs)
File "/root/anaconda3/envs/oea/lib/python3.7/site-packages/keras/engine/base_layer.py", line 311, in assert_input_compatibility
str(K.ndim(x)))
ValueError: Input 0 is incompatible with layer conv1d_2: expected ndim=3, found ndim=2`

---

我也没有得到第一个模型中使用了哪些输入，第二个模型中使用了哪些输入？

您必须这样定义您的新模型：

final\u model=model（[L\u branch.input，R\u branch.input]，out）

我提供了一个类似于您的网络结构的示例

vocab_size1 = 5000
vocab_size2 = 50000
input_size1 = 40
input_size2 = 40
max_words=50
emb_size = 15
nb_classes = 10


# first model
L_branch = Sequential()
L_branch.add(Embedding(vocab_size1, emb_size, input_length=input_size1, trainable=True))
L_branch.add(Conv1D(50, activation='relu', kernel_size=10))
L_branch.add(MaxPooling1D())
L_branch.add(Flatten())
L_branch.add(Dense(emb_size, activation='relu'))

# second model
R_branch = Sequential()
R_branch.add(Embedding(vocab_size2, emb_size, input_length=input_size2, trainable=True))
R_branch.add(Flatten())
R_branch.add(Dense(emb_size, activation='relu'))

merged = Concatenate()([L_branch.output, R_branch.output])
latent = Dense(50, activation='relu')(merged)
out = Dense(nb_classes, activation='softmax')(latent)

final_model = Model([L_branch.input, R_branch.input], out)
final_model.compile(
            loss='sparse_categorical_crossentropy',
            optimizer='adam',
            metrics=['accuracy'])
final_model.summary()


X1 = np.random.randint(0,vocab_size1, (100,input_size1))
X2 = np.random.randint(0,vocab_size2, (100,input_size2))
y = np.random.randint(0,nb_classes, 100)

final_model.fit([X1,X2], y, epochs=10)

---

评论不用于扩展讨论；这段对话已经结束。

vocab_size1 = 5000
vocab_size2 = 50000
input_size1 = 40
input_size2 = 40
max_words=50
emb_size = 15
nb_classes = 10


# first model
L_branch = Sequential()
L_branch.add(Embedding(vocab_size1, emb_size, input_length=input_size1, trainable=True))
L_branch.add(Conv1D(50, activation='relu', kernel_size=10))
L_branch.add(MaxPooling1D())
L_branch.add(Flatten())
L_branch.add(Dense(emb_size, activation='relu'))

# second model
R_branch = Sequential()
R_branch.add(Embedding(vocab_size2, emb_size, input_length=input_size2, trainable=True))
R_branch.add(Flatten())
R_branch.add(Dense(emb_size, activation='relu'))

merged = Concatenate()([L_branch.output, R_branch.output])
latent = Dense(50, activation='relu')(merged)
out = Dense(nb_classes, activation='softmax')(latent)

final_model = Model([L_branch.input, R_branch.input], out)
final_model.compile(
            loss='sparse_categorical_crossentropy',
            optimizer='adam',
            metrics=['accuracy'])
final_model.summary()


X1 = np.random.randint(0,vocab_size1, (100,input_size1))
X2 = np.random.randint(0,vocab_size2, (100,input_size2))
y = np.random.randint(0,nb_classes, 100)

final_model.fit([X1,X2], y, epochs=10)