我正在研究二进制分类，当我使用 CNN 时，我的代码在 Keras Lstm 上运行良好，但出现输入形状不兼容错误。
这是我得到的值错误
ValueError:检查目标时出错:期望dense_61具有3个维度，但得到形状为(24, 1)的数组
这是我使用 keras 的 cnn 代码
model=Sequential()
inputBatch = inputBatch.reshape(24,30, 1)
model.add(Conv1D(64, 3, activation='relu', input_shape=(30, 1)))
model.add(Conv1D(64, 3, activation='relu'))
model.add(MaxPooling1D(pool_size=4,strides=None, padding='valid'))
model.add(Conv1D(128, 3, activation='relu'))
model.add(Conv1D(128, 3, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])
model.fit(inputBatch,ponlabel,batch_size=24,epochs=20,validation_data=(inputBatch, ponlabel))
我正在研究二元分类，要么是正的，要么是负的
作为引用，这是我的 lstm 代码
inputBatch =inputBatch.reshape(24,30,1)
model=Sequential()
model.add(LSTM(50, input_shape=(30, 1)))
model.add(Dense(1, activation="relu"))
model.compile(loss='mean_absolute_error',optimizer='adam')
model.fit(inputBatch,ponlabel,batch_size=24,epochs=100,verbose=1)
inputBatch 是这样的，它适用于 LSTM 代码，但不适用于 CNN，这是我分别用于训练这两个代码的输入
[[ 0. 1288. 1288. 2214. 11266. 6923. 420. 0. 0. 8123.
0. 7619. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
[ 0. 0. 0. 0. 0. 0. 11516. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
[ 0. 9929. 11501. 6573. 11266. 7566. 9963. 4420. 10936. 3657.
7050. 0. 408. 11501. 9988. 9963. 8455. 2879. 9322. 2047.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
[ 0. 11956. 5222. 0. 0. 12106. 6481. 0. 7093. 13756.
12152. 0. 0. 0. 0. 10173. 0. 5173. 13756. 9371.
0. 9956. 0. 0. 9716. 0. 0. 0. 0. 0.]
[ 0. 0. 420. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
[ 0. 11501. 1916. 2073. 10936. 6312. 0. 10193. 10322. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
[ 0. 2879. 7852. 11501. 1934. 286. 11483. 0. 12004. 11118.
0. 12007. 9917. 12111. 1520. 10364. 0. 8840. 4195. 2910.
10773. 11386. 12117. 9321. 0. 0. 0. 0. 0. 0.]
[ 0. 7885. 7171. 1034. 11501. 3103. 5842. 4395. 11871. 3328.
6719. 5407. 1087. 8935. 2937. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
[ 0. 8894. 450. 11516. 7353. 11501. 11502. 11499. 0. 1319.
11693. 11501. 5735. 12111. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
[ 0. 1087. 9565. 23. 0. 3045. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
[ 0. 5015. 11501. 3306. 12111. 9307. 5050. 11501. 3306. 0.
3306. 12111. 1981. 11516. 615. 11516. 0. 3925. 11956. 9371.
9013. 4395. 12111. 5048. 0. 3925. 0. 0. 0. 0.]
[ 0. 1287. 420. 4070. 11087. 7410. 12186. 2387. 12111. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
[ 0. 128. 2073. 10936. 6312. 0. 10193. 10322. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
[ 0. 10173. 9435. 1320. 9322. 12018. 1055. 8840. 6684. 12051.
2879. 0. 12018. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
[ 0. 1570. 5466. 9322. 34. 11480. 1356. 11270. 420. 2153.
12006. 5157. 8840. 1055. 11516. 7387. 2356. 2163. 2879. 5541.
9443. 7441. 1295. 5473. 0. 0. 0. 0. 0. 0.]
[ 0. 5014. 0. 0. 3651. 1087. 63. 6153. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
[ 0. 10608. 10855. 9562. 0. 0. 0. 4202. 0. 0.
0. 10818. 10818. 5842. 0. 9963. 0. 11516. 10464. 7491.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
[ 0. 5952. 6133. 450. 7520. 5842. 3412. 10400. 3412. 2149.
4891. 2979. 3456. 505. 9929. 11501. 9322. 1836. 11501. 12111.
3435. 11105. 11266. 420. 9322. 34. 0. 0. 0. 0.]
[ 0. 1570. 5466. 9322. 34. 11480. 1356. 11270. 420. 2153.
12006. 5157. 8840. 1055. 11516. 7387. 2356. 2163. 2879. 5541.
9443. 7441. 1295. 5473. 0. 0. 0. 0. 0. 0.]
[ 0. 7544. 0. 1709. 420. 10936. 5222. 5842. 10407. 6937.
11329. 2937. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
[ 0. 7785. 8840. 0. 420. 8603. 12003. 2879. 1087. 2356.
2390. 12111. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
[ 0. 8695. 8744. 420. 8840. 6697. 9267. 11516. 11203. 2260.
8840. 7309. 0. 11100. 6041. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
[ 0. 9307. 12003. 2879. 6398. 9372. 4614. 5222. 0. 0.
2879. 10364. 6923. 4709. 4860. 11871. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
[ 0. 0. 2844. 1287. 420. 11501. 610. 11501. 596. 0.
12111. 3690. 6343. 9963. 0. 0. 8840. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]]
最佳答案
问题是输出形状，因为您使用 CNN，输出是 3D(样本、宽度、 channel )，并且 Dense 层将在最后一个维度上运行，为您提供 3D 输出。但你想要一个 2D 输出，所以你需要添加一个 Flatten 层:
model=Sequential()
model.add(Conv1D(64, 3, activation='relu', input_shape=(30, 1)))
model.add(Conv1D(64, 3, activation='relu'))
model.add(MaxPooling1D(pool_size=4,strides=None, padding='valid'))
model.add(Conv1D(128, 3, activation='relu'))
model.add(Conv1D(128, 3, activation='relu'))
model.add(Dropout(0.5))
model.add(Flatten())
model.add(Dense(1, activation='sigmoid'))
您可以通过执行model.summary()来比较该模型和原始模型的输出形状
关于python - Keras - CNN 输入形状不兼容，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/53119432/