Python 合并1D-CNN和2D-CNN
我想使用1D和2D CNN构建一个合并的CNN模型，但我尝试了许多方法来构建它，但这一种方法对我有效，但我不知道为什么在使用Python 合并1D-CNN和2D-CNN,python,tensorflow,keras,deep-learning,cnn,Python,Tensorflow,Keras,Deep Learning,Cnn,我想使用1D和2D CNN构建一个合并的CNN模型，但我尝试了许多方法来构建它，但这一种方法对我有效，但我不知道为什么在使用model\u combined.summary（）时会出现此错误。 我附上了两张包含1D和2D CNN摘要的图片， 多谢各位 ValueError Traceback (most recent call last) <ipython-input-20-3c58e6d04c4d> in <mo
model\u combined.summary（）时会出现此错误。
我附上了两张包含1D和2D CNN摘要的图片，
多谢各位
ValueError Traceback (most recent call last)
<ipython-input-20-3c58e6d04c4d> in <module>()
60 #opt = RMSprop(lr=0.001, rho=0.9)
61 model_combined.compile(optimizer=opt, loss='binary_crossentropy', metrics=['accuracy'])
---> 62 print(model_combined.summary())
/usr/local/lib/python3.6/dist-packages/keras/engine/network.py in summary(self, line_length, positions, print_fn)
1305 if not self.built:
1306 raise ValueError(
-> 1307 'This model has not yet been built. '
1308 'Build the model first by calling build() '
1309 'or calling fit() with some data. '
ValueError: This model has not yet been built. Build the model first by calling build() or calling fit() with some data. Or specify input_shape or batch_input_shape in the first layer for automatic build.
您可以通过仅在第一层向1D-CNN和2D-CNN提供输入形状来尝试函数式API，而不是顺序API。然后，通过在1D-CNN和2D-CNN中的密集层之前添加展平层，将解决您的问题。
您可以按照下面修改的代码进行操作。
1D-CNN:
# ----------------------- 1D CNN ----------------------
in_1D = Input((7380, 128000))
# 1
model_1D = Conv1D(32, kernel_size= 5 , strides=1, activation='relu')(in_1D)
model_1D = MaxPooling1D(pool_size= 4, strides=4)(model_1D)
# 2
model_1D = Conv1D(32, kernel_size= 5 , strides=1 , activation='relu')(model_1D)
model_1D = MaxPooling1D(pool_size= 4, strides=4)(model_1D)
# 3
model_1D = Conv1D(64, kernel_size= 5 , strides=1 , activation='relu')(model_1D)
model_1D = MaxPooling1D(pool_size= 4, strides=4)(model_1D)
# 4
model_1D = Conv1D(64, kernel_size= 5 , strides=1 , activation='relu')(model_1D)
model_1D = MaxPooling1D(pool_size= 2, strides=2)(model_1D)
# 5
model_1D = Conv1D(128, kernel_size= 5 , strides= 1 , activation='relu')(model_1D)
model_1D = MaxPooling1D(pool_size= 2, strides= 2)(model_1D)
# 6
model_1D = Conv1D(128, kernel_size= 5 , strides= 1 , activation='relu')(model_1D)
model_1D = MaxPooling1D(pool_size= 2, strides= 2)(model_1D)
model_1D = Flatten()(model_1D)
model_1D = Dense(9 , activation='softmax')(model_1D)
# ----------------------- 2D CNN ----------------------
in_2D = Input((7380, 128, 251))
model_2D = Conv2D(32, kernel_size=(3, 3) , strides=(1,1), activation='relu')(in_2D)
model_2D = Conv2D(32, kernel_size=(3, 3) , strides=(1,1), activation='relu')(model_2D)
model_2D = MaxPooling2D(pool_size=(2, 2), strides=(2, 2))(model_2D)
model_2D = Conv2D(32, kernel_size=(3, 3) , strides=(1,1), activation='relu')(model_2D)
model_2D = Conv2D(32, kernel_size=(3, 3) , strides=(1,1), activation='relu')(model_2D)
model_2D = MaxPooling2D(pool_size=(2, 2), strides=(2, 2))(model_2D)
model_2D = Flatten()(model_2D)
model_2D = Dense(9 , activation='relu')(model_2D)
merged = Concatenate()([model_2D, model_1D])
output = Dense(7, activation='softmax')(merged)
model_combined = Model(inputs=[in_2D, in_1D], outputs=[output])
model_combined.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model_combined.summary()
Model: "model_1"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) (None, 7380, 128000) 0
__________________________________________________________________________________________________
conv1d_1 (Conv1D) (None, 7376, 32) 20480032 input_1[0][0]
__________________________________________________________________________________________________
max_pooling1d_1 (MaxPooling1D) (None, 1844, 32) 0 conv1d_1[0][0]
__________________________________________________________________________________________________
conv1d_2 (Conv1D) (None, 1840, 32) 5152 max_pooling1d_1[0][0]
__________________________________________________________________________________________________
max_pooling1d_2 (MaxPooling1D) (None, 460, 32) 0 conv1d_2[0][0]
__________________________________________________________________________________________________
conv1d_3 (Conv1D) (None, 456, 64) 10304 max_pooling1d_2[0][0]
__________________________________________________________________________________________________
input_2 (InputLayer) (None, 7380, 128, 25 0
__________________________________________________________________________________________________
max_pooling1d_3 (MaxPooling1D) (None, 114, 64) 0 conv1d_3[0][0]
__________________________________________________________________________________________________
conv2d_1 (Conv2D) (None, 7378, 126, 32 72320 input_2[0][0]
__________________________________________________________________________________________________
conv1d_4 (Conv1D) (None, 110, 64) 20544 max_pooling1d_3[0][0]
__________________________________________________________________________________________________
conv2d_2 (Conv2D) (None, 7376, 124, 32 9248 conv2d_1[0][0]
__________________________________________________________________________________________________
max_pooling1d_4 (MaxPooling1D) (None, 55, 64) 0 conv1d_4[0][0]
__________________________________________________________________________________________________
max_pooling2d_1 (MaxPooling2D) (None, 3688, 62, 32) 0 conv2d_2[0][0]
__________________________________________________________________________________________________
conv1d_5 (Conv1D) (None, 51, 128) 41088 max_pooling1d_4[0][0]
__________________________________________________________________________________________________
conv2d_3 (Conv2D) (None, 3686, 60, 32) 9248 max_pooling2d_1[0][0]
__________________________________________________________________________________________________
max_pooling1d_5 (MaxPooling1D) (None, 25, 128) 0 conv1d_5[0][0]
__________________________________________________________________________________________________
conv2d_4 (Conv2D) (None, 3684, 58, 32) 9248 conv2d_3[0][0]
__________________________________________________________________________________________________
conv1d_6 (Conv1D) (None, 21, 128) 82048 max_pooling1d_5[0][0]
__________________________________________________________________________________________________
max_pooling2d_2 (MaxPooling2D) (None, 1842, 29, 32) 0 conv2d_4[0][0]
__________________________________________________________________________________________________
max_pooling1d_6 (MaxPooling1D) (None, 10, 128) 0 conv1d_6[0][0]
__________________________________________________________________________________________________
flatten_2 (Flatten) (None, 1709376) 0 max_pooling2d_2[0][0]
__________________________________________________________________________________________________
flatten_1 (Flatten) (None, 1280) 0 max_pooling1d_6[0][0]
__________________________________________________________________________________________________
dense_2 (Dense) (None, 9) 15384393 flatten_2[0][0]
__________________________________________________________________________________________________
dense_1 (Dense) (None, 9) 11529 flatten_1[0][0]
__________________________________________________________________________________________________
concatenate_1 (Concatenate) (None, 18) 0 dense_2[0][0]
dense_1[0][0]
__________________________________________________________________________________________________
dense_3 (Dense) (None, 7) 133 concatenate_1[0][0]
==================================================================================================
Total params: 36,135,287
Trainable params: 36,135,287
Non-trainable params: 0
2D-CNN:
# ----------------------- 1D CNN ----------------------
in_1D = Input((7380, 128000))
# 1
model_1D = Conv1D(32, kernel_size= 5 , strides=1, activation='relu')(in_1D)
model_1D = MaxPooling1D(pool_size= 4, strides=4)(model_1D)
# 2
model_1D = Conv1D(32, kernel_size= 5 , strides=1 , activation='relu')(model_1D)
model_1D = MaxPooling1D(pool_size= 4, strides=4)(model_1D)
# 3
model_1D = Conv1D(64, kernel_size= 5 , strides=1 , activation='relu')(model_1D)
model_1D = MaxPooling1D(pool_size= 4, strides=4)(model_1D)
# 4
model_1D = Conv1D(64, kernel_size= 5 , strides=1 , activation='relu')(model_1D)
model_1D = MaxPooling1D(pool_size= 2, strides=2)(model_1D)
# 5
model_1D = Conv1D(128, kernel_size= 5 , strides= 1 , activation='relu')(model_1D)
model_1D = MaxPooling1D(pool_size= 2, strides= 2)(model_1D)
# 6
model_1D = Conv1D(128, kernel_size= 5 , strides= 1 , activation='relu')(model_1D)
model_1D = MaxPooling1D(pool_size= 2, strides= 2)(model_1D)
model_1D = Flatten()(model_1D)
model_1D = Dense(9 , activation='softmax')(model_1D)
# ----------------------- 2D CNN ----------------------
in_2D = Input((7380, 128, 251))
model_2D = Conv2D(32, kernel_size=(3, 3) , strides=(1,1), activation='relu')(in_2D)
model_2D = Conv2D(32, kernel_size=(3, 3) , strides=(1,1), activation='relu')(model_2D)
model_2D = MaxPooling2D(pool_size=(2, 2), strides=(2, 2))(model_2D)
model_2D = Conv2D(32, kernel_size=(3, 3) , strides=(1,1), activation='relu')(model_2D)
model_2D = Conv2D(32, kernel_size=(3, 3) , strides=(1,1), activation='relu')(model_2D)
model_2D = MaxPooling2D(pool_size=(2, 2), strides=(2, 2))(model_2D)
model_2D = Flatten()(model_2D)
model_2D = Dense(9 , activation='relu')(model_2D)
merged = Concatenate()([model_2D, model_1D])
output = Dense(7, activation='softmax')(merged)
model_combined = Model(inputs=[in_2D, in_1D], outputs=[output])
model_combined.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model_combined.summary()
Model: "model_1"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) (None, 7380, 128000) 0
__________________________________________________________________________________________________
conv1d_1 (Conv1D) (None, 7376, 32) 20480032 input_1[0][0]
__________________________________________________________________________________________________
max_pooling1d_1 (MaxPooling1D) (None, 1844, 32) 0 conv1d_1[0][0]
__________________________________________________________________________________________________
conv1d_2 (Conv1D) (None, 1840, 32) 5152 max_pooling1d_1[0][0]
__________________________________________________________________________________________________
max_pooling1d_2 (MaxPooling1D) (None, 460, 32) 0 conv1d_2[0][0]
__________________________________________________________________________________________________
conv1d_3 (Conv1D) (None, 456, 64) 10304 max_pooling1d_2[0][0]
__________________________________________________________________________________________________
input_2 (InputLayer) (None, 7380, 128, 25 0
__________________________________________________________________________________________________
max_pooling1d_3 (MaxPooling1D) (None, 114, 64) 0 conv1d_3[0][0]
__________________________________________________________________________________________________
conv2d_1 (Conv2D) (None, 7378, 126, 32 72320 input_2[0][0]
__________________________________________________________________________________________________
conv1d_4 (Conv1D) (None, 110, 64) 20544 max_pooling1d_3[0][0]
__________________________________________________________________________________________________
conv2d_2 (Conv2D) (None, 7376, 124, 32 9248 conv2d_1[0][0]
__________________________________________________________________________________________________
max_pooling1d_4 (MaxPooling1D) (None, 55, 64) 0 conv1d_4[0][0]
__________________________________________________________________________________________________
max_pooling2d_1 (MaxPooling2D) (None, 3688, 62, 32) 0 conv2d_2[0][0]
__________________________________________________________________________________________________
conv1d_5 (Conv1D) (None, 51, 128) 41088 max_pooling1d_4[0][0]
__________________________________________________________________________________________________
conv2d_3 (Conv2D) (None, 3686, 60, 32) 9248 max_pooling2d_1[0][0]
__________________________________________________________________________________________________
max_pooling1d_5 (MaxPooling1D) (None, 25, 128) 0 conv1d_5[0][0]
__________________________________________________________________________________________________
conv2d_4 (Conv2D) (None, 3684, 58, 32) 9248 conv2d_3[0][0]
__________________________________________________________________________________________________
conv1d_6 (Conv1D) (None, 21, 128) 82048 max_pooling1d_5[0][0]
__________________________________________________________________________________________________
max_pooling2d_2 (MaxPooling2D) (None, 1842, 29, 32) 0 conv2d_4[0][0]
__________________________________________________________________________________________________
max_pooling1d_6 (MaxPooling1D) (None, 10, 128) 0 conv1d_6[0][0]
__________________________________________________________________________________________________
flatten_2 (Flatten) (None, 1709376) 0 max_pooling2d_2[0][0]
__________________________________________________________________________________________________
flatten_1 (Flatten) (None, 1280) 0 max_pooling1d_6[0][0]
__________________________________________________________________________________________________
dense_2 (Dense) (None, 9) 15384393 flatten_2[0][0]
__________________________________________________________________________________________________
dense_1 (Dense) (None, 9) 11529 flatten_1[0][0]
__________________________________________________________________________________________________
concatenate_1 (Concatenate) (None, 18) 0 dense_2[0][0]
dense_1[0][0]
__________________________________________________________________________________________________
dense_3 (Dense) (None, 7) 133 concatenate_1[0][0]
==================================================================================================
Total params: 36,135,287
Trainable params: 36,135,287
Non-trainable params: 0
合并：
# ----------------------- 1D CNN ----------------------
in_1D = Input((7380, 128000))
# 1
model_1D = Conv1D(32, kernel_size= 5 , strides=1, activation='relu')(in_1D)
model_1D = MaxPooling1D(pool_size= 4, strides=4)(model_1D)
# 2
model_1D = Conv1D(32, kernel_size= 5 , strides=1 , activation='relu')(model_1D)
model_1D = MaxPooling1D(pool_size= 4, strides=4)(model_1D)
# 3
model_1D = Conv1D(64, kernel_size= 5 , strides=1 , activation='relu')(model_1D)
model_1D = MaxPooling1D(pool_size= 4, strides=4)(model_1D)
# 4
model_1D = Conv1D(64, kernel_size= 5 , strides=1 , activation='relu')(model_1D)
model_1D = MaxPooling1D(pool_size= 2, strides=2)(model_1D)
# 5
model_1D = Conv1D(128, kernel_size= 5 , strides= 1 , activation='relu')(model_1D)
model_1D = MaxPooling1D(pool_size= 2, strides= 2)(model_1D)
# 6
model_1D = Conv1D(128, kernel_size= 5 , strides= 1 , activation='relu')(model_1D)
model_1D = MaxPooling1D(pool_size= 2, strides= 2)(model_1D)
model_1D = Flatten()(model_1D)
model_1D = Dense(9 , activation='softmax')(model_1D)
# ----------------------- 2D CNN ----------------------
in_2D = Input((7380, 128, 251))
model_2D = Conv2D(32, kernel_size=(3, 3) , strides=(1,1), activation='relu')(in_2D)
model_2D = Conv2D(32, kernel_size=(3, 3) , strides=(1,1), activation='relu')(model_2D)
model_2D = MaxPooling2D(pool_size=(2, 2), strides=(2, 2))(model_2D)
model_2D = Conv2D(32, kernel_size=(3, 3) , strides=(1,1), activation='relu')(model_2D)
model_2D = Conv2D(32, kernel_size=(3, 3) , strides=(1,1), activation='relu')(model_2D)
model_2D = MaxPooling2D(pool_size=(2, 2), strides=(2, 2))(model_2D)
model_2D = Flatten()(model_2D)
model_2D = Dense(9 , activation='relu')(model_2D)
merged = Concatenate()([model_2D, model_1D])
output = Dense(7, activation='softmax')(merged)
model_combined = Model(inputs=[in_2D, in_1D], outputs=[output])
model_combined.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model_combined.summary()
Model: "model_1"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) (None, 7380, 128000) 0
__________________________________________________________________________________________________
conv1d_1 (Conv1D) (None, 7376, 32) 20480032 input_1[0][0]
__________________________________________________________________________________________________
max_pooling1d_1 (MaxPooling1D) (None, 1844, 32) 0 conv1d_1[0][0]
__________________________________________________________________________________________________
conv1d_2 (Conv1D) (None, 1840, 32) 5152 max_pooling1d_1[0][0]
__________________________________________________________________________________________________
max_pooling1d_2 (MaxPooling1D) (None, 460, 32) 0 conv1d_2[0][0]
__________________________________________________________________________________________________
conv1d_3 (Conv1D) (None, 456, 64) 10304 max_pooling1d_2[0][0]
__________________________________________________________________________________________________
input_2 (InputLayer) (None, 7380, 128, 25 0
__________________________________________________________________________________________________
max_pooling1d_3 (MaxPooling1D) (None, 114, 64) 0 conv1d_3[0][0]
__________________________________________________________________________________________________
conv2d_1 (Conv2D) (None, 7378, 126, 32 72320 input_2[0][0]
__________________________________________________________________________________________________
conv1d_4 (Conv1D) (None, 110, 64) 20544 max_pooling1d_3[0][0]
__________________________________________________________________________________________________
conv2d_2 (Conv2D) (None, 7376, 124, 32 9248 conv2d_1[0][0]
__________________________________________________________________________________________________
max_pooling1d_4 (MaxPooling1D) (None, 55, 64) 0 conv1d_4[0][0]
__________________________________________________________________________________________________
max_pooling2d_1 (MaxPooling2D) (None, 3688, 62, 32) 0 conv2d_2[0][0]
__________________________________________________________________________________________________
conv1d_5 (Conv1D) (None, 51, 128) 41088 max_pooling1d_4[0][0]
__________________________________________________________________________________________________
conv2d_3 (Conv2D) (None, 3686, 60, 32) 9248 max_pooling2d_1[0][0]
__________________________________________________________________________________________________
max_pooling1d_5 (MaxPooling1D) (None, 25, 128) 0 conv1d_5[0][0]
__________________________________________________________________________________________________
conv2d_4 (Conv2D) (None, 3684, 58, 32) 9248 conv2d_3[0][0]
__________________________________________________________________________________________________
conv1d_6 (Conv1D) (None, 21, 128) 82048 max_pooling1d_5[0][0]
__________________________________________________________________________________________________
max_pooling2d_2 (MaxPooling2D) (None, 1842, 29, 32) 0 conv2d_4[0][0]
__________________________________________________________________________________________________
max_pooling1d_6 (MaxPooling1D) (None, 10, 128) 0 conv1d_6[0][0]
__________________________________________________________________________________________________
flatten_2 (Flatten) (None, 1709376) 0 max_pooling2d_2[0][0]
__________________________________________________________________________________________________
flatten_1 (Flatten) (None, 1280) 0 max_pooling1d_6[0][0]
__________________________________________________________________________________________________
dense_2 (Dense) (None, 9) 15384393 flatten_2[0][0]
__________________________________________________________________________________________________
dense_1 (Dense) (None, 9) 11529 flatten_1[0][0]
__________________________________________________________________________________________________
concatenate_1 (Concatenate) (None, 18) 0 dense_2[0][0]
dense_1[0][0]
__________________________________________________________________________________________________
dense_3 (Dense) (None, 7) 133 concatenate_1[0][0]
==================================================================================================
Total params: 36,135,287
Trainable params: 36,135,287
Non-trainable params: 0
输出：
# ----------------------- 1D CNN ----------------------
in_1D = Input((7380, 128000))
# 1
model_1D = Conv1D(32, kernel_size= 5 , strides=1, activation='relu')(in_1D)
model_1D = MaxPooling1D(pool_size= 4, strides=4)(model_1D)
# 2
model_1D = Conv1D(32, kernel_size= 5 , strides=1 , activation='relu')(model_1D)
model_1D = MaxPooling1D(pool_size= 4, strides=4)(model_1D)
# 3
model_1D = Conv1D(64, kernel_size= 5 , strides=1 , activation='relu')(model_1D)
model_1D = MaxPooling1D(pool_size= 4, strides=4)(model_1D)
# 4
model_1D = Conv1D(64, kernel_size= 5 , strides=1 , activation='relu')(model_1D)
model_1D = MaxPooling1D(pool_size= 2, strides=2)(model_1D)
# 5
model_1D = Conv1D(128, kernel_size= 5 , strides= 1 , activation='relu')(model_1D)
model_1D = MaxPooling1D(pool_size= 2, strides= 2)(model_1D)
# 6
model_1D = Conv1D(128, kernel_size= 5 , strides= 1 , activation='relu')(model_1D)
model_1D = MaxPooling1D(pool_size= 2, strides= 2)(model_1D)
model_1D = Flatten()(model_1D)
model_1D = Dense(9 , activation='softmax')(model_1D)
# ----------------------- 2D CNN ----------------------
in_2D = Input((7380, 128, 251))
model_2D = Conv2D(32, kernel_size=(3, 3) , strides=(1,1), activation='relu')(in_2D)
model_2D = Conv2D(32, kernel_size=(3, 3) , strides=(1,1), activation='relu')(model_2D)
model_2D = MaxPooling2D(pool_size=(2, 2), strides=(2, 2))(model_2D)
model_2D = Conv2D(32, kernel_size=(3, 3) , strides=(1,1), activation='relu')(model_2D)
model_2D = Conv2D(32, kernel_size=(3, 3) , strides=(1,1), activation='relu')(model_2D)
model_2D = MaxPooling2D(pool_size=(2, 2), strides=(2, 2))(model_2D)
model_2D = Flatten()(model_2D)
model_2D = Dense(9 , activation='relu')(model_2D)
merged = Concatenate()([model_2D, model_1D])
output = Dense(7, activation='softmax')(merged)
model_combined = Model(inputs=[in_2D, in_1D], outputs=[output])
model_combined.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model_combined.summary()
Model: "model_1"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) (None, 7380, 128000) 0
__________________________________________________________________________________________________
conv1d_1 (Conv1D) (None, 7376, 32) 20480032 input_1[0][0]
__________________________________________________________________________________________________
max_pooling1d_1 (MaxPooling1D) (None, 1844, 32) 0 conv1d_1[0][0]
__________________________________________________________________________________________________
conv1d_2 (Conv1D) (None, 1840, 32) 5152 max_pooling1d_1[0][0]
__________________________________________________________________________________________________
max_pooling1d_2 (MaxPooling1D) (None, 460, 32) 0 conv1d_2[0][0]
__________________________________________________________________________________________________
conv1d_3 (Conv1D) (None, 456, 64) 10304 max_pooling1d_2[0][0]
__________________________________________________________________________________________________
input_2 (InputLayer) (None, 7380, 128, 25 0
__________________________________________________________________________________________________
max_pooling1d_3 (MaxPooling1D) (None, 114, 64) 0 conv1d_3[0][0]
__________________________________________________________________________________________________
conv2d_1 (Conv2D) (None, 7378, 126, 32 72320 input_2[0][0]
__________________________________________________________________________________________________
conv1d_4 (Conv1D) (None, 110, 64) 20544 max_pooling1d_3[0][0]
__________________________________________________________________________________________________
conv2d_2 (Conv2D) (None, 7376, 124, 32 9248 conv2d_1[0][0]
__________________________________________________________________________________________________
max_pooling1d_4 (MaxPooling1D) (None, 55, 64) 0 conv1d_4[0][0]
__________________________________________________________________________________________________
max_pooling2d_1 (MaxPooling2D) (None, 3688, 62, 32) 0 conv2d_2[0][0]
__________________________________________________________________________________________________
conv1d_5 (Conv1D) (None, 51, 128) 41088 max_pooling1d_4[0][0]
__________________________________________________________________________________________________
conv2d_3 (Conv2D) (None, 3686, 60, 32) 9248 max_pooling2d_1[0][0]
__________________________________________________________________________________________________
max_pooling1d_5 (MaxPooling1D) (None, 25, 128) 0 conv1d_5[0][0]
__________________________________________________________________________________________________
conv2d_4 (Conv2D) (None, 3684, 58, 32) 9248 conv2d_3[0][0]
__________________________________________________________________________________________________
conv1d_6 (Conv1D) (None, 21, 128) 82048 max_pooling1d_5[0][0]
__________________________________________________________________________________________________
max_pooling2d_2 (MaxPooling2D) (None, 1842, 29, 32) 0 conv2d_4[0][0]
__________________________________________________________________________________________________
max_pooling1d_6 (MaxPooling1D) (None, 10, 128) 0 conv1d_6[0][0]
__________________________________________________________________________________________________
flatten_2 (Flatten) (None, 1709376) 0 max_pooling2d_2[0][0]
__________________________________________________________________________________________________
flatten_1 (Flatten) (None, 1280) 0 max_pooling1d_6[0][0]
__________________________________________________________________________________________________
dense_2 (Dense) (None, 9) 15384393 flatten_2[0][0]
__________________________________________________________________________________________________
dense_1 (Dense) (None, 9) 11529 flatten_1[0][0]
__________________________________________________________________________________________________
concatenate_1 (Concatenate) (None, 18) 0 dense_2[0][0]
dense_1[0][0]
__________________________________________________________________________________________________
dense_3 (Dense) (None, 7) 133 concatenate_1[0][0]
==================================================================================================
Total params: 36,135,287
Trainable params: 36,135,287
Non-trainable params: 0
您可以通过仅在第一层向1D-CNN和2D-CNN提供输入形状来尝试函数式API，而不是顺序API。然后，通过在1D-CNN和2D-CNN中的密集层之前添加展平层，将解决您的问题。
您可以按照下面修改的代码进行操作。
1D-CNN:
# ----------------------- 1D CNN ----------------------
in_1D = Input((7380, 128000))
# 1
model_1D = Conv1D(32, kernel_size= 5 , strides=1, activation='relu')(in_1D)
model_1D = MaxPooling1D(pool_size= 4, strides=4)(model_1D)
# 2
model_1D = Conv1D(32, kernel_size= 5 , strides=1 , activation='relu')(model_1D)
model_1D = MaxPooling1D(pool_size= 4, strides=4)(model_1D)
# 3
model_1D = Conv1D(64, kernel_size= 5 , strides=1 , activation='relu')(model_1D)
model_1D = MaxPooling1D(pool_size= 4, strides=4)(model_1D)
# 4
model_1D = Conv1D(64, kernel_size= 5 , strides=1 , activation='relu')(model_1D)
model_1D = MaxPooling1D(pool_size= 2, strides=2)(model_1D)
# 5
model_1D = Conv1D(128, kernel_size= 5 , strides= 1 , activation='relu')(model_1D)
model_1D = MaxPooling1D(pool_size= 2, strides= 2)(model_1D)
# 6
model_1D = Conv1D(128, kernel_size= 5 , strides= 1 , activation='relu')(model_1D)
model_1D = MaxPooling1D(pool_size= 2, strides= 2)(model_1D)
model_1D = Flatten()(model_1D)
model_1D = Dense(9 , activation='softmax')(model_1D)
# ----------------------- 2D CNN ----------------------
in_2D = Input((7380, 128, 251))
model_2D = Conv2D(32, kernel_size=(3, 3) , strides=(1,1), activation='relu')(in_2D)
model_2D = Conv2D(32, kernel_size=(3, 3) , strides=(1,1), activation='relu')(model_2D)
model_2D = MaxPooling2D(pool_size=(2, 2), strides=(2, 2))(model_2D)
model_2D = Conv2D(32, kernel_size=(3, 3) , strides=(1,1), activation='relu')(model_2D)
model_2D = Conv2D(32, kernel_size=(3, 3) , strides=(1,1), activation='relu')(model_2D)
model_2D = MaxPooling2D(pool_size=(2, 2), strides=(2, 2))(model_2D)
model_2D = Flatten()(model_2D)
model_2D = Dense(9 , activation='relu')(model_2D)
merged = Concatenate()([model_2D, model_1D])
output = Dense(7, activation='softmax')(merged)
model_combined = Model(inputs=[in_2D, in_1D], outputs=[output])
model_combined.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model_combined.summary()
Model: "model_1"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) (None, 7380, 128000) 0
__________________________________________________________________________________________________
conv1d_1 (Conv1D) (None, 7376, 32) 20480032 input_1[0][0]
__________________________________________________________________________________________________
max_pooling1d_1 (MaxPooling1D) (None, 1844, 32) 0 conv1d_1[0][0]
__________________________________________________________________________________________________
conv1d_2 (Conv1D) (None, 1840, 32) 5152 max_pooling1d_1[0][0]
__________________________________________________________________________________________________
max_pooling1d_2 (MaxPooling1D) (None, 460, 32) 0 conv1d_2[0][0]
__________________________________________________________________________________________________
conv1d_3 (Conv1D) (None, 456, 64) 10304 max_pooling1d_2[0][0]
__________________________________________________________________________________________________
input_2 (InputLayer) (None, 7380, 128, 25 0
__________________________________________________________________________________________________
max_pooling1d_3 (MaxPooling1D) (None, 114, 64) 0 conv1d_3[0][0]
__________________________________________________________________________________________________
conv2d_1 (Conv2D) (None, 7378, 126, 32 72320 input_2[0][0]
__________________________________________________________________________________________________
conv1d_4 (Conv1D) (None, 110, 64) 20544 max_pooling1d_3[0][0]
__________________________________________________________________________________________________
conv2d_2 (Conv2D) (None, 7376, 124, 32 9248 conv2d_1[0][0]
__________________________________________________________________________________________________
max_pooling1d_4 (MaxPooling1D) (None, 55, 64) 0 conv1d_4[0][0]
__________________________________________________________________________________________________
max_pooling2d_1 (MaxPooling2D) (None, 3688, 62, 32) 0 conv2d_2[0][0]
__________________________________________________________________________________________________
conv1d_5 (Conv1D) (None, 51, 128) 41088 max_pooling1d_4[0][0]
__________________________________________________________________________________________________
conv2d_3 (Conv2D) (None, 3686, 60, 32) 9248 max_pooling2d_1[0][0]
__________________________________________________________________________________________________
max_pooling1d_5 (MaxPooling1D) (None, 25, 128) 0 conv1d_5[0][0]
__________________________________________________________________________________________________
conv2d_4 (Conv2D) (None, 3684, 58, 32) 9248 conv2d_3[0][0]
__________________________________________________________________________________________________
conv1d_6 (Conv1D) (None, 21, 128) 82048 max_pooling1d_5[0][0]
__________________________________________________________________________________________________
max_pooling2d_2 (MaxPooling2D) (None, 1842, 29, 32) 0 conv2d_4[0][0]
__________________________________________________________________________________________________
max_pooling1d_6 (MaxPooling1D) (None, 10, 128) 0 conv1d_6[0][0]
__________________________________________________________________________________________________
flatten_2 (Flatten) (None, 1709376) 0 max_pooling2d_2[0][0]
__________________________________________________________________________________________________
flatten_1 (Flatten) (None, 1280) 0 max_pooling1d_6[0][0]
__________________________________________________________________________________________________
dense_2 (Dense) (None, 9) 15384393 flatten_2[0][0]
__________________________________________________________________________________________________
dense_1 (Dense) (None, 9) 11529 flatten_1[0][0]
__________________________________________________________________________________________________
concatenate_1 (Concatenate) (None, 18) 0 dense_2[0][0]
dense_1[0][0]
__________________________________________________________________________________________________
dense_3 (Dense) (None, 7) 133 concatenate_1[0][0]
==================================================================================================
Total params: 36,135,287
Trainable params: 36,135,287
Non-trainable params: 0
2D-CNN:
# ----------------------- 1D CNN ----------------------
in_1D = Input((7380, 128000))
# 1
model_1D = Conv1D(32, kernel_size= 5 , strides=1, activation='relu')(in_1D)
model_1D = MaxPooling1D(pool_size= 4, strides=4)(model_1D)
# 2
model_1D = Conv1D(32, kernel_size= 5 , strides=1 , activation='relu')(model_1D)
model_1D = MaxPooling1D(pool_size= 4, strides=4)(model_1D)
# 3
model_1D = Conv1D(64, kernel_size= 5 , strides=1 , activation='relu')(model_1D)
model_1D = MaxPooling1D(pool_size= 4, strides=4)(model_1D)
# 4
model_1D = Conv1D(64, kernel_size= 5 , strides=1 , activation='relu')(model_1D)
model_1D = MaxPooling1D(pool_size= 2, strides=2)(model_1D)
# 5
model_1D = Conv1D(128, kernel_size= 5 , strides= 1 , activation='relu')(model_1D)
model_1D = MaxPooling1D(pool_size= 2, strides= 2)(model_1D)
# 6
model_1D = Conv1D(128, kernel_size= 5 , strides= 1 , activation='relu')(model_1D)
model_1D = MaxPooling1D(pool_size= 2, strides= 2)(model_1D)
model_1D = Flatten()(model_1D)
model_1D = Dense(9 , activation='softmax')(model_1D)
# ----------------------- 2D CNN ----------------------
in_2D = Input((7380, 128, 251))
model_2D = Conv2D(32, kernel_size=(3, 3) , strides=(1,1), activation='relu')(in_2D)
model_2D = Conv2D(32, kernel_size=(3, 3) , strides=(1,1), activation='relu')(model_2D)
model_2D = MaxPooling2D(pool_size=(2, 2), strides=(2, 2))(model_2D)
model_2D = Conv2D(32, kernel_size=(3, 3) , strides=(1,1), activation='relu')(model_2D)
model_2D = Conv2D(32, kernel_size=(3, 3) , strides=(1,1), activation='relu')(model_2D)
model_2D = MaxPooling2D(pool_size=(2, 2), strides=(2, 2))(model_2D)
model_2D = Flatten()(model_2D)
model_2D = Dense(9 , activation='relu')(model_2D)
merged = Concatenate()([model_2D, model_1D])
output = Dense(7, activation='softmax')(merged)
model_combined = Model(inputs=[in_2D, in_1D], outputs=[output])
model_combined.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model_combined.summary()
Model: "model_1"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) (None, 7380, 128000) 0
__________________________________________________________________________________________________
conv1d_1 (Conv1D) (None, 7376, 32) 20480032 input_1[0][0]
__________________________________________________________________________________________________
max_pooling1d_1 (MaxPooling1D) (None, 1844, 32) 0 conv1d_1[0][0]
__________________________________________________________________________________________________
conv1d_2 (Conv1D) (None, 1840, 32) 5152 max_pooling1d_1[0][0]
__________________________________________________________________________________________________
max_pooling1d_2 (MaxPooling1D) (None, 460, 32) 0 conv1d_2[0][0]
__________________________________________________________________________________________________
conv1d_3 (Conv1D) (None, 456, 64) 10304 max_pooling1d_2[0][0]
__________________________________________________________________________________________________
input_2 (InputLayer) (None, 7380, 128, 25 0
__________________________________________________________________________________________________
max_pooling1d_3 (MaxPooling1D) (None, 114, 64) 0 conv1d_3[0][0]
__________________________________________________________________________________________________
conv2d_1 (Conv2D) (None, 7378, 126, 32 72320 input_2[0][0]
__________________________________________________________________________________________________
conv1d_4 (Conv1D) (None, 110, 64) 20544 max_pooling1d_3[0][0]
__________________________________________________________________________________________________
conv2d_2 (Conv2D) (None, 7376, 124, 32 9248 conv2d_1[0][0]
__________________________________________________________________________________________________
max_pooling1d_4 (MaxPooling1D) (None, 55, 64) 0 conv1d_4[0][0]
__________________________________________________________________________________________________
max_pooling2d_1 (MaxPooling2D) (None, 3688, 62, 32) 0 conv2d_2[0][0]
__________________________________________________________________________________________________
conv1d_5 (Conv1D) (None, 51, 128) 41088 max_pooling1d_4[0][0]
__________________________________________________________________________________________________
conv2d_3 (Conv2D) (None, 3686, 60, 32) 9248 max_pooling2d_1[0][0]
__________________________________________________________________________________________________
max_pooling1d_5 (MaxPooling1D) (None, 25, 128) 0 conv1d_5[0][0]
__________________________________________________________________________________________________
conv2d_4 (Conv2D) (None, 3684, 58, 32) 9248 conv2d_3[0][0]
__________________________________________________________________________________________________
conv1d_6 (Conv1D) (None, 21, 128) 82048 max_pooling1d_5[0][0]
__________________________________________________________________________________________________
max_pooling2d_2 (MaxPooling2D) (None, 1842, 29, 32) 0 conv2d_4[0][0]
__________________________________________________________________________________________________
max_pooling1d_6 (MaxPooling1D) (None, 10, 128) 0 conv1d_6[0][0]
__________________________________________________________________________________________________
flatten_2 (Flatten) (None, 1709376) 0 max_pooling2d_2[0][0]
__________________________________________________________________________________________________
flatten_1 (Flatten) (None, 1280) 0 max_pooling1d_6[0][0]
__________________________________________________________________________________________________
dense_2 (Dense) (None, 9) 15384393 flatten_2[0][0]
__________________________________________________________________________________________________
dense_1 (Dense) (None, 9) 11529 flatten_1[0][0]
__________________________________________________________________________________________________
concatenate_1 (Concatenate) (None, 18) 0 dense_2[0][0]
dense_1[0][0]
__________________________________________________________________________________________________
dense_3 (Dense) (None, 7) 133 concatenate_1[0][0]
==================================================================================================
Total params: 36,135,287
Trainable params: 36,135,287
Non-trainable params: 0
合并：
# ----------------------- 1D CNN ----------------------
in_1D = Input((7380, 128000))
# 1
model_1D = Conv1D(32, kernel_size= 5 , strides=1, activation='relu')(in_1D)
model_1D = MaxPooling1D(pool_size= 4, strides=4)(model_1D)
# 2
model_1D = Conv1D(32, kernel_size= 5 , strides=1 , activation='relu')(model_1D)
model_1D = MaxPooling1D(pool_size= 4, strides=4)(model_1D)
# 3
model_1D = Conv1D(64, kernel_size= 5 , strides=1 , activation='relu')(model_1D)
model_1D = MaxPooling1D(pool_size= 4, strides=4)(model_1D)
# 4
model_1D = Conv1D(64, kernel_size= 5 , strides=1 , activation='relu')(model_1D)
model_1D = MaxPooling1D(pool_size= 2, strides=2)(model_1D)
# 5
model_1D = Conv1D(128, kernel_size= 5 , strides= 1 , activation='relu')(model_1D)
model_1D = MaxPooling1D(pool_size= 2, strides= 2)(model_1D)
# 6
model_1D = Conv1D(128, kernel_size= 5 , strides= 1 , activation='relu')(model_1D)
model_1D = MaxPooling1D(pool_size= 2, strides= 2)(model_1D)
model_1D = Flatten()(model_1D)
model_1D = Dense(9 , activation='softmax')(model_1D)
# ----------------------- 2D CNN ----------------------
in_2D = Input((7380, 128, 251))
model_2D = Conv2D(32, kernel_size=(3, 3) , strides=(1,1), activation='relu')(in_2D)
model_2D = Conv2D(32, kernel_size=(3, 3) , strides=(1,1), activation='relu')(model_2D)
model_2D = MaxPooling2D(pool_size=(2, 2), strides=(2, 2))(model_2D)
model_2D = Conv2D(32, kernel_size=(3, 3) , strides=(1,1), activation='relu')(model_2D)
model_2D = Conv2D(32, kernel_size=(3, 3) , strides=(1,1), activation='relu')(model_2D)
model_2D = MaxPooling2D(pool_size=(2, 2), strides=(2, 2))(model_2D)
model_2D = Flatten()(model_2D)
model_2D = Dense(9 , activation='relu')(model_2D)
merged = Concatenate()([model_2D, model_1D])
output = Dense(7, activation='softmax')(merged)
model_combined = Model(inputs=[in_2D, in_1D], outputs=[output])
model_combined.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model_combined.summary()
Model: "model_1"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) (None, 7380, 128000) 0
__________________________________________________________________________________________________
conv1d_1 (Conv1D) (None, 7376, 32) 20480032 input_1[0][0]
__________________________________________________________________________________________________
max_pooling1d_1 (MaxPooling1D) (None, 1844, 32) 0 conv1d_1[0][0]
__________________________________________________________________________________________________
conv1d_2 (Conv1D) (None, 1840, 32) 5152 max_pooling1d_1[0][0]
__________________________________________________________________________________________________
max_pooling1d_2 (MaxPooling1D) (None, 460, 32) 0 conv1d_2[0][0]
__________________________________________________________________________________________________
conv1d_3 (Conv1D) (None, 456, 64) 10304 max_pooling1d_2[0][0]
__________________________________________________________________________________________________
input_2 (InputLayer) (None, 7380, 128, 25 0
__________________________________________________________________________________________________
max_pooling1d_3 (MaxPooling1D) (None, 114, 64) 0 conv1d_3[0][0]
__________________________________________________________________________________________________
conv2d_1 (Conv2D) (None, 7378, 126, 32 72320 input_2[0][0]
__________________________________________________________________________________________________
conv1d_4 (Conv1D) (None, 110, 64) 20544 max_pooling1d_3[0][0]
__________________________________________________________________________________________________
conv2d_2 (Conv2D) (None, 7376, 124, 32 9248 conv2d_1[0][0]
__________________________________________________________________________________________________
max_pooling1d_4 (MaxPooling1D) (None, 55, 64) 0 conv1d_4[0][0]
__________________________________________________________________________________________________
max_pooling2d_1 (MaxPooling2D) (None, 3688, 62, 32) 0 conv2d_2[0][0]
__________________________________________________________________________________________________
conv1d_5 (Conv1D) (None, 51, 128) 41088 max_pooling1d_4[0][0]
__________________________________________________________________________________________________
conv2d_3 (Conv2D) (None, 3686, 60, 32) 9248 max_pooling2d_1[0][0]
__________________________________________________________________________________________________
max_pooling1d_5 (MaxPooling1D) (None, 25, 128) 0 conv1d_5[0][0]
__________________________________________________________________________________________________
conv2d_4 (Conv2D) (None, 3684, 58, 32) 9248 conv2d_3[0][0]
__________________________________________________________________________________________________
conv1d_6 (Conv1D) (None, 21, 128) 82048 max_pooling1d_5[0][0]
__________________________________________________________________________________________________
max_pooling2d_2 (MaxPooling2D) (None, 1842, 29, 32) 0 conv2d_4[0][0]
__________________________________________________________________________________________________
max_pooling1d_6 (MaxPooling1D) (None, 10, 128) 0 conv1d_6[0][0]
__________________________________________________________________________________________________
flatten_2 (Flatten) (None, 1709376) 0 max_pooling2d_2[0][0]
__________________________________________________________________________________________________
flatten_1 (Flatten) (None, 1280) 0 max_pooling1d_6[0][0]
__________________________________________________________________________________________________
dense_2 (Dense) (None, 9) 15384393 flatten_2[0][0]
__________________________________________________________________________________________________
dense_1 (Dense) (None, 9) 11529 flatten_1[0][0]
__________________________________________________________________________________________________
concatenate_1 (Concatenate) (None, 18) 0 dense_2[0][0]
dense_1[0][0]
__________________________________________________________________________________________________
dense_3 (Dense) (None, 7) 133 concatenate_1[0][0]
==================================================================================================
Total params: 36,135,287
Trainable params: 36,135,287
Non-trainable params: 0
输出：
# ----------------------- 1D CNN ----------------------
in_1D = Input((7380, 128000))
# 1
model_1D = Conv1D(32, kernel_size= 5 , strides=1, activation='relu')(in_1D)
model_1D = MaxPooling1D(pool_size= 4, strides=4)(model_1D)
# 2
model_1D = Conv1D(32, kernel_size= 5 , strides=1 , activation='relu')(model_1D)
model_1D = MaxPooling1D(pool_size= 4, strides=4)(model_1D)
# 3
model_1D = Conv1D(64, kernel_size= 5 , strides=1 , activation='relu')(model_1D)
model_1D = MaxPooling1D(pool_size= 4, strides=4)(model_1D)
# 4
model_1D = Conv1D(64, kernel_size= 5 , strides=1 , activation='relu')(model_1D)
model_1D = MaxPooling1D(pool_size= 2, strides=2)(model_1D)
# 5
model_1D = Conv1D(128, kernel_size= 5 , strides= 1 , activation='relu')(model_1D)
model_1D = MaxPooling1D(pool_size= 2, strides= 2)(model_1D)
# 6
model_1D = Conv1D(128, kernel_size= 5 , strides= 1 , activation='relu')(model_1D)
model_1D = MaxPooling1D(pool_size= 2, strides= 2)(model_1D)
model_1D = Flatten()(model_1D)
model_1D = Dense(9 , activation='softmax')(model_1D)
# ----------------------- 2D CNN ----------------------
in_2D = Input((7380, 128, 251))
model_2D = Conv2D(32, kernel_size=(3, 3) , strides=(1,1), activation='relu')(in_2D)
model_2D = Conv2D(32, kernel_size=(3, 3) , strides=(1,1), activation='relu')(model_2D)
model_2D = MaxPooling2D(pool_size=(2, 2), strides=(2, 2))(model_2D)
model_2D = Conv2D(32, kernel_size=(3, 3) , strides=(1,1), activation='relu')(model_2D)
model_2D = Conv2D(32, kernel_size=(3, 3) , strides=(1,1), activation='relu')(model_2D)
model_2D = MaxPooling2D(pool_size=(2, 2), strides=(2, 2))(model_2D)
model_2D = Flatten()(model_2D)
model_2D = Dense(9 , activation='relu')(model_2D)
merged = Concatenate()([model_2D, model_1D])
output = Dense(7, activation='softmax')(merged)
model_combined = Model(inputs=[in_2D, in_1D], outputs=[output])
model_combined.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model_combined.summary()
Model: "model_1"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) (None, 7380, 128000) 0
__________________________________________________________________________________________________
conv1d_1 (Conv1D) (None, 7376, 32) 20480032 input_1[0][0]
__________________________________________________________________________________________________
max_pooling1d_1 (MaxPooling1D) (None, 1844, 32) 0 conv1d_1[0][0]
__________________________________________________________________________________________________
conv1d_2 (Conv1D) (None, 1840, 32) 5152 max_pooling1d_1[0][0]
__________________________________________________________________________________________________
max_pooling1d_2 (MaxPooling1D) (None, 460, 32) 0 conv1d_2[0][0]
__________________________________________________________________________________________________
conv1d_3 (Conv1D) (None, 456, 64) 10304 max_pooling1d_2[0][0]
__________________________________________________________________________________________________
input_2 (InputLayer) (None, 7380, 128, 25 0
__________________________________________________________________________________________________
max_pooling1d_3 (MaxPooling1D) (None, 114, 64) 0 conv1d_3[0][0]
__________________________________________________________________________________________________
conv2d_1 (Conv2D) (None, 7378, 126, 32 72320 input_2[0][0]
__________________________________________________________________________________________________
conv1d_4 (Conv1D) (None, 110, 64) 20544 max_pooling1d_3[0][0]
__________________________________________________________________________________________________
conv2d_2 (Conv2D) (None, 7376, 124, 32 9248 conv2d_1[0][0]
__________________________________________________________________________________________________
max_pooling1d_4 (MaxPooling1D) (None, 55, 64) 0 conv1d_4[0][0]
__________________________________________________________________________________________________
max_pooling2d_1 (MaxPooling2D) (None, 3688, 62, 32) 0 conv2d_2[0][0]
__________________________________________________________________________________________________
conv1d_5 (Conv1D) (None, 51, 128) 41088 max_pooling1d_4[0][0]
__________________________________________________________________________________________________
conv2d_3 (Conv2D) (None, 3686, 60, 32) 9248 max_pooling2d_1[0][0]
__________________________________________________________________________________________________
max_pooling1d_5 (MaxPooling1D) (None, 25, 128) 0 conv1d_5[0][0]
__________________________________________________________________________________________________
conv2d_4 (Conv2D) (None, 3684, 58, 32) 9248 conv2d_3[0][0]
__________________________________________________________________________________________________
conv1d_6 (Conv1D) (None, 21, 128) 82048 max_pooling1d_5[0][0]
__________________________________________________________________________________________________
max_pooling2d_2 (MaxPooling2D) (None, 1842, 29, 32) 0 conv2d_4[0][0]
__________________________________________________________________________________________________
max_pooling1d_6 (MaxPooling1D) (None, 10, 128) 0 conv1d_6[0][0]
__________________________________________________________________________________________________
flatten_2 (Flatten) (None, 1709376) 0 max_pooling2d_2[0][0]
__________________________________________________________________________________________________
flatten_1 (Flatten) (None, 1280) 0 max_pooling1d_6[0][0]
__________________________________________________________________________________________________
dense_2 (Dense) (None, 9) 15384393 flatten_2[0][0]
__________________________________________________________________________________________________
dense_1 (Dense) (None, 9) 11529 flatten_1[0][0]
__________________________________________________________________________________________________
concatenate_1 (Concatenate) (None, 18) 0 dense_2[0][0]
dense_1[0][0]
__________________________________________________________________________________________________
dense_3 (Dense) (None, 7) 133 concatenate_1[0][0]
==================================================================================================
Total params: 36,135,287
Trainable params: 36,135,287
Non-trainable params: 0