In [42]:
import numpy 
import numpy as np
from keras.models import Model
from keras.layers import Dense, Merge, concatenate, Input
from keras.layers import LSTM
from keras.utils import np_utils
In [43]:
inp1 = Input(shape=(10,20))
inp2 = Input(shape=(10,32))
cc1 = concatenate([inp1, inp2],axis=2) # Merge column, same row
output = Dense(30, activation='relu')(cc1)
In [44]:
model = Model(inputs=[inp1, inp2], outputs=output)
In [45]:
model.summary()
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_40 (InputLayer)           (None, 10, 20)       0                                            
__________________________________________________________________________________________________
input_41 (InputLayer)           (None, 10, 32)       0                                            
__________________________________________________________________________________________________
concatenate_21 (Concatenate)    (None, 10, 52)       0           input_40[0][0]                   
                                                                 input_41[0][0]                   
__________________________________________________________________________________________________
dense_12 (Dense)                (None, 10, 30)       1590        concatenate_21[0][0]             
==================================================================================================
Total params: 1,590
Trainable params: 1,590
Non-trainable params: 0
__________________________________________________________________________________________________
In [46]:
inp1 = Input(shape=(20,10))
inp2 = Input(shape=(32,10))
cc1 = concatenate([inp1, inp2],axis=1) # Merge row, same column
output = Dense(30, activation='relu')(cc1)
model = Model(inputs=[inp1, inp2], outputs=output)
model.summary()
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_42 (InputLayer)           (None, 20, 10)       0                                            
__________________________________________________________________________________________________
input_43 (InputLayer)           (None, 32, 10)       0                                            
__________________________________________________________________________________________________
concatenate_22 (Concatenate)    (None, 52, 10)       0           input_42[0][0]                   
                                                                 input_43[0][0]                   
__________________________________________________________________________________________________
dense_13 (Dense)                (None, 52, 30)       330         concatenate_22[0][0]             
==================================================================================================
Total params: 330
Trainable params: 330
Non-trainable params: 0
__________________________________________________________________________________________________
In [47]:
inp1 = Input(shape=(10,10))
inp2 = Input(shape=(10,10))
cc1 = concatenate([inp1, inp2],axis=0) # Merge data must same row column
output = Dense(30, activation='relu')(cc1)
model = Model(inputs=[inp1, inp2], outputs=output)
model.summary()
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_44 (InputLayer)           (None, 10, 10)       0                                            
__________________________________________________________________________________________________
input_45 (InputLayer)           (None, 10, 10)       0                                            
__________________________________________________________________________________________________
concatenate_23 (Concatenate)    (None, 10, 10)       0           input_44[0][0]                   
                                                                 input_45[0][0]                   
__________________________________________________________________________________________________
dense_14 (Dense)                (None, 10, 30)       330         concatenate_23[0][0]             
==================================================================================================
Total params: 330
Trainable params: 330
Non-trainable params: 0
__________________________________________________________________________________________________
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