How to concatenate two layers in keras

Question

I have an example of a neural network with two layers  The first layer takes two arguments and has one output  The second should take one argument as result of the first layer and one additional argument  It should looks like this   x1  x2  x3             y1                 y2   So  I d created a model with two layers and tried to merge them but it returns an error  The first layer in a Sequential model must get an  input shape  or  batch input shape  argument  on the line result add merged    Model   first   Sequential   first add Dense 1  input shape  2    activation  sigmoid     second   Sequential   second add Dense 1  input shape  1    activation  sigmoid     result   Sequential   merged   Concatenate  first  second   ada grad   Adagrad lr 0 1  epsilon 1e-08  decay 0 0  result add merged  result compile optimizer ada grad  loss  loss tensor  metrics   accuracy

User · Answer

Adding to the above-accepted answer so that it helps those who are using tensorflow 2.0


import tensorflow as tf

# some data
c1 = tf.constant([[1, 1, 1], [2, 2, 2]], dtype=tf.float32)
c2 = tf.constant([[2, 2, 2], [3, 3, 3]], dtype=tf.float32)
c3 = tf.constant([[3, 3, 3], [4, 4, 4]], dtype=tf.float32)

# bake layers x1, x2, x3
x1 = tf.keras.layers.Dense(10)(c1)
x2 = tf.keras.layers.Dense(10)(c2)
x3 = tf.keras.layers.Dense(10)(c3)

# merged layer y1
y1 = tf.keras.layers.Concatenate(axis=1)([x1, x2])

# merged layer y2
y2 = tf.keras.layers.Concatenate(axis=1)([y1, x3])

# print info
print("-"*30)
print("x1", x1.shape, "x2", x2.shape, "x3", x3.shape)
print("y1", y1.shape)
print("y2", y2.shape)
print("-"*30)

Result:

------------------------------
x1 (2, 10) x2 (2, 10) x3 (2, 10)
y1 (2, 20)
y2 (2, 30)
------------------------------

User · Answer

You re getting the error because result defined as Sequential   is just a container for the model and you have not defined an input for it   Given what you re trying to build set result to take the third input x3  first   Sequential   first add Dense 1  input shape  2    activation  sigmoid     second   Sequential   second add Dense 1  input shape  1    activation  sigmoid     third   Sequential     of course you must provide the input to result which will be your x3 third add Dense 1  input shape  1    activation  sigmoid       lets say you add a few more layers to first and second    concatenate them merged   Concatenate  first  second      then concatenate the two outputs  result   Concatenate  merged   third    ada grad   Adagrad lr 0 1  epsilon 1e-08  decay 0 0   result compile optimizer ada grad  loss  binary crossentropy                  metrics   accuracy     However  my preferred way of building a model that has this type of input structure would be to use the functional api  Here is an implementation of your requirements to get you started  from keras models import Model from keras layers import Concatenate  Dense  LSTM  Input  concatenate from keras optimizers import Adagrad  first input   Input shape  2     first dense   Dense 1    first input   second input   Input shape  2     second dense   Dense 1    second input   merge one   concatenate  first dense  second dense    third input   Input shape  1     merge two   concatenate  merge one  third input    model   Model inputs  first input  second input  third input   outputs merge two  ada grad   Adagrad lr 0 1  epsilon 1e-08  decay 0 0  model compile optimizer ada grad  loss  binary crossentropy                  metrics   accuracy     To answer the question in the comments   How are result and merged connected  Assuming you mean how are they concatenated   Concatenation works like this    a        b         c a b c   g h i    a b c g h i d e f   j k l    d e f j k l  i e rows are just joined   Now  x1 is input to first  x2 is input into second and x3 input into third

User · Answer

You can experiment with model summary    notice the concatenate XX  Concatenate  layer size     merge samples  two input must be same shape inp1   Input shape  10 32   inp2   Input shape  10 32   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      merge row must same column size inp1   Input shape  20 10   inp2   Input shape  32 10   cc1   concatenate  inp1  inp2  axis 1  output   Dense 30  activation  relu   cc1  model   Model inputs  inp1  inp2   outputs output  model summary      merge column must same row size inp1   Input shape  10 20   inp2   Input shape  10 32   cc1   concatenate  inp1  inp2  axis 1  output   Dense 30  activation  relu   cc1  model   Model inputs  inp1  inp2   outputs output  model summary     You can view notebook here for detail  https   nbviewer jupyter org github anhhh11 DeepLearning blob master Concanate two layer keras ipynb

[python] How to concatenate two layers in keras?

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