How to concatenate two layers in keras?

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 x3third.add(Dense(1, input_shape=(1,), activation='sigmoid'))# lets say you add a few more layers to first and second.# concatenate themmerged = Concatenate([first, second])# then concatenate the two outputsresult = 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 Modelfrom keras.layers import Concatenate, Dense, LSTM, Input, concatenatefrom keras.optimizers import Adagradfirst_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:

1. How are result and merged connected? Assuming you mean how are they concatenated.

Concatenation works like this:

  a        b         ca b c   g h i    a b c g h id e f   j k l    d e f j k l

i.e rows are just joined.

2. Now, x1 is input to first, x2 is input into second and x3 input into third.

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

import tensorflow as tf# some datac1 = 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, x3x1 = tf.keras.layers.Dense(10)(c1)x2 = tf.keras.layers.Dense(10)(c2)x3 = tf.keras.layers.Dense(10)(c3)# merged layer y1y1 = tf.keras.layers.Concatenate(axis=1)([x1, x2])# merged layer y2y2 = tf.keras.layers.Concatenate(axis=1)([y1, x3])# print infoprint("-"*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)------------------------------

You can experiment with model.summary() (notice the concatenate_XX (Concatenate) layer size)

# merge samples, two input must be same shapeinp1 = Input(shape=(10,32))inp2 = Input(shape=(10,32))cc1 = concatenate([inp1, inp2],axis=0) # Merge data must same row columnoutput = Dense(30, activation='relu')(cc1)model = Model(inputs=[inp1, inp2], outputs=output)model.summary()# merge row must same column sizeinp1 = 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 sizeinp1 = 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