Below are some keras concatenate examples, the main reason for this error is the result defined as Sequential() is just a container for the model and you have not defined input for it.
It looks like you’re trying to build a set result to take the third input x3.
Improvised code for your problem:
first = Sequential()
first.add(Dense(1, input_shape=(2,), activation='sigmoid'))
second = Sequential()
second.add(Dense(1, input_shape=(1,), activation='sigmoid'))
third = Sequential()
# provide the input to result with will be your x3
third.add(Dense(1, input_shape=(1,), activation='sigmoid'))
#then 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'])
You can try another way of building a model that this type of input structure would be to use the functional API.
For example:
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'])
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.
2) You can say that x1 is input to first, x2 is input into second and x3 input into third.
I hope this solution solved your problem.
