How to "Merge" Sequential models in Keras 2.0?

try this demo with keras==2.2.4 and tensorflow==1.13.1:

from keras import Sequential, Model
from keras.layers import Embedding, GlobalAveragePooling1D, Dense, concatenate
import numpy as np

model1 = Sequential()
model1.add(Embedding(20, 10, trainable=True))
model1.add(GlobalAveragePooling1D())
model1.add(Dense(1, activation='sigmoid'))
model2 = Sequential()
model2.add(Embedding(20, 10, trainable=True))
model2.add(GlobalAveragePooling1D())
model2.add(Dense(1, activation='sigmoid'))

model_concat = concatenate([model1.output, model2.output], axis=-1)
model_concat = Dense(1, activation='softmax')(model_concat)
model = Model(inputs=[model1.input, model2.input], outputs=model_concat)

model.compile(loss='binary_crossentropy', optimizer='adam')

X_train_1 = np.random.randint(0, 20, (10000, 256))
X_train_2 = np.random.randint(0, 20, (10000, 256))
Y_train = np.random.randint(0, 2, 10000)

model.fit([X_train_1, X_train_2], Y_train, batch_size=1000, epochs=200,
              verbose=True)

Unless you have a good reason to keep the models separated, you can (and should) have the same topology in a single model. Something like:

input1 = Input(shape=(27, 27, 1))
dense1 = Dense(300, activation='relu', name='layer_1')(input1)
input2 = Input(shape=(27, 27, 1))
dense2 = Dense(300, activation='relu', name='layer_2')(input2)
merged = concatenate([dense1, dense2])
out = Dense(1, activation='softmax', name='output_layer')(merged)
model = Model(inputs = [input1, input2], outputs = [out])

What that warning is saying is that instead of using the Merge layer with a specific mode, the different modes have now been split into their own individual layers.

So Merge(mode='concat') is now concatenate(axis=-1).

However, since you want to merge models not layers, this will not work in your case. What you will need to do is use the functional model since this behavior is no longer supported with the basic Sequential model type.

In your case that means the code should be changed to the following:

from keras.layers.merge import concatenate
from keras.models import Model, Sequential
from keras.layers import Dense, Input

model1_in = Input(shape=(27, 27, 1))
model1_out = Dense(300, input_dim=40, activation='relu', name='layer_1')(model1_in)
model1 = Model(model1_in, model1_out)

model2_in = Input(shape=(27, 27, 1))
model2_out = Dense(300, input_dim=40, activation='relu', name='layer_2')(model2_in)
model2 = Model(model2_in, model2_out)


concatenated = concatenate([model1_out, model2_out])
out = Dense(1, activation='softmax', name='output_layer')(concatenated)

merged_model = Model([model1_in, model2_in], out)
merged_model.compile(loss='binary_crossentropy', optimizer='adam', 
metrics=['accuracy'])

checkpoint = ModelCheckpoint('weights.h5', monitor='val_acc',
save_best_only=True, verbose=2)
early_stopping = EarlyStopping(monitor="val_loss", patience=5)

merged_model.fit([x1, x2], y=y, batch_size=384, epochs=200,
             verbose=1, validation_split=0.1, shuffle=True, 
callbacks=[early_stopping, checkpoint])