traffic-model3-build-model.py

from keras.models import Sequential
from keras.optimizers import Adam # Other optimisers are available

# Create the MLP and CNN models
mlp = create_mlp(trainAttrX.shape[1])
cnn = create_cnn(128, 128, 3)
 
# Create the input to the final set of layers as the output of both the MLP and CNN
combinedInput = concatenate([mlp.output, cnn.output])

# The final fully-connected layer head will have two dense layers (one relu and one sigmoid)
x = Dense(4, activation="relu")(combinedInput)
x = Dense(1, activation="sigmoid")(x)

# The final model accepts numerical data on the MLP input and images on the CNN input, outputting a single value
model1 = Model(inputs=[mlp.input, cnn.input], outputs=x)

# Compile the model 
opt = Adam(lr=1e-3, decay=1e-3 / 200)
model1.compile(loss="binary_crossentropy", metrics=['acc'], optimizer=opt)
 
# Train the model
model1_history = model1.fit(
  [trainAttrX, trainImagesX], 
  trainY, 
  validation_data=([testAttrX, testImagesX], testY), 
  epochs=5, 
  batch_size=10)