multi_outputs.py

from keras import layers
from keras import Input
from keras.models import Model

vocabulary_size = 50000
num_income_groups = 10

posts_input = Input(shape=(None,), dtype='int32', name='posts')

embedded_posts = layers.Embedding(256, vocabulary_size)(posts_input)
x = layers.Conv1D(128, 5, activation='relu')(embedded_posts)
x = layers.MaxPooling1D(5)(x)
x = layers.Conv1D(256, 5, activation='relu')(x)
x = layers.Conv1D(256, 5, activation='relu')(x)
x = layers.MaxPooling1D(5)(x)
x = layers.Conv1D(256, 5, activation='relu')(x)
x = layers.Conv1D(256, 5, activation='relu')(x)
x = layers.GlobalMaxPooling1D()(x)
x = layers.Dense(128, activation='relu')(x)

age_prediction = layers.Dense(1, name='age')(x)
income_prediction = layers.Dense(num_income_groups, activation='softmax', name='income')(x)
gender_prediction = layers.Dense(1, activation='sigmoid', name='gender')(x)

model = Model(posts_input, [age_prediction, income_prediction, gender_prediction])

# model.compile(optimizer='rmsprop',
#               loss=['mse', 'categorical_crossentropy', 'binary_crossentropy'])
# model.compile(optimizer='rmsprop',
#               loss={'age': 'mse','income': 'categorical_crossentropy','gender': 'binary_crossentropy'}) #only if you give names to the output layers
# model.compile(optimizer='rmsprop',
#               loss=['mse', 'categorical_crossentropy', 'binary_crossentropy'],
#               loss_weights=[0.25, 1., 10.])
model.compile(optimizer='rmsprop',
              loss={'age': 'mse', 'income': 'categorical_crossentropy', 'gender': 'binary_crossentropy'},
              loss_weights={'age': 0.25, 'income': 1., 'gender': 10.})

posts = []
age_targets = []
income_targets = []
gender_targets = []
model.fit(posts, {'age': age_targets, 'income': income_targets,'gender': gender_targets},epochs=10, batch_size=64)