def build_UMAL(input_size,
architecture,
learning_rate=0.001,
training=True,
mu_act='linear',
b_act=lambda x: elu_modif(x, shift=0., epsilon=1e-15)):
"""
UMAL building model process.
If training = True, the returned model is the keras trainable model given any DL architecture.
If training = False, the returned graph is an inference TensorFlow model.
mu_act is the activation function for the condioned position mu parameter of the ALD.
b_act is the activation function for the conditioned scale b parameter of the ALD.
Requirements: Given any set of hidden layers, noted as NN, the parameter 'architecture' has
to be a function with two inputs and returning the last hidden output as follows
def template_architecture(input, name):
penultimate_output = NN(input,name=name)
return penultimate_output
return: the built custom Keras model
"""
general_type = K.floatx()
i = Input(name='input', shape=(input_size, ), dtype=general_type)
if training:
n_taus = K.variable(1, dtype=general_type.replace('float', 'int'))
inputs = Lambda(lambda x: K.reshape(K.repeat(x, n_taus),
shape=[-1, i.shape[-1]]))(i)
tau = Lambda(lambda x: K.random_uniform(shape=[K.shape(inputs)[0], 1],
minval=1e-2,
maxval=1. - 1e-2,
dtype=general_type))(inputs)
else:
sel_taus = tf.placeholder(general_type, shape=[None])
tau = Lambda(lambda i: K.reshape(
K.permute_dimensions(K.repeat(K.reshape(sel_taus, [-1, 1]),
K.shape(i)[0]),
pattern=(1, 0, 2)), (-1, 1)))(i)
inputs = Lambda(lambda i: K.reshape(K.repeat(i,
K.shape(sel_taus)[0]),
shape=[-1, i.shape[-1]]))(i)
it = concatenate([inputs, tau], axis=1, name='input_concat_1')
model = architecture(it, name='_hiden_layers')
mu = Dense(units=1, activation=mu_act, name='l_mu')(model)
b = Dense(units=1, activation=b_act, name='l_b')(model)
model_output = concatenate([mu, b, tau], axis=1, name='main_output')
model = Model(inputs=[i], outputs=[model_output])
if training:
model.n_taus = n_taus
else:
model.taus = sel_taus
n_taus = K.shape(sel_taus)[0]
opt = Adam(lr=learning_rate)
model.compile(
optimizer=opt,
loss=(lambda y, p: umal_log_pdf(y, p, general_type, n_taus=n_taus)))
model._hp = {
'input_size': input_size,
'architecture': architecture,
'learning_rate': learning_rate,
'training': training,
'mu_act': mu_act,
'b_act': b_act
}
return model
