def make_classifier_combined_morphingaware(n_hidden_layers=2,
hidden_layer_size=100,
activation='tanh',
dropout_prob=0.0,
alpha=5.,
learn_log_r=False,
epsilon=1.e-4,
learning_rate=1.e-3):
"""
Builds a Keras model for the morphing-aware version of the CASCAL technique.
:param n_hidden_layers: Number of hidden layers.
:param hidden_layer_size: Number of units in each hidden layer.
:param activation: Activation function.
:param dropout_prob: Dropout probability.
:param alpha: RASCAL hyperparameter that weights the score term in the loss.
:param learn_log_r: Fully connected network represents log r rather than s.
:param epsilon: Parameter for numerical stability.
:param learning_rate: Initial learning rate.
:return: Keras model.
"""
# Inputs
input_layer = Input(shape=(settings.n_thetas_features, ))
x_layer = Lambda(lambda x: x[:, :settings.n_features],
output_shape=(settings.n_features, ))(input_layer)
theta_layer = Lambda(lambda x: x[:, -settings.n_params:],
output_shape=(settings.n_params, ))(input_layer)
# Morphing weights
wtilde_layer = generate_wtilde_layer(theta_layer)
wi_layer = generate_wi_layer(wtilde_layer)
# Ratio estimators for each component
ri_hat_layers = []
for i in range(settings.n_morphing_samples):
hidden_layer = Dense(hidden_layer_size, activation=activation)(x_layer)
if n_hidden_layers > 1:
hidden_layer_ = build_hidden_layers(
n_hidden_layers - 1,
hidden_layer_size=hidden_layer_size,
activation=activation,
dropout_prob=dropout_prob)
hidden_layer = hidden_layer_(hidden_layer)
if learn_log_r:
log_ri_hat_layer = Dense(1, activation='linear')(hidden_layer)
ri_hat_layers.append(Lambda(lambda x: K.exp(x))(log_ri_hat_layer))
else:
si_hat_layer = Dense(1, activation='sigmoid')(hidden_layer)
ri_hat_layers.append(
Reshape((1, ))(
Lambda(lambda x: (1. - x) / (x + epsilon))(si_hat_layer)))
ri_hat_layer = Concatenate()(ri_hat_layers)
# Combine, clip, transform to \hat{s}
wi_ri_hat_layer = Multiply()([wi_layer, ri_hat_layer])
r_hat_layer = Reshape(
(1, ))(Lambda(lambda x: K.sum(x, axis=1))(wi_ri_hat_layer))
positive_r_hat_layer = Activation('relu')(r_hat_layer)
s_hat_layer = Lambda(lambda x: 1. / (1. + x))(positive_r_hat_layer)
# Score
log_r_hat_layer = Lambda(lambda x: K.log(x + epsilon))(
positive_r_hat_layer)
gradient_layer = Lambda(lambda x: K.gradients(x[0], x[1])[0],
output_shape=(settings.n_thetas_features, ))(
[log_r_hat_layer, input_layer])
score_layer = Lambda(lambda x: x[:, -settings.n_params:],
output_shape=(settings.n_params, ))(gradient_layer)
# Combine outputs
output_layer = Concatenate()(
[s_hat_layer, log_r_hat_layer, score_layer, wi_layer, ri_hat_layer])
model = Model(inputs=[input_layer], outputs=[output_layer])
# Compile model
model.compile(loss=lambda x, y: loss_function_combined(x, y, alpha=alpha),
metrics=metrics,
optimizer=optimizers.Adam(lr=learning_rate, clipnorm=1.))
return model
def make_regressor_morphingaware(n_hidden_layers=2,
hidden_layer_size=100,
activation='tanh',
dropout_prob=0.0,
factor_out_sm=True,
epsilon=1.e-4,
learning_rate=1.e-3):
"""
Builds a Keras model for the morphing-aware version of the ROLR technique.
:param n_hidden_layers: Number of hidden layers.
:param hidden_layer_size: Number of units in each hidden layer.
:param activation: Activation function.
:param dropout_prob: Dropout probability.
:param factor_out_sm: If True, the SM and the deviation from it are modelled separately and then added.
:param epsilon: Parameter for numerical stability.
:param learning_rate: Initial learning rate.
:return: Keras model.
"""
# Inputs
input_layer = Input(shape=(settings.n_thetas_features, ))
x_layer = Lambda(lambda x: x[:, :settings.n_features],
output_shape=(settings.n_features, ))(input_layer)
theta_layer = Lambda(lambda x: x[:, -settings.n_params:],
output_shape=(settings.n_params, ))(input_layer)
# Morphing weights
wtilde_layer = generate_wtilde_layer(theta_layer)
wi_layer = generate_wi_layer(wtilde_layer)
# Log ratio estimator for SM
if factor_out_sm:
hidden_layer = Dense(hidden_layer_size,
activation=activation)(input_layer)
if n_hidden_layers > 1:
hidden_layer_ = build_hidden_layers(
n_hidden_layers - 1,
hidden_layer_size=hidden_layer_size,
activation=activation,
dropout_prob=dropout_prob)
hidden_layer = hidden_layer_(hidden_layer)
log_r0_hat_layer = Dense(1, activation='linear')(hidden_layer)
r0_hat_layer = Lambda(lambda x: K.exp(x))(log_r0_hat_layer)
# Log ratio estimators for each component
ri_hat_layers = []
for i in range(settings.n_morphing_samples):
hidden_layer = Dense(hidden_layer_size, activation=activation)(x_layer)
if n_hidden_layers > 1:
hidden_layer_ = build_hidden_layers(
n_hidden_layers - 1,
hidden_layer_size=hidden_layer_size,
activation=activation,
dropout_prob=dropout_prob)
hidden_layer = hidden_layer_(hidden_layer)
if factor_out_sm:
delta_ri_hat_layer = Dense(1, activation='linear')(hidden_layer)
ri_hat_layer = Add()([delta_ri_hat_layer, r0_hat_layer])
else:
log_ri_hat_layer = Dense(1, activation='linear')(hidden_layer)
ri_hat_layer = Lambda(lambda x: K.exp(x))(log_ri_hat_layer)
ri_hat_layers.append(Reshape((1, ))(ri_hat_layer))
ri_hat_layer = Concatenate()(ri_hat_layers)
# Combine, clip, transform to \hat{s}
wi_ri_hat_layer = Multiply()([wi_layer, ri_hat_layer])
r_hat_layer = Reshape(
(1, ))(Lambda(lambda x: K.sum(x, axis=1))(wi_ri_hat_layer))
positive_r_hat_layer = Activation('relu')(r_hat_layer)
# Translate to s
s_hat_layer = Lambda(lambda x: 1. / (1. + x))(positive_r_hat_layer)
# Score
log_r_hat_layer = Lambda(lambda x: K.log(x + epsilon))(
positive_r_hat_layer)
gradient_layer = Lambda(lambda x: K.gradients(x[0], x[1])[0],
output_shape=(settings.n_thetas_features, ))(
[log_r_hat_layer, input_layer])
score_layer = Lambda(lambda x: x[:, -settings.n_params:],
output_shape=(settings.n_params, ))(gradient_layer)
# Combine outputs
output_layer = Concatenate()(
[s_hat_layer, log_r_hat_layer, score_layer, wi_layer, ri_hat_layer])
model = Model(inputs=[input_layer], outputs=[output_layer])
# Compile model
model.compile(loss=loss_function_ratio_regression,
metrics=metrics,
optimizer=optimizers.Adam(lr=learning_rate, clipnorm=10.))
return model