def build_pred_model(n_tokens=4, seq_length=33, enc1_units=50, pred_var=0.1):
x = Input(shape=(seq_length, n_tokens))
h = Flatten()(x)
h = Dense(enc1_units, activation='elu')(h)
h = Dense(enc1_units, activation='elu')(h)
out = Dense(1)(h)
model = Model(inputs=[x], outputs=[out])
model.compile(optimizer='adam',
loss=[get_gaussian_nll(pred_var)],
metrics=['mse'])
return model
def build_pred_vae_model(latent_dim,
n_tokens=4,
seq_length=33,
enc1_units=50,
eps_std=1.,
pred_var=0.1,
learn_uncertainty=False):
"""Returns a compiled VAE that makes supervised predictions from its latent
space. For use with the Gomez-Bombarelli optimization method"""
model = SimpleSupervisedVAE(input_shape=(
seq_length,
n_tokens,
),
latent_dim=latent_dim,
pred_dim=1,
pred_var=pred_var,
learn_uncertainty=learn_uncertainty)
# set encoder layers:
model.encoderLayers_ = [
Dense(units=enc1_units, activation='elu', name='e2'),
]
# set decoder layers:
model.decoderLayers_ = [
Dense(units=enc1_units, activation='elu', name='d1'),
Dense(units=n_tokens * seq_length, name='d3'),
Reshape((seq_length, n_tokens), name='d4'),
Dense(units=n_tokens, activation='softmax', name='d5'),
]
# set predictor layers:
model.predictorLayers_ = [
Dense(units=20, activation='elu', name='p1'),
]
# build models:
kl_scale = K.variable(1.)
model.build_encoder()
model.build_decoder(decode_activation='softmax')
model.build_predictor()
model.build_vae(epsilon_std=eps_std, kl_scale=kl_scale)
y_var = K.exp(model.vae_.outputs[3])
losses = [
summed_categorical_crossentropy, identity_loss,
get_gaussian_nll(y_var), zero_loss
]
model.compile(optimizer='adam', loss=losses, metrics=['mse'])
return model
