def list_params(cls, expected_params=None):
print("San Encoder:")
SanEncoderEstimator.list_params(expected_params)
print()
print("Seq2Seq Decoder:")
Seq2SeqDecoderEstimator.list_params(expected_params)
print()
def __init__(self, model_dir="/tmp", params=dict(), config=None, scope="", is_mmi_model=False):
self.encoder = SanEncoderEstimator(
model_dir, params, config=config, scope=scope+"/encoder")
self.decoder = Seq2SeqDecoderEstimator(
model_dir, params, config=config, scope=scope+"/decoder", is_mmi_model=is_mmi_model)
super().__init__([self.encoder, self.decoder],
model_dir, params, config, scope)
def __init__(self, model_dir, params, config=None, scope="default"):
self.core_encoder = Seq2SeqEncoderEstimator(model_dir, params, scope="core_encoder")
self.ae_encoder = Seq2SeqEncoderEstimator(model_dir, params, scope="ae_encoder")
self.noise = NoiseLayer(model_dir, params, scope="noise")
self.decoder = Seq2SeqDecoderEstimator(model_dir, params, scope="decoder")
self.core_model = EstimatorChain([self.core_encoder, self.decoder], model_dir, params, scope="core")
self.noisy_core_model = EstimatorChain([self.core_encoder, self.noise, self.decoder], model_dir, params, scope="noisy_core")
self.autoencoder = EstimatorChain([self.ae_encoder, self.noise, self.decoder], model_dir, params, scope="ae")
self.loss_balance = [1.0, 1.0, 1.0, 1.0]
super().__init__([self.core_model, self.noisy_core_model, self.autoencoder], model_dir, params, config=config, scope=scope)