kiwi.systems.decoders.estimator

Module Contents

Classes

EstimatorDecoder

Base class for all neural network modules.

kiwi.systems.decoders.estimator.logger

class kiwi.systems.decoders.estimator.EstimatorDecoder(inputs_dims, config)

Bases: kiwi.systems._meta_module.MetaModule

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing to nest them in a tree structure. You can assign the submodules as regular attributes:

import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will have their parameters converted too when you call to(), etc.

class Config

Bases: kiwi.utils.io.BaseConfig

Base class for all pydantic configs. Used to configure base behaviour of configs.

hidden_size :int = 100

Size of hidden layers in LSTM

rnn_layers :PositiveInt = 1

Layers in PredictorEstimator RNN

use_mlp :bool = True

Pass the PredictorEstimator input through a linear layer reducing dimensionality before RNN.

dropout :confloat(ge=0.0, le=1.0) = 0.0

use_v0_buggy_strategy :bool = False

The Predictor implementation in Kiwi<=0.3.4 had a bug in applying the LSTM to encode source (it used lengths too short by 2) and in reversing the target embeddings for applying the backward LSTM (also short by 2). This flag is set to true when loading a saved model from those versions.

dropout_on_rnns(cls, v, values)

size(self, field=None)

forward(self, features, batch_inputs)

kiwi.systems.decoders kiwi.systems.decoders.linear