config_trans_ptb.py

# Copyright 2018 The Texar Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
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"""Config file of VAE with Trasnformer decoder, on PTB data.
"""

# pylint: disable=invalid-name, too-few-public-methods, missing-docstring

dataset = 'ptb'
num_epochs = 100
hidden_size = 256
dec_dropout_in = 0.
enc_dropout_in = 0.
enc_dropout_out = 0.
batch_size = 32
embed_dim = 256

latent_dims = 32

lr_decay_hparams = {
    'init_lr': 0.001,
    'threshold': 2,
    'decay_factor': 0.5,
    'max_decay': 5
}


relu_dropout = 0.2
embedding_dropout = 0.2
attention_dropout = 0.2
residual_dropout = 0.2
num_blocks = 3

decoder_type = 'transformer'

enc_cell_hparams = {
    'type': 'LSTMBlockCell',
    'kwargs': {
        'num_units': hidden_size,
        'forget_bias': 0.
    },
    'dropout': {'output_keep_prob': 1. - enc_dropout_out},
    'num_layers': 1
}

enc_emb_hparams = {
    'name': 'lookup_table',
    'dim': embed_dim,
    'dropout_rate': enc_dropout_in,
    'initializer': {
        'type': 'random_normal_initializer',
        'kwargs': {
            'mean': 0.0,
            'stddev': embed_dim**-0.5,
        },
    }
}

dec_emb_hparams = {
    'name': 'lookup_table',
    'dim': embed_dim,
    'dropout_rate': dec_dropout_in,
    'initializer': {
        'type': 'random_normal_initializer',
        'kwargs': {
            'mean': 0.0,
            'stddev': embed_dim**-0.5,
        },
    }
}

max_pos = 200  # max sequence length in training data
dec_pos_emb_hparams = {
    'dim': hidden_size,
}

# due to the residual connection, the embed_dim should be equal to hidden_size
trans_hparams = {
    'output_layer_bias': False,
    'embedding_dropout': embedding_dropout,
    'residual_dropout': residual_dropout,
    'num_blocks': num_blocks,
    'dim': hidden_size,
    'initializer': {
        'type': 'variance_scaling_initializer',
        'kwargs': {
            'scale': 1.0,
            'mode': 'fan_avg',
            'distribution': 'uniform',
        },
    },
    'multihead_attention': {
        'dropout_rate': attention_dropout,
        'num_heads': 8,
        'num_units': hidden_size,
        'output_dim': hidden_size
    },
    'poswise_feedforward': {
        'name': 'fnn',
        'layers': [
            {
                'type': 'Dense',
                'kwargs': {
                    'name': 'conv1',
                    'units': hidden_size * 4,
                    'activation': 'relu',
                    'use_bias': True,
                },
            },
            {
                'type': 'Dropout',
                'kwargs': {
                    'rate': relu_dropout,
                }
            },
            {
                'type': 'Dense',
                'kwargs': {
                    'name': 'conv2',
                    'units': hidden_size,
                    'use_bias': True,
                    }
            }
        ],
    }
}

# KL annealing
kl_anneal_hparams = {
    'warm_up': 10,
    'start': 0.1
}

train_data_hparams = {
    'num_epochs': 1,
    'batch_size': batch_size,
    'seed': 123,
    'dataset': {
        'files': './simple-examples/data/ptb.train.txt',
        'vocab_file': './simple-examples/data/vocab.txt'
    }
}

val_data_hparams = {
    'num_epochs': 1,
    'batch_size': batch_size,
    'seed': 123,
    'dataset': {
        'files': './simple-examples/data/ptb.valid.txt',
        'vocab_file': './simple-examples/data/vocab.txt'
    }
}

test_data_hparams = {
    'num_epochs': 1,
    'batch_size': batch_size,
    'dataset': {
        'files': './simple-examples/data/ptb.test.txt',
        'vocab_file': './simple-examples/data/vocab.txt'
    }
}

opt_hparams = {
    'optimizer': {
        'type': 'AdamOptimizer',
        'kwargs': {
            'learning_rate': 0.001
        }
    },
    'gradient_clip': {
        'type': 'clip_by_global_norm',
        'kwargs': {'clip_norm': 5.}
    }
}