Python dict2obj 예제들

예제 #1

import tensorflow as tf
import numpy as np

curr_dir = os.path.dirname(os.path.realpath(__file__))

from qiznlp.common.modules.common_layers import mask_nonpad_from_embedding, add_timing_signal_1d, label_smoothing, softmax_cross_entropy
from qiznlp.common.modules.embedding import embedding
from qiznlp.common.modules.encoder import transformer_encoder, mean_pool, multi_head_attention_base_pool
import qiznlp.common.utils as utils

conf = utils.dict2obj({
    'vocab_size': 14180,
    'label_size': 16,
    "embed_size": 300,
    'hidden_size': 300,
    'num_heads': 6,
    'num_encoder_layers': 6,
    'dropout_rate': 0.2,
    'lr': 1e-3,
    'pretrain_emb': None,
    # 'pretrain_emb': np.load(f'{curr_dir}/pretrain_word_emb300.npy'),
})


class Model(object):
    def __init__(self, build_graph=True, **kwargs):
        self.conf = conf
        self.run_model = kwargs.get('run_model', None)  # acquire outside run_model instance
        if build_graph:
            # build placeholder
            self.build_placeholder()
            # build model

예제 #2

파일: run_mch.py 프로젝트: sjx0451/QizNLP

sys.path.append(curr_dir + '/..')  # 添加上级目录即默认qiznlp根目录
from model.mch_model import Model as MCH_Model

try:
    import horovod.tensorflow as hvd
    # 示例：horovodrun -np 2 -H localhost:2 python run_mch.py
    # 可根据local_rank设置shard的数据，保证各个gpu采样的数据不重叠。
except:
    HVD_ENABLE = False
else:
    HVD_ENABLE = True

conf = utils.dict2obj({
    'early_stop_patience': None,
    'just_save_best': False,
    'n_epochs': 20,
    'data_type': 'tfrecord',
    # 'data_type': 'pkldata',
})


class Run_Model_Mch(Run_Model_Base):
    def __init__(self,
                 model_name,
                 tokenize=None,
                 pbmodel_dir=None,
                 use_hvd=False):
        # 维护sess graph config saver
        self.model_name = model_name
        if tokenize is None:
            self.jieba = jieba.Tokenizer()

예제 #3

파일: multi_s2s_model.py 프로젝트: sjx0451/QizNLP

from qiznlp.common.modules.common_layers import shape_list, mask_nonpad_from_embedding, add_timing_signal_1d, get_timing_signal_1d, shift_right, split_heads
from qiznlp.common.modules.embedding import embedding, proj_logits
from qiznlp.common.modules.encoder import transformer_encoder, transformer_decoder, EncDecAttention
from qiznlp.common.modules.birnn import Bi_RNN, RNN
from qiznlp.common.modules.beam_search import beam_search, greedy_search, get_state_shape_invariants
import qiznlp.common.utils as utils

conf = utils.dict2obj({
    'max_turn': 5,
    'vocab_size': 4000,
    'embed_size': 300,
    'hidden_size': 300,
    'num_heads': 6,
    'num_encoder_layers': 6,
    'num_decoder_layers': 6,
    'dropout_rate': 0.2,
    'lr': 1e-3,
    'pretrain_emb': None,
    'beam_size': 5,
    'max_decode_len': 50,
    'eos_id': 2,
})


class Model(object):
    def __init__(self, build_graph=True, **kwargs):
        self.conf = conf
        self.run_model = kwargs.get('run_model', None)  # acquire outside run_model instance
        if build_graph:
            # build placeholder

예제 #4

파일: mch_model.py 프로젝트: sjx0451/QizNLP

import os
import tensorflow as tf
import numpy as np

curr_dir = os.path.dirname(os.path.realpath(__file__))

from qiznlp.common.modules.common_layers import mask_nonpad_from_embedding
from qiznlp.common.modules.embedding import embedding
from qiznlp.common.modules.birnn import Bi_RNN
import qiznlp.common.utils as utils

conf = utils.dict2obj({
    'vocab_size': 1142,
    'embed_size': 300,
    'birnn_hidden_size': 300,
    'l2_reg': 0.0001,
    'dropout_rate': 0.2,
    'lr': 1e-3,
    'pretrain_emb': None,
})


class Model(object):
    def __init__(self, build_graph=True, **kwargs):
        self.conf = conf
        self.run_model = kwargs.get('run_model', None)  # acquire outside run_model instance
        if build_graph:
            # build placeholder
            self.build_placeholder()
            # build model
            self.model_name = kwargs.get('model_name', 'esim')

예제 #5

파일: s2s_model.py 프로젝트: sjx0451/QizNLP

from qiznlp.common.modules.common_layers import shape_list, mask_nonpad_from_embedding, add_timing_signal_1d, get_timing_signal_1d, shift_right, split_heads
from qiznlp.common.modules.embedding import embedding, proj_logits
from qiznlp.common.modules.encoder import transformer_encoder, transformer_decoder, EncDecAttention
from qiznlp.common.modules.birnn import Bi_RNN
from qiznlp.common.modules.beam_search import beam_search, greedy_search, get_state_shape_invariants
import qiznlp.common.utils as utils

conf = utils.dict2obj({
    'vocab_size': 4000,
    'embed_size': 300,
    'hidden_size': 300,
    'num_heads': 6,
    'num_encoder_layers': 6,
    'num_decoder_layers': 6,
    'dropout_rate': 0.2,
    'lr': 1e-3,
    'pretrain_emb': None,
    'beam_size': 40,
    'max_decode_len': 50,
    'eos_id': 2,
    'gamma': 1,  # 多样性鼓励因子
    'num_group': 1,  # 分组beam_search
    'top_k': 30  # 详见beam_search
})


class Model(object):
    def __init__(self, build_graph=True, **kwargs):
        self.conf = conf
        self.run_model = kwargs.get('run_model', None)  # acquire outside run_model instance
        if build_graph:

예제 #6

import tensorflow as tf
import numpy as np

curr_dir = os.path.dirname(os.path.realpath(__file__))

from qiznlp.common.modules.common_layers import mask_nonpad_from_embedding
from qiznlp.common.modules.embedding import embedding
from qiznlp.common.modules.birnn import Bi_RNN
from qiznlp.common.modules.idcnn import IDCNN
import qiznlp.common.utils as utils

conf = utils.dict2obj({
    'vocab_size': 1847,
    'embed_size': 300,
    'label_size': 30,
    'birnn_hidden_size': 300,
    'dropout_rate': 0.2,
    'lr': 1e-3,
    'pretrain_emb': None,
    # 'pretrain_emb': np.load(f'{curr_dir}/pretrain_word_emb300.npy'),
})


class Model(object):
    def __init__(self, build_graph=True, **kwargs):
        self.conf = conf
        self.run_model = kwargs.get('run_model',
                                    None)  # acquire outside run_model instance
        if build_graph:
            # build placeholder
            self.build_placeholder()
            # build model