def child_process(curr_step):
print('Starting child process')
model = RNNModel.Builder().set_max_steps(max_steps). \
set_word_feature_size(word_feature_size + pos_feature_size). \
set_read_path(os.path.join('records','eval')). \
set_epochs(1). \
set_char_emb_status(use_char_embeddings). \
set_cell_type(RNNModel.CellType.RNN_CELL_TYPE_GRU). \
set_cell_size(cell_size). \
set_batch_size(batch_size). \
set_class_size(num_classes). \
set_entity_class_size(num_entity_classes) .\
set_layer_size(num_layers). \
set_model_path(model_path). \
set_model_name(model_name).\
set_logs_path(logs_path). \
set_bi_directional(bi_directional). \
set_classifer_status(is_classifer). \
set_state_feedback(state_feeback). \
set_time_major(time_major).\
set_char_feature_size(char_feature_size).\
set_char_cell_size(char_cell_size).\
set_char_vocab_size(char_vocab_size).\
set_oper_mode(RNNModel.OperMode.OPER_MODE_EVAL). \
build()
model.evaluate(curr_step=curr_step)
thread.start()
thread.join()
# eval_model.evaluate()
train_model = RNNModel.Builder().set_max_steps(max_steps).\
set_feature_size(feature_size).\
set_read_path(os.path.join('records', 'train')). \
set_epochs(train_epochs).\
set_cell_type(cell_type).\
set_cell_size(cell_size).\
set_batch_size(batch_size).\
set_class_size(num_classes).\
set_layer_size(num_layers).\
set_learning_rate(learning_rate). \
set_model_path(model_path). \
set_model_name(model_name). \
set_logs_path(logs_path).\
set_eval_fn(evaluator). \
set_time_major(time_major). \
set_state_feedback(state_feeback). \
set_bi_directional(bi_directional) .\
set_classifer_status(is_classifer).\
set_oper_mode(RNNModel.OperMode.OPER_MODE_TRAIN). \
set_validation_step(validation_step).\
build()
train_model.train(keep_prob)
# set_eval_fn(evaluator). \
import numpy as np
from utils import preprocess_data
from paths import *
if __name__ == '__main__':
model = RNNModel.Builder().set_max_steps(max_steps). \
set_word_feature_size(word_feature_size + pos_feature_size). \
set_cell_type(RNNModel.CellType.RNN_CELL_TYPE_GRU). \
set_cell_size(cell_size). \
set_batch_size(1). \
set_class_size(num_classes). \
set_entity_class_size(num_entity_classes). \
set_layer_size(num_layers). \
set_model_path(model_path). \
set_model_name(model_name). \
set_time_major(time_major).\
set_bi_directional(bi_directional). \
set_state_feedback(state_feeback). \
set_classifer_status(is_classifer). \
set_char_feature_size(char_feature_size). \
set_char_cell_size(char_cell_size). \
set_char_vocab_size(char_vocab_size). \
set_oper_mode(RNNModel.OperMode.OPER_MODE_TEST). \
build()
model.init_graph()
nlp = spacy.load(spacy_model_path)
nlp_pos = spacy.load('en_core_web_sm')
# eval_model.evaluate()
train_model = RNNModel.Builder().set_max_steps(max_steps). \
set_char_emb_status(use_char_embeddings) .\
set_word_feature_size(word_feature_size + pos_feature_size).\
set_read_path(os.path.join('records', 'train')). \
set_epochs(train_epochs).\
set_cell_type(RNNModel.CellType.RNN_CELL_TYPE_GRU).\
set_cell_size(cell_size).\
set_batch_size(batch_size).\
set_class_size(num_classes). \
set_entity_class_size(num_entity_classes). \
set_layer_size(num_layers).\
set_learning_rate(learning_rate). \
set_model_path(model_path). \
set_model_name(model_name). \
set_logs_path(logs_path).\
set_eval_fn(evaluator). \
set_time_major(time_major). \
set_state_feedback(state_feeback). \
set_bi_directional(bi_directional) .\
set_classifer_status(is_classifer).\
set_char_feature_size(char_feature_size).\
set_char_cell_size(char_cell_size). \
set_char_vocab_size(char_vocab_size). \
set_oper_mode(RNNModel.OperMode.OPER_MODE_TRAIN). \
set_validation_step(validation_step).\
build()
train_model.train(keep_prob)
config.set_string('-hmm', os.path.join(modeldir, r'en-us\en-us'))
config.set_string('-dict', os.path.join(modeldir, r'en-us\cmudict-en-us.dict'))
config.set_string('-keyphrase', 'alexa')
config.set_float('-kws_threshold', 1e-20)
config.set_string('-logfn', 'null')
decoder = Decoder(config)
decoder.start_utt()
model = RNNModel.Builder().set_max_steps(max_steps). \
set_feature_size(feature_size). \
set_cell_type(RNNModel.CellType.RNN_CELL_TYPE_GRU). \
set_cell_size(cell_size). \
set_batch_size(batch_size). \
set_class_size(num_classes). \
set_layer_size(num_layers). \
set_model_path(model_path). \
set_model_name(model_name). \
set_time_major(time_major).\
set_classifer_status(is_classifer). \
set_oper_mode(RNNModel.OperMode.OPER_MODE_TEST). \
build()
model.init_graph()
data = b''
batch_count = 15
count = 0
def evaluator():