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():