vocab = Vocab(params["vocab_path"], params["vocab_size"]) # 构建模型 print("Building the model ...") # model = Seq2Seq(params) model = PGN(params) # print("Creating the batcher ...") # dataset = batcher(params["train_seg_x_dir"], params["train_seg_y_dir"], vocab, params) # print('dataset is ', dataset) # 获取保存管理者 print("Creating the checkpoint manager") checkpoint = tf.train.Checkpoint(Seq2Seq=model) checkpoint_manager = tf.train.CheckpointManager(checkpoint, CKPT_DIR, max_to_keep=5) checkpoint.restore(checkpoint_manager.latest_checkpoint) if checkpoint_manager.latest_checkpoint: print("Restored from {}".format(checkpoint_manager.latest_checkpoint)) else: print("Initializing from scratch.") # 训练模型 print("Starting the training ...") train_model(model, vocab, params, checkpoint_manager) if __name__ == '__main__': # 获得参数 params = get_params() # 训练模型 train(params)
from utils.params import get_params import model.FastText.FastText_params as FastText_params import model.TextCNN.TextCNN_params as TextCNN_params app = Flask(__name__) app.config['JSON_AS_ASCII'] = False err_result = {"errCode": "", "errMsg": "", "status": False} FastText_model = tf.keras.models.load_model( './results/FastText/2020-03-27-15-01') # FastText_model = tf.keras.models.load_model('./results/FastText/model.h5') TextCNN_model = tf.keras.models.load_model( './results/TextCNN/2020-03-27-21-41') _, _, _, _, vocab, mlb = data_loader(get_params()) labels = np.array(mlb.classes_) fastText_params = FastText_params.get_params() textCNN_params = TextCNN_params.get_params() @app.route("/FastText_service/", methods=['GET', 'POST']) def FastText_service(): try: text_list = request.json predict_data = convert(text_list, fastText_params) except Exception as e: return jsonify(err_result) else: preds = FastText_model.predict(predict_data)
# Build vector of repetitions for a in assignments: descriptor[a] += 1 # L2 normalize descriptor = normalize(descriptor) return descriptor if __name__ == "__main__": params = get_params() # Change to training set params['split'] = 'train' print "Stacking features together..." # Save features for training set t = time.time() X, pca, scaler = stack_features(params) print "Done. Time elapsed:", time.time() - t print "Number of training features", np.shape(X) print "Training codebook..." t = time.time() train_codebook(params,X) print "Done. Time elapsed:", time.time() - t
def main(): args = get_params() args = create_exp_dirs(args) agent = Agent(args) agent.run()