BIDI_ML_INFO_EXTRACTION/BiddingKG/dl/table_head/train.py

import sys
import os
sys.path.append(os.path.abspath("../.."))
from keras import optimizers
from tensorflow.contrib.metrics import f1_score
from tensorflow.python.ops.metrics_impl import precision, recall
from BiddingKG.dl.table_head.models.model import get_model
from BiddingKG.dl.table_head.loss import focal_loss
from keras.callbacks import ModelCheckpoint
from BiddingKG.dl.table_head.pre_process import get_data_from_file
import numpy as np


input_shape = (2, 10)
output_shape = (2,)
pretrained_path = ""
checkpoint_path = "checkpoints/"
PRETRAINED = False
CHECKPOINT = False


def train():
    # Data
    data_x, data_y = get_data_from_file()
    data_x = np.array(data_x)
    data_y = np.array(data_y)

    # Split -> Train, Test
    split_size = int(len(data_x)*0.1)
    test_x, test_y = data_x[:split_size], data_y[:split_size]
    train_x, train_y = data_x[split_size:], data_y[split_size:]

    # (table_num, 2 sentences, dim characters) -> (2, table_num, dim)
    train_x = np.transpose(train_x, (1, 0, 2))
    test_x = np.transpose(test_x, (1, 0, 2))

    # Model
    model = get_model(input_shape, output_shape)
    if PRETRAINED:
        model.load_weights(pretrained_path)
        print("read pretrained model", pretrained_path)
    else:
        print("no pretrained")
    if CHECKPOINT:
        model.load_weights(checkpoint_path)
        print("read checkpoint model", checkpoint_path)
    else:
        print("no checkpoint")

    filepath = '{epoch:02d}-{val_loss:.2f}.h5'
    checkpoint = ModelCheckpoint(checkpoint_path+filepath+".hdf5", monitor=focal_loss(),
                                 verbose=1, save_best_only=True, mode='min')
    model.compile(optimizer=optimizers.Adam(lr=0.0005), loss=focal_loss(),
                  metrics=[focal_loss()])

    print(train_x.shape, train_y.shape)
    model.fit(x=[train_x[0], train_x[1]], y=train_y,
              validation_data=([test_x[0], test_x[1]], test_y),
              epochs=100, batch_size=128, shuffle=True,
              callbacks=[checkpoint])


if __name__ == '__main__':
    train()