train.py

"""
Training the model
"""
import sys
import json
from datetime import datetime
import tensorflow as tf

sys.path.append("../")

from model.create_model import get_model
from data.dataset_generator import generate_data_for_siamese


CONFIG_FILE = 'configurations.json'

with open(CONFIG_FILE, encoding="utf-8") as load_value:
    configurations = json.load(load_value)

print(configurations)
with open(configurations["MODEL_DIR"] +
"/" + "model_specifications.json", encoding="utf-8") as specs:
    model_config = json.load(specs)

def main(_args):
    """
    Main Function
    """

    # Loading the Data
    data = generate_data_for_siamese(
        configurations["data_dir"],
        configurations["IMAGE_DIR"]
        )


    # Loading the model

    weights_dir = "model/siamese/weights"
    model = get_model(model_config)


    checkpoint = tf.keras.callbacks.ModelCheckpoint(
        weights_dir + "/" + model_config["base_architecture"] +
        "/siam-{epoch}-"+str(model_config["lr"])+"-"+str("net")+"_{loss:.4f}.h5",
        monitor="loss",
        verbose=1,
        save_best_only=True,
        save_weights_only=True,
        mode="min",
    )
    # stop = tf.keras.callbacks.EarlyStopping(
    #     monitor="loss",
    #     patience=configurations["TRAIN_PATIENC"],
    #     mode="min")
    # reduce_lr = tf.keras.callbacks.ReduceLROnPlateau(
    #     monitor="loss",
    #     factor=0.6,
    #     patience=5,
    #     min_lr=1e-6,
    #     verbose=1,
    #     mode="min")

    # Defining the Keras TensorBoard callback.
    logdir = "logs/fit/" + datetime.now().strftime("%Y%m%d-%H%M%S")
    tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=logdir)


    model.fit(
        data,
        epochs=model_config["epochs"],
        callbacks=[tensorboard_callback, checkpoint],
        verbose=1
    )


if __name__ == "__main__":
    try:
        tf.compat.v1.app.run(main)

    except SystemExit:
        pass


# EOL