Train a New Deep Neural Network and Make Predictions

This uses environment variables to build, train and make predictions using the AntiNex client.

This python script is available in the pip: ai_train_dnn.py

It takes parameters:

parser = argparse.ArgumentParser(
    description=("Python client to Train a Deep Neural Network "
                 "with AntiNex Django Rest Framework"))
parser.add_argument(
        "-u",
        help="username",
        required=False,
        dest="user")
parser.add_argument(
        "-p",
        help="user password",
        required=False,
        dest="password")
parser.add_argument(
        "-e",
        help="user email",
        required=False,
        dest="email")
parser.add_argument(
        "-a",
        help="url endpoint with default http://localhost:8010",
        required=False,
        dest="url")
parser.add_argument(
        "-f",
        help="file to use default ./examples/test-keras-dnn.json",
        required=False,
        dest="datafile")
parser.add_argument(
        "-s",
        help="silent",
        required=False,
        dest="silent",
        action="store_true")
parser.add_argument(
        "-d",
        help="debug",
        required=False,
        dest="debug",
        action="store_true")

Source Code

antinex_client.scripts.ai_train_dnn.train_new_deep_neural_network()

Train a new deep neural network and store the results as a new: MLJob and MLJobResult database records.