mnist.py

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"""
Train mnist, see more explanation at https://mxnet.io/tutorials/python/mnist.html
"""
import os
import argparse
import logging
import mxnet as mx
import numpy as np
import gzip
import struct
from common import fit
from common import utils
# This example only for mlp network
from symbols import mlp

# Use this format (%Y-%m-%dT%H:%M:%SZ) to record timestamp of the metrics
logging.basicConfig(
    format="%(asctime)s %(levelname)-8s %(message)s",
    datefmt="%Y-%m-%dT%H:%M:%SZ",
    level=logging.DEBUG)


def get_mnist_iter(args, kv):
    """
    Create data iterator with NDArrayIter
    """
    mnist = mx.test_utils.get_mnist()

    # Get MNIST data.
    train_data = mx.io.NDArrayIter(
        mnist['train_data'], mnist['train_label'], args.batch_size, shuffle=True)
    val_data = mx.io.NDArrayIter(
        mnist['test_data'], mnist['test_label'], args.batch_size)

    return (train_data, val_data)


if __name__ == '__main__':
    # parse args
    parser = argparse.ArgumentParser(description="train mnist",
                                     formatter_class=argparse.ArgumentDefaultsHelpFormatter)
    parser.add_argument('--num-classes', type=int, default=10,
                        help='the number of classes')
    parser.add_argument('--num-examples', type=int, default=60000,
                        help='the number of training examples')

    parser.add_argument('--add_stn',  action="store_true", default=False,
                        help='Add Spatial Transformer Network Layer (lenet only)')
    parser.add_argument('--image_shape', default='1, 28, 28', help='shape of training images')

    fit.add_fit_args(parser)
    parser.set_defaults(
        # network
        network='mlp',
        # train
        gpus=None,
        batch_size=64,
        disp_batches=100,
        num_epochs=10,
        lr=.05,
        lr_step_epochs='10'
    )
    args = parser.parse_args()

    # load mlp network
    sym = mlp.get_symbol(**vars(args))

    # train
    fit.fit(args, sym, get_mnist_iter)