MNIST : MXNet

MNIST Data (refer to MNIST : Caffe)

Prepare imglist for MXNet

import os
import sys

input_path = sys.argv[1].rstrip(os.sep)

out_path = sys.argv[2]

filenames = os.listdir(input_path)
with open(out_path, ‘w’) as f:
for i, filename in enumerate(filenames):
filepath = os.sep.join([input_path, filename])
label = filename[: filename.rfind(‘.’)].split(‘_‘)[1]

    line = '{}\\t{}\\t{}\\n'.format(i, label, filepath)
    f.write(line)

Convert to rec

python gen_mxnet_imglist.py mnist/train train.lst
python gen_mxnet_imglist.py mnist/val val.lst
python gen_mxnet_imglist.py mnist/test test.lst

/home/d/mxnet/bin/im2rec train.lst ./ train.rec color=0
/home/d/mxnet/bin/im2rec val.lst ./ val.rec color=0
/home/d/mxnet/bin/im2rec test.lst ./ test.rec color=0

Train (LeNet-5)

import mxnet as mx

data = mx.symbol.Variable(‘data’)

conv1 = mx.symbol.Convolution(data=data, kernel=(5, 5), num_filter=20)
pool1 = mx.symbol.Pooling(data=conv1, pool_type=”max”, kernel=(2, 2), stride=(2, 2))

conv2 = mx.symbol.Convolution(data=pool1, kernel=(5, 5), num_filter=50)
pool2 = mx.symbol.Pooling(data=conv2, pool_type=”max”, kernel=(2, 2), stride=(2, 2))

flatten = mx.symbol.Flatten(data=pool2)
fc1 = mx.symbol.FullyConnected(data=flatten, num_hidden=500)
relu1 = mx.symbol.Activation(data=fc1, act_type=”relu”)

fc2 = mx.symbol.FullyConnected(data=relu1, num_hidden=10)

lenet5 = mx.symbol.SoftmaxOutput(data=fc2, name=’softmax’)

mod = mx.mod.Module(lenet5, context=mx.gpu(0))

train_dataiter = mx.io.ImageRecordIter(
path_imgrec=”train.rec”,
data_shape=(1, 28, 28),
batch_size=50,
mean_r=128,
scale=0.00390625,
rand_crop=True,
min_crop_size=24,
max_crop_size=28,
max_rotate_angle=15,
fill_value=0
)
val_dataiter = mx.io.ImageRecordIter(
path_imgrec=”val.rec”,
data_shape=(1, 28, 28),
batch_size=100,
mean_r=128,
scale=0.00390625,
)

import logging

logging.getLogger().setLevel(logging.DEBUG)
fh = logging.FileHandler(‘train_mnist_lenet.log’)
logging.getLogger().addHandler(fh)

lr_scheduler = mx.lr_scheduler.FactorScheduler(1000, factor=0.95)
optimizer_params = {
‘learning_rate’: 0.01,
‘momentum’: 0.9,
‘wd’: 0.0005,
‘lr_scheduler’: lr_scheduler
}

checkpoint = mx.callback.do_checkpoint(‘mnist_lenet’, period=5)

mod.fit(train_dataiter,
eval_data=val_dataiter,
optimizer_params=optimizer_params,
num_epoch=36,
epoch_end_callback=checkpoint)

Log Visualization

Test Model Accuracy

import mxnet as mx

test_dataiter = mx.io.ImageRecordIter(
path_imgrec=”test.rec”,
data_shape=(1, 28, 28),
batch_size=100,
mean_r=128,
scale=0.00390625,
)

mod = mx.mod.Module.load(‘mnist_lenet’, 35, context=mx.gpu(0))

mod.bind(
data_shapes=test_dataiter.provide_data,
label_shapes=test_dataiter.provide_label,
for_training=False)

metric = mx.metric.create(‘acc’)

mod.score(test_dataiter, metric)

for name, val in metric.get_name_value():
print(‘{} = {:.2f}%’.format(name, val * 100))

Test Model Time

import time
import mxnet as mx

benchmark_dataiter = mx.io.ImageRecordIter(
path_imgrec=”test.rec”,
data_shape=(1, 28, 28),
batch_size=64,
mean_r=128,
scale=0.00390625,
)

mod = mx.mod.Module.load(‘mnist_lenet’, 35, context=mx.gpu(0))
mod.bind(
data_shapes=benchmark_dataiter.provide_data,
label_shapes=benchmark_dataiter.provide_label,
for_training=False)

start = time.time()

for i, batch in enumerate(benchmark_dataiter):
mod.forward(batch)

time_elapsed = time.time() - start
msg = ‘{} batches iterated!\nAverage forward time per batch: {:.6f} ms’
print(msg.format(i + 1, 1000 * time_elapsed / float(i)))