mxnet-gan's Introduction

MXNet GAN

MXNet module implementation of multi GPU compatible generative models.

List of Methods

Unsupervised Training
Semisupervised Training
Minibatch discrimation

Usage

import logging
import numpy as np
import mxnet as mx

from mxgan import module, generator, encoder, viz

def ferr(label, pred):
    pred = pred.ravel()
    label = label.ravel()
    return np.abs(label - (pred > 0.5)).sum() / label.shape[0]

lr = 0.0005
beta1 = 0.5
batch_size = 100
rand_shape = (batch_size, 100)
num_epoch = 100
data_shape = (batch_size, 1, 28, 28)
context = mx.gpu()

logging.basicConfig(level=logging.DEBUG, format='%(asctime)-15s %(message)s')
sym_gen = generator.dcgan28x28(oshape=data_shape, ngf=32, final_act="sigmoid")

gmod = module.GANModule(
    sym_gen,
    symbol_encoder=encoder.lenet(),
    context=context,
    data_shape=data_shape,
    code_shape=rand_shape)

gmod.init_params(mx.init.Xavier(factor_type="in", magnitude=2.34))

gmod.init_optimizer(
    optimizer="adam",
    optimizer_params={
        "learning_rate": lr,
        "wd": 0.,
        "beta1": beta1,
})

data_dir = './../../mxnet/example/image-classification/mnist/'
train = mx.io.MNISTIter(
    image = data_dir + "train-images-idx3-ubyte",
    label = data_dir + "train-labels-idx1-ubyte",
    input_shape = data_shape[1:],
    batch_size = batch_size,
    shuffle = True)

metric_acc = mx.metric.CustomMetric(ferr)

for epoch in range(num_epoch):
    train.reset()
    metric_acc.reset()
    for t, batch in enumerate(train):
        gmod.update(batch)
        gmod.temp_label[:] = 0.0
        metric_acc.update([gmod.temp_label], gmod.outputs_fake)
        gmod.temp_label[:] = 1.0
        metric_acc.update([gmod.temp_label], gmod.outputs_real)

        if t % 100 == 0:
            logging.info("epoch: %d, iter %d, metric=%s", epoch, t, metric_acc.get())
            viz.imshow("gout", gmod.temp_outG[0].asnumpy(), 2)
            diff = gmod.temp_diffD[0].asnumpy()
            diff = (diff - diff.mean()) / diff.std() + 0.5
            viz.imshow("diff", diff)
            viz.imshow("data", batch.data[0].asnumpy(), 2)

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