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")
encoder = encoder.lenet()
encoder = ops.minibatch_layer(encoder, batch_size, num_kernels=100)
gmod = module.GANModule(sym_gen,
symbol_encoder=encoder,
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",
ngf = 64
lr = 0.0003
beta1 = 0.5
batch_size = 100
rand_shape = (batch_size, 100)
num_epoch = 100
data_shape = (batch_size, 3, 32, 32)
context = mx.gpu()
logging.basicConfig(level=logging.DEBUG, format='%(asctime)-15s %(message)s')
sym_gen = generator.dcgan32x32(oshape=data_shape, ngf=ngf, final_act="tanh")
sym_dec = encoder.dcgan(ngf=ngf / 2)
gmod = module.GANModule(sym_gen,
sym_dec,
context=context,
data_shape=data_shape,
code_shape=rand_shape)
gmod.modG.init_params(mx.init.Normal(0.05))
gmod.modD.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/cifar10/'
train = mx.io.ImageRecordIter(path_imgrec=data_dir + "train.rec",