Python lenet Exemples

Exemple #1

Fichier : gan_mnist_minibatch_discrimination.py Projet : littletomatodonkey/gan-mnist

    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")
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,

Exemple #2

Fichier : gan_mnist.py Projet : qgzang/mxnet-gan

    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/'

Exemple #3

Fichier : gan_mnist_minibatch_discrimination.py Projet : BenJamesbabala/mxnet-gan

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")
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,

Exemple #4

Fichier : gan_mnist.py Projet : tqchen/mxnet-gan

    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/'