from mxnet import init, gluon
from mxnet.gluon import loss as gloss, nn
import utils
batch_size = 256
num_inputs = 784
num_outputs = 10
num_hiddens = 256
num_epochs = 100
dropout_prob1 = 0.2
dropout_prob2 = 0.5
learning_rate = 0.1
train_iter, test_iter = utils.load_fashion_mnist(batch_size)
net = nn.Sequential()
net.add(nn.Dense(num_hiddens, activation='relu'))
net.add(nn.Dropout(dropout_prob1))
net.add(nn.Dense(num_hiddens, activation='relu'))
net.add(nn.Dropout(dropout_prob2))
net.add(nn.Dense(num_outputs))
net.initialize(init.Normal(sigma=0.01))
loss = gloss.SoftmaxCrossEntropyLoss()
trainer = gluon.Trainer(net.collect_params(), 'sgd',
{'learning_rate': learning_rate})
utils.train_mnist(net=net,
train_iter=train_iter,
test_iter=test_iter,
loss=loss,
num_epochs=num_epochs,
batch_size=batch_size,
trainer=trainer)
def dropout(X, dropout_prob):
assert 0 <= dropout_prob <= 1
keep_prob = 1 - dropout_prob
if keep_prob == 0:
return X.zeros_like()
mask = nd.random.uniform(0, 1, X.shape) < keep_prob
return mask * X / keep_prob
def net(X):
X = X.reshape((-1, num_inputs))
H1 = (nd.dot(X, W1) + b1).relu()
if autograd.is_training():
H1 = dropout(H1, dropout_prob1)
H2 = (nd.dot(H1, W2) + b2).relu()
if autograd.is_training():
H2 = dropout(H2, dropout_prob2)
return nd.dot(H2, W3) + b3
loss = gloss.SoftmaxCrossEntropyLoss()
utils.train_mnist(net=net,
train_iter=train_iter,
test_iter=test_iter,
loss=loss,
num_epochs=num_epochs,
batch_size=batch_size,
params=params,
learning_rate=learning_rate)
import utils
from mxnet import gluon, init
from mxnet.gluon import data as gdata, nn, loss as gloss
num_inputs = 784
num_outputs = 10
batch_size = 256
num_workers = 4
num_epochs = 10
learning_rate = 0.1
# Load data
mnist_train = gdata.vision.FashionMNIST(train=True)
mnist_test = gdata.vision.FashionMNIST(train=False)
transformer = gdata.vision.transforms.ToTensor()
train_iter = gdata.DataLoader(mnist_train.transform_first(transformer), batch_size, shuffle=True, num_workers=num_workers)
test_iter = gdata.DataLoader(mnist_test.transform_first(transformer), batch_size, shuffle=False, num_workers=num_workers)
# Construct neural network model
net = nn.Sequential()
net.add(nn.Dense(num_outputs))
net.initialize(init.Normal(sigma=0.01))
loss = gloss.SoftmaxCrossEntropyLoss()
trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': learning_rate})
utils.train_mnist(net, train_iter, test_iter, loss, num_epochs, batch_size, None, None, trainer)
from mxnet import nd
from mxnet.gluon import data as gdata
def net(X):
return utils.softmax(nd.dot(X.reshape((-1, num_inputs)), w) + b)
num_inputs = 784
num_outputs = 10
batch_size = 256
num_workers = 4
num_epochs = 100
learning_rate = 0.1
w = nd.random.normal(scale=1.0, shape=(num_inputs, num_outputs))
b = nd.zeros(num_outputs)
w.attach_grad()
b.attach_grad()
mnist_train = gdata.vision.FashionMNIST(train=True)
mnist_test = gdata.vision.FashionMNIST(train=False)
transformer = gdata.vision.transforms.ToTensor()
train_iter = gdata.DataLoader(mnist_train.transform_first(transformer),
batch_size,
shuffle=True,
num_workers=num_workers)
test_iter = gdata.DataLoader(mnist_test.transform_first(transformer),
batch_size,
shuffle=False,
num_workers=num_workers)
utils.train_mnist(net, train_iter, test_iter, utils.cross_entropy, num_epochs,
batch_size, [w, b], learning_rate)