from __future__ import print_function
import mxnet as mx
from mxnet import nd, autograd
from mxnet import gluon
import numpy as np
mx.random.seed(1)
ctx = mx.cpu()
batch_size = 64
def transform(data, label):
return nd.transpose(data.astype(np.float32), (2,0,1))/255, label.astype(np.float32)
train_data = mx.gluon.data.DataLoader(mx.gluon.data.vision.MNIST(train=True, transform=transform),
batch_size, shuffle=True)
test_data = mx.gluon.data.DataLoader(mx.gluon.data.vision.MNIST(train=False, transform=transform),
batch_size, shuffle=False)
from mxnet.gluon import nn
def vgg_block(num_convs, channels):
out = nn.Sequential()
for _ in range(num_convs):
out.add(nn.Conv2D(channels=channels, kernel_size=3,
padding=1, activation='relu'))
out.add(nn.MaxPool2D(pool_size=2, strides=2))
return out
def vgg_stack(architecture):
out = nn.Sequential()
for (num_convs, channels) in architecture:
out.add(vgg_block(num_convs, channels))
return out
num_outputs = 10
architecture = ((1,64), (1,128), (2,256), (2,512))
net = nn.Sequential()
with net.name_scope():
net.add(vgg_stack(architecture))
net.add(nn.Flatten())
net.add(nn.Dense(512, activation="relu"))
net.add(nn.Dropout(.5))
net.add(nn.Dense(512, activation="relu"))
net.add(nn.Dropout(.5))
net.add(nn.Dense(num_outputs))
net.collect_params().initialize(mx.init.Xavier(magnitude=2.24), ctx=ctx)
trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': .05})
softmax_cross_entropy = gluon.loss.SoftmaxCrossEntropyLoss()
def accuracy(output, label):
return nd.mean(output.argmax(axis=1)==label).asscalar()
def evaluate_accuracy(data_iterator, net):
acc = mx.metric.Accuracy()
for d, l in data_iterator:
data = d.as_in_context(ctx)
label = l.as_in_context(ctx)
output = net(data)
predictions = nd.argmax(output, axis=1)
acc.update(preds=predictions, labels=label)
return acc.get()[1]
epochs = 5
smoothing_constant = .01
for epoch in range(5):
train_loss = 0.
train_acc = 0.
for data, label in train_data:
label = label.as_in_context(ctx)
with autograd.record():
output = net(data)
loss = softmax_cross_entropy(output, label)
loss.backward()
trainer.step(batch_size)
train_loss += nd.mean(loss).asscalar()
train_acc += accuracy(output, label)
test_acc = evaluate_accuracy(test_data, net)
print("Epoch %d. Loss: %f, Train acc %f, Test acc %f" % (epoch, train_loss/len(train_data),train_acc/len(train_data), test_acc))
