使用gluon实现简单的CNN

Posted 2020-10-15 白菜hxj

from mxnet import ndarray as nd
from mxnet import gluon
from mxnet import autograd
from mxnet.gluon import nn

def transform(data, label):
    return nd.transpose(data.astype(np.float32), (2,0,1))/255, label.astype(np.float32)
mnist_train = gluon.data.vision.FashionMNIST(train=True, transform=transform)
mnist_test = gluon.data.vision.FashionMNIST(train=False, transform=transform)

batch_size = 256
train_data = gluon.data.DataLoader(mnist_train, batch_size, shuffle=True)
test_data = gluon.data.DataLoader(mnist_test, batch_size, shuffle=False)

import mxnet as mx
try:
    ctx = mx.gpu()
    _ = nd.zeros((1,), ctx = ctx)
except:
    ctx = mx.cpu()
ctx

def accuracy(output, label):
    return nd.mean(output.argmax(axis=1)==label).asscalar()

def evaluate_accuracy(data_iterator, net):
    acc = 0.
    for data, label in data_iterator:
        output = net(data)
        acc += accuracy(output, label)
    return acc / len(data_iterator)

net = nn.Sequential()
with net.name_scope():
        net.add(
            nn.Conv2D(channels=20, kernel_size=5, activation=‘relu‘),
            nn.MaxPool2D(pool_size=2, strides=2),
            nn.Conv2D(channels=50, kernel_size=3, activation=‘relu‘),
            nn.MaxPool2D(pool_size=2, strides=2),
            nn.Flatten(),
            nn.Dense(128, activation="relu"),
            nn.Dense(10))

net.initialize(ctx=ctx)
softmax_cross_entropy = gluon.loss.SoftmaxCrossEntropyLoss()
trainer = gluon.Trainer(net.collect_params(), ‘sgd‘, {‘learning_rate‘: 0.2})

softmax_cross_entropy = gluon.loss.SoftmaxCrossEntropyLoss()

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))