上一篇演示了纯手动添加隐藏层,这次使用gluon让代码更精减,代码来自:https://zh.gluon.ai/chapter_supervised-learning/mlp-gluon.html
from mxnet import gluon from mxnet import ndarray as nd import matplotlib.pyplot as plt import mxnet as mx from mxnet import autograd def transform(data, label): return data.astype('float32')/255, label.astype('float32') mnist_train = gluon.data.vision.FashionMNIST(train=True, transform=transform) mnist_test = gluon.data.vision.FashionMNIST(train=False, transform=transform) def show_images(images): n = images.shape[0] _, figs = plt.subplots(1, n, figsize=(15, 15)) for i in range(n): figs[i].imshow(images[i].reshape((28, 28)).asnumpy()) figs[i].axes.get_xaxis().set_visible(False) figs[i].axes.get_yaxis().set_visible(False) plt.show() def get_text_labels(label): text_labels = [ 'T 恤', '长 裤', '套头衫', '裙 子', '外 套', '凉 鞋', '衬 衣', '运动鞋', '包 包', '短 靴' ] return [text_labels[int(i)] for i in label] data, label = mnist_train[0:10] print('example shape: ', data.shape, 'label:', label) show_images(data) print(get_text_labels(label)) 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) #计算模型 net = gluon.nn.Sequential() with net.name_scope(): net.add(gluon.nn.Flatten()) net.add(gluon.nn.Dense(256, activation="relu")) net.add(gluon.nn.Dense(10)) net.initialize() softmax_cross_entropy = gluon.loss.SoftmaxCrossEntropyLoss() #定义训练器 trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.5}) def accuracy(output, label): return nd.mean(output.argmax(axis=1) == label).asscalar() def _get_batch(batch): if isinstance(batch, mx.io.DataBatch): data = batch.data[0] label = batch.label[0] else: data, label = batch return data, label def evaluate_accuracy(data_iterator, net): acc = 0. if isinstance(data_iterator, mx.io.MXDataIter): data_iterator.reset() for i, batch in enumerate(data_iterator): data, label = _get_batch(batch) output = net(data) acc += accuracy(output, label) return acc / (i+1) for epoch in range(5): train_loss = 0. train_acc = 0. for data, label in train_data: with autograd.record(): output = net(data) loss = softmax_cross_entropy(output, label) loss.backward() trainer.step(batch_size) #使用训练器,向"前"走一步 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)) data, label = mnist_test[0:10] show_images(data) print('true labels') print(get_text_labels(label)) predicted_labels = net(data).argmax(axis=1) print('predicted labels') print(get_text_labels(predicted_labels.asnumpy()))
有变化的地方,已经加上了注释。运行效果,跟一篇完全相同,就不重复贴图了
作者:菩提树下的杨过
出处:http://yjmyzz.cnblogs.com
本文版权归作者和博客园共有,欢迎转载,但未经作者同意必须保留此段声明,且在文章页面明显位置给出原文连接,否则保留追究法律责任的权利。
出处:http://yjmyzz.cnblogs.com
本文版权归作者和博客园共有,欢迎转载,但未经作者同意必须保留此段声明,且在文章页面明显位置给出原文连接,否则保留追究法律责任的权利。