mxnet-深度学习网络

发布时间：2023-09-06 02:22责任编辑：郭大石关键词：深度学习

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<NDArray 200x1x28x28 @cpu(0)>
[8 3 9 ... 4 3 0]
<NDArray 200 @cpu(0)>
Sequential(
(0): Conv2D(None -> 6, kernel_size=(5, 5), stride=(1, 1))
(1): MaxPool2D(size=(2, 2), stride=(2, 2), padding=(0, 0), ceil_mode=False)
(2): Conv2D(None -> 16, kernel_size=(3, 3), stride=(1, 1))
(3): MaxPool2D(size=(2, 2), stride=(2, 2), padding=(0, 0), ceil_mode=False)
(4): Flatten
(5): Dense(None -> 120, Activation(relu))
(6): Dense(None -> 84, Activation(relu))
(7): Dense(None -> 10, linear)
)
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Epoch 9: Loss: 0.007, Train acc 0.001, Test acc 0.004, \ Time 15.3 sec
Epoch 9: Loss: 0.007, Train acc 0.001, Test acc 0.008, \ Time 15.4 sec
Epoch 9: Loss: 0.007, Train acc 0.001, Test acc 0.012, \ Time 15.4 sec
Epoch 9: Loss: 0.007, Train acc 0.001, Test acc 0.017, \ Time 15.5 sec
Epoch 9: Loss: 0.007, Train acc 0.001, Test acc 0.022, \ Time 15.5 sec
Epoch 9: Loss: 0.007, Train acc 0.001, Test acc 0.026, \ Time 15.5 sec
Epoch 9: Loss: 0.007, Train acc 0.001, Test acc 0.032, \ Time 15.6 sec
Epoch 9: Loss: 0.007, Train acc 0.001, Test acc 0.035, \ Time 15.6 sec
Epoch 9: Loss: 0.007, Train acc 0.001, Test acc 0.039, \ Time 15.7 sec
Epoch 9: Loss: 0.007, Train acc 0.001, Test acc 0.044, \ Time 15.7 sec
Epoch 9: Loss: 0.007, Train acc 0.001, Test acc 0.049, \ Time 15.7 sec
Epoch 9: Loss: 0.007, Train acc 0.001, Test acc 0.054, \ Time 15.8 sec
Epoch 9: Loss: 0.007, Train acc 0.001, Test acc 0.059, \ Time 15.8 sec
Epoch 9: Loss: 0.007, Train acc 0.001, Test acc 0.063, \ Time 15.8 sec
Epoch 9: Loss: 0.007, Train acc 0.001, Test acc 0.066, \ Time 15.9 sec
Epoch 9: Loss: 0.007, Train acc 0.001, Test acc 0.071, \ Time 15.9 sec
Epoch 9: Loss: 0.007, Train acc 0.001, Test acc 0.075, \ Time 16.0 sec
Epoch 9: Loss: 0.007, Train acc 0.001, Test acc 0.080, \ Time 16.0 sec
Epoch 9: Loss: 0.007, Train acc 0.001, Test acc 0.084, \ Time 16.0 sec
Epoch 9: Loss: 0.007, Train acc 0.001, Test acc 0.088, \ Time 16.1 sec
Epoch 9: Loss: 0.007, Train acc 0.001, Test acc 0.092, \ Time 16.1 sec
Epoch 9: Loss: 0.007, Train acc 0.001, Test acc 0.097, \ Time 16.2 sec
Epoch 9: Loss: 0.007, Train acc 0.001, Test acc 0.101, \ Time 16.2 sec
Epoch 9: Loss: 0.007, Train acc 0.001, Test acc 0.105, \ Time 16.2 sec
Epoch 9: Loss: 0.007, Train acc 0.001, Test acc 0.109, \ Time 16.3 sec
Epoch 9: Loss: 0.007, Train acc 0.001, Test acc 0.114, \ Time 16.3 sec
Epoch 9: Loss: 0.007, Train acc 0.001, Test acc 0.118, \ Time 16.4 sec
Epoch 9: Loss: 0.007, Train acc 0.001, Test acc 0.123, \ Time 16.4 sec
Epoch 9: Loss: 0.007, Train acc 0.001, Test acc 0.127, \ Time 16.4 sec
Epoch 9: Loss: 0.007, Train acc 0.001, Test acc 0.131, \ Time 16.5 sec
Epoch 9: Loss: 0.007, Train acc 0.001, Test acc 0.136, \ Time 16.5 sec
Epoch 9: Loss: 0.007, Train acc 0.001, Test acc 0.140, \ Time 16.6 sec
Epoch 9: Loss: 0.007, Train acc 0.001, Test acc 0.145, \ Time 16.6 sec
Epoch 9: Loss: 0.007, Train acc 0.001, Test acc 0.150, \ Time 16.6 sec
Epoch 9: Loss: 0.007, Train acc 0.001, Test acc 0.155, \ Time 16.7 sec
Epoch 9: Loss: 0.007, Train acc 0.001, Test acc 0.160, \ Time 16.7 sec
Epoch 9: Loss: 0.007, Train acc 0.001, Test acc 0.164, \ Time 16.8 sec
Epoch 9: Loss: 0.007, Train acc 0.001, Test acc 0.168, \ Time 16.8 sec
Epoch 9: Loss: 0.007, Train acc 0.001, Test acc 0.173, \ Time 16.8 sec
Epoch 9: Loss: 0.007, Train acc 0.001, Test acc 0.177, \ Time 16.9 sec
Epoch 9: Loss: 0.007, Train acc 0.001, Test acc 0.181, \ Time 16.9 sec
Epoch 9: Loss: 0.007, Train acc 0.001, Test acc 0.186, \ Time 17.0 sec
Epoch 9: Loss: 0.007, Train acc 0.001, Test acc 0.190, \ Time 17.0 sec
Epoch 9: Loss: 0.007, Train acc 0.001, Test acc 0.194, \ Time 17.0 sec
Epoch 9: Loss: 0.007, Train acc 0.001, Test acc 0.198, \ Time 17.1 sec
Epoch 9: Loss: 0.007, Train acc 0.001, Test acc 0.202, \ Time 17.1 sec
Epoch 9: Loss: 0.007, Train acc 0.001, Test acc 0.206, \ Time 17.2 sec
Epoch 9: Loss: 0.007, Train acc 0.001, Test acc 0.210, \ Time 17.2 sec
Epoch 9: Loss: 0.007, Train acc 0.001, Test acc 0.213, \ Time 17.2 sec
Epoch 9: Loss: 0.007, Train acc 0.001, Test acc 0.217, \ Time 17.3 sec

#!/usr/bin/env python2# -*- coding: utf-8 -*-"""Created on Fri Aug 10 16:13:29 2018@author: myhaspl"""from mxnet import nd, gluon, init, autogradfrom mxnet.gluon import nnfrom mxnet.gluon.data.vision import datasets,transforms import matplotlib.pyplot as pltfrom time import timemnist_train = datasets.FashionMNIST(train=True)X, y = mnist_train[0]print (‘X shape: ‘, X.shape, ‘X dtype‘, X.dtype, ‘y:‘, y,‘Y dtype‘, y.dtype)#x:(height, width, channel)#y:numpy.scalar，标签text_labels = [ ???????????‘t-shirt‘, ‘trouser‘, ‘pullover‘, ‘dress‘, ‘coat‘, ???????????‘sandal‘, ‘shirt‘, ‘sneaker‘, ‘bag‘, ‘ankle boot‘]X, y = mnist_train[0:6]#取6个样本_, figs = plt.subplots(1, X.shape[0], figsize=(15, 15))for f,x,yi in zip(figs, X,y): ???# 3D->2D by removing the last channel dim ???f.imshow(x.reshape((28,28)).asnumpy()) ???ax = f.axes ???ax.set_title(text_labels[int(yi)]) ???ax.title.set_fontsize(20) ???ax.get_xaxis().set_visible(False) ???ax.get_yaxis().set_visible(False)plt.show()#转换图像为(channel, height, weight)格式，并且为floating数据类型，通过transforms.ToTensor。#另外，normalize所有像素值 使用 transforms.Normalize平均值0.13和标准差0.31. transformer = transforms.Compose([ ???????????transforms.ToTensor(), ???????????transforms.Normalize(0.13, 0.31)])#只转换第一个元素，图像部分。第二个元素为标签。mnist_train = mnist_train.transform_first(transformer)#加载批次数据batch_size = 200train_data = gluon.data.DataLoader(mnist_train, batch_size=batch_size, shuffle=True)#读取本批数据i=1for data, label in train_data: ???print i ???print data,label ???break#没有这一行，会以每批次200个数据来读取。mnist_valid = gluon.data.vision.FashionMNIST(train=False)valid_data = gluon.data.DataLoader(mnist_valid.transform_first(transformer),batch_size=batch_size)#定义网络net = nn.Sequential()net.add(nn.Conv2D(channels=6,kernel_size=5,activation="relu"), ???????nn.MaxPool2D(pool_size=2, strides=2), ???????nn.Conv2D(channels=16, kernel_size=3, activation="relu"), ???????nn.MaxPool2D(pool_size=2, strides=2), ???????nn.Flatten(), ???????nn.Dense(120, activation="relu"), ???????nn.Dense(84, activation="relu"), ???????nn.Dense(10))net.initialize(init=init.Xavier())print net#输出softmax与误差softmax_cross_entropy = gluon.loss.SoftmaxCrossEntropyLoss()#定义训练器trainer = gluon.Trainer(net.collect_params(), ‘sgd‘, {‘learning_rate‘: 0.1})def acc(output, label): ???# output: (batch, num_output) float32 ndarray ???# label: (batch, ) int32 ndarray ???return (output.argmax(axis=1) == label.astype(‘float32‘)).mean().asscalar()for epoch in range(10): ???train_loss, train_acc, valid_acc = 0., 0., 0. ???tic = time() ???for data, label in train_data: ???????# 前向与反馈 ???????with autograd.record(): ???????????output = net(data) ???????????loss = softmax_cross_entropy(output, label) ???????loss.backward() ???# 换一批样本数据，更新参数 ???trainer.step(batch_size) ???# 计算训练误差和正确率 ???train_loss += loss.mean().asscalar() ???train_acc += acc(output, label) ???print "." ???#测试正确率 ???for data, label in valid_data: ????????valid_acc += acc(net(data), label) ???????print("Epoch %d: Loss: %.3f, Train acc %.3f, Test acc %.3f, \ Time %.1f sec" % ( ????????????????epoch, train_loss/len(train_data), ????????????????train_acc/len(train_data), ????????????????valid_acc/len(valid_data), time()-tic))#!/usr/bin/env python2# -*- coding: utf-8 -*-#非深度网络from mxnet import gluonfrom mxnet import ndarray as ndimport matplotlib.pyplot as pltimport mxnet as mxfrom mxnet import autograddef 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-shirt‘, ‘trouser‘, ‘pullover‘, ‘dress‘, ‘coat‘, ???????????????‘sandal‘, ‘shirt‘, ‘sneaker‘, ‘bag‘, ‘ankle boot‘ ???] ???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 = 256train_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, labeldef 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()))

mxnet-深度学习网络

原文地址：http://blog.51cto.com/13959448/2316672

mxnet-深度学习网络

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