MXnet代码实战之CNN

介绍

CNN指的是卷积神经网络，这个介绍网上资料多的很，我就不介绍了，我这里主要是针对沐神教程的CNN代码做一个笔记。理解有不对的地方欢迎指出。

卷积神经网络里面最重要也是最基本的概念就是卷积层、池化层、全连接层、卷积核、参数共享等。

图：

这个图是对下面代码的一个描述，对于一张图片，首先处理成28*28，通过第一层卷积层，得到20个通道的输出（每个输出为24*24），通过第一个池化层，得到20个12*12的输出。第二层卷积层：输入有20个通道，输出有50个通道，所以这里有50个filter（卷积核），每一个filter都会去卷积20个通道，然后求和，得到一个输出。所以50个filter就得到50个输出啦（输出为10*10）。通过第二个池化层得到50个5*5的输出。然后就是flatten层，意思是把它们拉开成一条向量，所以就是5*5*50=1250啦。后面又接了二个全连接层，最终输出为1*10，正好对应10类（有多少类别都就通过全连接层处理成多少类）。其实这个就是常用的LeNet。

从0开始实现CNN

代码：

#!/usr/bin/env python# -*- coding:utf-8 -*-#Author: yuquanle#2017/10/28#沐神教程实战之CNN# 其实就是简单的LeNet雏形#本例子使用FashionMNIST数据集from mxnet import ndfrom mxnet import gluon# 处理输入数据def transform_mnist(data, label):    # change data from height x weight x channel to channel x height x weight    return nd.transpose(data.astype('float32'), (2,0,1))/255, label.astype('float32')def load_data_fashion_mnist(batch_size, transform=transform_mnist):    """download the fashion mnist dataest and then load into memory"""    mnist_train = gluon.data.vision.FashionMNIST(        train=True, transform=transform)    mnist_test = gluon.data.vision.FashionMNIST(        train=False, transform=transform)    train_data = gluon.data.DataLoader(        mnist_train, batch_size, shuffle=True)    test_data = gluon.data.DataLoader(        mnist_test, batch_size, shuffle=False)    return (train_data, test_data)# 载入数据batch_size = 256train_data, test_data = load_data_fashion_mnist(batch_size)import mxnet as mx# 优先使用GPU运算try:    ctx = mx.gpu()    _ = nd.zeros((1,), ctx=ctx)except:    ctx = mx.cpu()weight_scale = .01# output channels = 20, kernel = (5,5)W1 = nd.random_normal(shape=(20,1,5,5), scale=weight_scale, ctx=ctx)b1 = nd.zeros(W1.shape[0], ctx=ctx)# output channels = 50, kernel = (3,3)W2 = nd.random_normal(shape=(50,20,3,3), scale=weight_scale, ctx=ctx)b2 = nd.zeros(W2.shape[0], ctx=ctx)# output dim = 128W3 = nd.random_normal(shape=(1250, 128), scale=weight_scale, ctx=ctx)b3 = nd.zeros(W3.shape[1], ctx=ctx)# output dim = 10W4 = nd.random_normal(shape=(W3.shape[1], 10), scale=weight_scale, ctx=ctx)b4 = nd.zeros(W4.shape[1], ctx=ctx)params = [W1, b1, W2, b2, W3, b3, W4, b4]for param in params:    param.attach_grad()# 定义模型def net(X, verbose=False):    X = X.as_in_context(W1.context)    # 第⼀层卷积，kernel为5*5，filter有20个，所以卷积输出有20个通道    h1_conv = nd.Convolution( data=X, weight=W1, bias=b1, kernel=W1.shape[2:], num_filter=W1.shape[0])    # 激活函数    h1_activation = nd.relu(h1_conv)    # 池化    h1 = nd.Pooling(data=h1_activation, pool_type="max", kernel=(2, 2), stride=(2, 2))    # 第⼆层卷积，kernel为3*3，filter有50个，所以卷积输出有50个通道。输入有20个通道，所以每一个filter会卷积20个通道，然后求和    h2_conv = nd.Convolution(data=h1, weight=W2, bias=b2, kernel=W2.shape[2:], num_filter=W2.shape[0])    h2_activation = nd.relu(h2_conv)    h2 = nd.Pooling(data=h2_activation, pool_type="max", kernel=(2, 2), stride=(2, 2))    # flatten成向量    h2 = nd.flatten(h2)    # 第⼀层全连接    h3_linear = nd.dot(h2, W3) + b3    h3 = nd.relu(h3_linear)    # 第⼆层全连接    h4_linear = nd.dot(h3, W4) + b4    if verbose:        print('X:',X.shape)        print('h1_conv:', h1_conv.shape)        print('1st conv block:', h1.shape)        print('h2_conv:', h2_conv.shape)        print('2nd conv block:', h2.shape)        print('1st dense:', h3.shape)        print('2nd dense:', h4_linear.shape)        print('output:', h4_linear)    return h4_linear#   查看信息for data, _ in train_data:    print(data.shape)    net(data, verbose=True)    breakdef evaluate_accuracy(data_iterator, net, ctx=mx.cpu()):    acc = 0.    for data, label in data_iterator:        output = net(data.as_in_context(ctx))        acc += accuracy(output, label.as_in_context(ctx))    return acc / len(data_iterator)from mxnet import autograd as autogradfrom utils import SGD, accuracyfrom mxnet import gluon##softmax_cross_entropy = gluon.loss.SoftmaxCrossEntropyLoss()learning_rate = .2for 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()        SGD(params, learning_rate / batch_size)        train_loss += nd.mean(loss).asscalar()        train_acc += accuracy(output, label)        test_acc = evaluate_accuracy(test_data, net, ctx)    print("Epoch %d. Loss: %f, Train acc %f, Test acc %f" % (            epoch, train_loss / len(train_data),            train_acc / len(train_data), test_acc))

使用Gluon

代码：

#!/usr/bin/env python# -*- coding:utf-8 -*-#Author: yuquanle#2017/10/28#沐神教程实战之CNN，使用Gluon#本例子使用FashionMNIST数据集from mxnet import gluonfrom time import time# 处理输入数据def transform_mnist(data, label):    # change data from height x weight x channel to channel x height x weight    return nd.transpose(data.astype('float32'), (2,0,1))/255, label.astype('float32')def load_data_fashion_mnist(batch_size, transform=transform_mnist):    """download the fashion mnist dataest and then load into memory"""    mnist_train = gluon.data.vision.FashionMNIST(        train=True, transform=transform)    mnist_test = gluon.data.vision.FashionMNIST(        train=False, transform=transform)    train_data = gluon.data.DataLoader(        mnist_train, batch_size, shuffle=True)    test_data = gluon.data.DataLoader(        mnist_test, batch_size, shuffle=False)    return (train_data, test_data)# 获取数据batch_size = 256train_data, test_data = load_data_fashion_mnist(batch_size)# 定义模型from mxnet.gluon import nnnet = 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)    )# 初始化from mxnet import autogradfrom mxnet import ndimport mxnet as mx# 优先使用GPU运算try:    ctx = mx.gpu()    _ = nd.zeros((1,), ctx=ctx)except:    ctx = mx.cpu()net.initialize(ctx=ctx)print('initialize weight on', ctx)def _get_batch(batch, ctx):    """return data and label on ctx"""    if isinstance(batch, mx.io.DataBatch):        data = batch.data[0]        label = batch.label[0]    else:        data, label = batch    return (gluon.utils.split_and_load(data, ctx),            gluon.utils.split_and_load(label, ctx),            data.shape[0])def evaluate_accuracy(data_iterator, net, ctx=[mx.cpu()]):    if isinstance(ctx, mx.Context):        ctx = [ctx]    acc = nd.array([0])    n = 0.    if isinstance(data_iterator, mx.io.MXDataIter):        data_iterator.reset()    for batch in data_iterator:        data, label, batch_size = _get_batch(batch, ctx)        for X, y in zip(data, label):            acc += nd.sum(net(X).argmax(axis=1)==y).copyto(mx.cpu())        acc.wait_to_read() # don't push too many operators into backend        n += batch_size    return acc.asscalar() / ndef train(train_data, test_data, net, loss, trainer, ctx, num_epochs, print_batches=None):    """Train a network"""    print("Start training on ", ctx)    if isinstance(ctx, mx.Context):        ctx = [ctx]    for epoch in range(num_epochs):        train_loss, train_acc, n = 0.0, 0.0, 0.0        if isinstance(train_data, mx.io.MXDataIter):            train_data.reset()        start = time()        for i, batch in enumerate(train_data):            data, label, batch_size = _get_batch(batch, ctx)            losses = []            with autograd.record():                outputs = [net(X) for X in data]                losses = [loss(yhat, y) for yhat, y in zip(outputs, label)]            for l in losses:                l.backward()            train_acc += sum([(yhat.argmax(axis=1)==y).sum().asscalar()                              for yhat, y in zip(outputs, label)])            train_loss += sum([l.sum().asscalar() for l in losses])            trainer.step(batch_size)            n += batch_size            if print_batches and (i+1) % print_batches == 0:                print("Batch %d. Loss: %f, Train acc %f" % (                    n, train_loss/n, train_acc/n                ))        test_acc = evaluate_accuracy(test_data, net, ctx)        print("Epoch %d. Loss: %.3f, Train acc %.2f, Test acc %.2f, Time %.1f sec" % (            epoch, train_loss/n, train_acc/n, test_acc, time() - start        ))# 训练和测试loss = gluon.loss.SoftmaxCrossEntropyLoss()trainer = gluon.Trainer(net.collect_params(),'sgd', {'learning_rate': 0.5})train(train_data, test_data, net, loss, trainer, ctx, num_epochs=5)结果：initialize weight on gpu(0)Start training on  gpu(0)[10:50:57] d:\program files (x86)\jenkins\workspace\mxnet\mxnet\src\operator\./cudnn_algoreg-inl.h:106: Running performance tests to find the best convolution algorithm, this can take a while... (setting env variable MXNET_CUDNN_AUTOTUNE_DEFAULT to 0 to disable)Epoch 0. Loss: 1.140, Train acc 0.57, Test acc 0.80, Time 14.0 secEpoch 1. Loss: 0.457, Train acc 0.83, Test acc 0.82, Time 13.3 secEpoch 2. Loss: 0.380, Train acc 0.86, Test acc 0.77, Time 17.2 secEpoch 3. Loss: 0.342, Train acc 0.87, Test acc 0.87, Time 17.2 secEpoch 4. Loss: 0.311, Train acc 0.88, Test acc 0.88, Time 16.9 secProcess finished with exit code 0