Python使用gluon/mxnet模块实现的mnist手写数字识别功能完整示例

Python  /  管理员 发布于 7年前   172

本文实例讲述了Python使用gluon/mxnet模块实现的mnist手写数字识别功能。分享给大家供大家参考，具体如下：

import gluonbook as gbfrom mxnet import autograd,nd,init,gluonfrom mxnet.gluon import loss as gloss,data as gdata,nn,utils as gutilsimport mxnet as mxnet = nn.Sequential()with net.name_scope():  net.add(    nn.Conv2D(channels=32, kernel_size=5, activation='relu'),    nn.MaxPool2D(pool_size=2, strides=2),    nn.Flatten(),    nn.Dense(128, activation='sigmoid'),    nn.Dense(10, activation='sigmoid')  )lr = 0.5batch_size=256ctx = mx.gpu()net.initialize(init=init.Xavier(), ctx=ctx)train_data, test_data = gb.load_data_fashion_mnist(batch_size)trainer = gluon.Trainer(net.collect_params(),'sgd',{'learning_rate' : lr})loss = gloss.SoftmaxCrossEntropyLoss()num_epochs = 30def train(train_data, test_data, net, loss, trainer,num_epochs):  for epoch in range(num_epochs):    total_loss = 0    for x,y in train_data:      with autograd.record():        x = x.as_in_context(ctx)        y = y.as_in_context(ctx)        y_hat=net(x)        l = loss(y_hat,y)      l.backward()      total_loss += l      trainer.step(batch_size)    mx.nd.waitall()    print("Epoch [{}]: Loss {}".format(epoch, total_loss.sum().asnumpy()[0]/(batch_size*len(train_data))))if __name__ == '__main__':  try:    ctx = mx.gpu()    _ = nd.zeros((1,), ctx=ctx)  except:    ctx = mx.cpu()  ctx  gb.train(train_data,test_data,net,loss,trainer,ctx,num_epochs)

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