mxnet实现softmax regression（多层感知机）

'coding = utf-8'
import mxnet.ndarray as nd
from mxnet import gluon
import matplotlib.pyplot as plt
from mxnet import nd
import sys
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)

data,label = mnist_train[0]
print('example shape:',data.shape,'label:',label)

def show_images(images):
    n = images.shape[0]
    _,figs = plt.subplot(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[0:9]
show_images(data)
print(get_text_labels(label))

#数据读取
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)

#初始化模型参数
num_inputs = 784
num_outputs = 10

w = nd.random_normal(shape=(num_inputs,num_outputs))
b = nd.random_normal(shape=num_outputs)

params = [w,b]

for param in params:
    param.attach_grad()


#定义模型
def softmax(x):
    exp = nd.exp(x)
    partition = exp.sum(axis=1,keepdims=True)
    return exp / partition

x = nd.random_normal(shape=(2,5))
x_prob = softmax(x)
print(x)
print(x_prob)
print(x_prob.sum(axis=1))

def net(x):
    return softmax(nd.dot(x.reshape((-1,num_inputs)),w) + b)

#交叉熵loss
def cross_entropy(yhat,y):
    return - nd.pick(nd.log(yhat), y)

#计算精度
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)

evaluate_accuracy(test_data,net)

#训练
def SGD(params,lr):
    for param in params:
        param[:] = param - lr * param.grad
sys.path.append('..')

learning_rate = 0.1

for epoch in range(5):
    train_loss = 0.
    train_acc = 0.
    for data,label in train_data:
        with autograd.record():
            output = net(data)
            loss = 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)
    print('Epoch %d.Loss: %f,Train_acc %f,Test_acc %f' % (epoch,train_loss/len(train_data),train_acc/len(train_data),test_acc/len(test_data)))

#预测
data,label = mnist_test[0:9]
show_images(data)
print('ture lables')
print(get_text_labels(label))

predicted_lables = net(data).argmax(axis=1)
print('predicted lables')
print(get_text_labels(predicted_lables.asnumpy()))
mxnet实现softmax regression（多层感知机）

猜你喜欢
