#coding:utf-8
'''
多类别分类
'''
from mxnet import gluon
from mxnet import ndarray
from mxnet import autograd
import numpy as np
import matplotlib.pyplot as plt
from mxnet import nd
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)
# 显示样本的形状和标号
data,label = mnist_train[0]
print('shape:',data.shape,',label:',label)
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]
# 显示数据
# data,label = mnist_train[0:9]
# show_images(data)
# print(get_text_labels(label))
# 读取数据
batch_size = 64
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]
# 申请自动求导
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_prob)
# print(X_prob.sum(axis = 1))
# ots = nd.dot(X.reshape((-1,num_inputs)),W) + b
# print('X.reshape(-1,nums_input):',X.reshape((-1,num_inputs)).shape)
# print('ots.shape:',ots.shape)
# print('exp:',np.exp(ots.asnumpy()))
def net(X):
return softmax(nd.dot(X.reshape((-1,num_inputs)),W) + b)
# 定义损失函数,交叉熵损失函数
def cross_entropy(yhat,y):
return -nd.pick(nd.log(yhat),y)
# 定义精度计算
def accuracy(output,label):
return nd.mean(output.argmax(axis=1) == label).asscalar()
# 估计模型精度
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)
# 优化器
def SGD(params,lr):
for param in params:
param[:] = param - lr * param.grad
# 训练
learning_rate = 0.1
epochs = 5
for epoch in range(epochs):
train_loss = 0.0
train_acc = 0.0
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))
# 预测
data, label = mnist_test[0:9]
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动手学深度学习笔记:多类别分类
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转载自my.oschina.net/wujux/blog/1809140
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