多层感知机训练minist数据集

 

MLP

 

 

In [1]:

%matplotlib inline
import gluonbook as gb
from mxnet.gluon import loss as gloss
from mxnet import nd
from mxnet import autograd

In [2]:

batch_size = 256
train_iter, test_iter = gb.load_data_fashion_mnist(batch_size)

 

模型参数初始化

In [3]:

num_inputs, num_out_puts, num_hiddens = 28*28, 10, 256
W1 = nd.random.normal(scale=0.01,shape=(num_inputs,num_hiddens))
b1 = nd.zeros(num_hiddens)
W2 = nd.random.normal(scale=0.01,shape=(num_hiddens,num_out_puts))
b2 = nd.zeros(num_out_puts)
params = [W1,b1,W2,b2]

for param in params:
    param.attach_grad()

 

激活函数

In [4]:

def relu(X):
    return nd.maximum(X,0)

In [5]:

X = nd.array([[1,3,-1],[2,-2,-1]])
relu(X)

Out[5]:

[[1. 3. 0.]
 [2. 0. 0.]]
<NDArray 2x3 @cpu(0)>

 

定义模型 H = relu(XW+b) O = HW + b

In [6]:

def net(X):
    X = X.reshape((-1, num_inputs))
    H = relu(nd.dot(X,W1) + b1)
    return nd.dot(H,W2) + b2

 

softmax损失函数

In [7]:

loss = gloss.SoftmaxCrossEntropyLoss()

 

调整参数

In [9]:

def sgd(params, lr, batch_size):
    for param in params:
        param[:] = param - lr * param.grad / batch_size

 

是否预测中

In [10]:

def accuracy(y_hat,y):
    return (y_hat.argmax(axis=1)==y.astype('float32')).mean().asscalar()

 

正确率

In [11]:

def evaluate_accuracy(data_iter,net):
    acc = 0
    for X,y in data_iter:
        acc+= accuracy(net(X),y)
    return acc / len(data_iter)

 

训练模型

In [12]:

def train(net,train_iter,test_iter,loss,num_epochs,batch_size,params=None,lr=None,trainer=None):
    for epoch in range(num_epochs):
        train_l_sum = 0
        train_acc_sum = 0
        for X,y in train_iter:
            with autograd.record():
                y_hat = net(X)
                l = loss(y_hat,y)
            l.backward()
            if trainer is None:
                sgd(params, lr , batch_size)
            else:
                trainer.step(batch_size)
            train_l_sum += l.mean().asscalar()
            train_acc_sum += accuracy(y_hat,y)
        test_acc = evaluate_accuracy(test_iter,net)
        print('epoch %d, loss %.4f, train acc %.3f,test acc %.3f'
              %(epoch+1, train_l_sum / len(train_iter),
               train_acc_sum / len(train_iter),test_acc))

num_epochs , lr = 5, 0.1
train(net, train_iter,test_iter,loss,num_epochs,batch_size,params,lr)        

 

epoch 1, loss 1.0423, train acc 0.640,test acc 0.745
epoch 2, loss 0.6048, train acc 0.787,test acc 0.818
epoch 3, loss 0.5297, train acc 0.814,test acc 0.833
epoch 4, loss 0.4827, train acc 0.831,test acc 0.842
epoch 5, loss 0.4626, train acc 0.837,test acc 0.846

In [ ]:

 

转载于:https://www.cnblogs.com/TreeDream/p/10020964.html