In [ ]:

    

import numpy as np
import matplotlib.pyplot as plt

import torch
import torch.nn as nn
from torch.autograd import Variable


    

In [ ]:

    

train_X = np.random.normal(scale=2, size=(800, 69))
train_y = np.random.randint(0, high=10, size=(800,1), dtype=np.int64)


    

In [ ]:

    

class FullyConnectedLayers(nn.Module):
    
    def __init__(self, inputs, targets, targets_size=10, learning_rate=1e-4):
        super().__init__() # 在子类中调用父类的初始化方法
        self._train_X = inputs
        self._train_y = targets
        self._train_X_size = inputs.shape[1]
        self._train_y_size = targets_size
        self._learning_rate = learning_rate    
        
        self._fc1 = nn.Linear(self._train_X_size, self._train_X_size)
        self._ac1 = nn.ReLU() # activation function
        self._fc2 = nn.Linear(self._train_X_size, self._train_y_size)
        
        self._loss_function = nn.CrossEntropyLoss()
        self._optimizer = torch.optim.SGD(self.parameters(), lr=learning_rate)
        
    def forward(self, X):
        out = self._fc1(X)
        out = self._ac1(out)
        out = self._fc2(out)
        return out
    
    def fit(self, training_epochs= 1e3, display= 1e2):
        display = np.int(display)
        for epoch in np.arange(np.int(training_epochs)):
            inputs = Variable(torch.FloatTensor(self._train_X),requires_grad=True)
            targets = Variable(torch.LongTensor(self._train_y.flatten()))
            self._optimizer.zero_grad() #清空所有被优化过的Variable的梯度.
            outputs = self.forward(inputs) # 使用神经网络架构前向推断
            self._loss = self._loss_function(outputs, targets) # 计算批次损失函数
            self._loss.backward() # 误差反向传播
            self._optimizer.step()
            
            if (epoch+1) % display == 0:
                print ('Epoch (%d/%d), loss:%.4f' %(epoch+1, training_epochs, self._loss.data[0]))    
    
    def pred(self,X):
        inputs = (Variable(torch.FloatTensor(X)))
        outputs = self.forward(inputs)
        _, output_labels  = torch.max(outputs, 1)
        return output_labels
    
    
a = FullyConnectedLayers(train_X, train_y, 10)
a.fit(1e3, 1e2)


    

In [ ]:

    

a.pred(train_X)


    

In [ ]: