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| import torch import torch.nn as nn import torch.optim as optim import numpy as np import matplotlib.pyplot as plt import time import pandas as pd
class Net(nn.Module): def __init__(self, layers=[{ "name": "input", 'size': 11, 'act': nn.ReLU() }, { "name": "hidden1", "size": 1000, 'act': nn.ReLU() }, { "name": "hidden2", "size": 100, 'act': nn.ReLU() }, { "name": "opt", "size": 2, 'act': nn.ReLU() }]): super().__init__() self.layers = nn.Sequential() self.name = '' for i in range(len(layers)-1): layer = layers[i] nextLayer = layers[i+1] self.layers.add_module(layer['name'], nn.Linear( layer['size'], nextLayer['size'])) self.layers.add_module(layer['name']+'ACT', layer['act']) self.name += str(layer['size'])+'-' optLayer = layers[-1] self.layers.add_module('softMax', nn.Softmax(0)) self.name += str(optLayer['size'])
def forward(self,x): x = self.layers(x) return x
def train(self,eval=True, trainData=0, testData=0, optimizer=0,lossFunc=0,epoch=100, batchSize=100, batchNum=100): if(not eval): return print(self.name) trainLen = len(trainData['data']) print(trainLen) loss=0 for __ in range(epoch): cont = 0 for _ in range(batchNum): optimizer.zero_grad() for _ in range(10): fwd=self.forward(trainData['data'][cont]) loss = lossFunc(fwd, trainData['answer'][cont]) loss.backward() cont = (cont+1) % trainLen optimizer.step() if(__%10==0): print(loss) self.loss=str(loss) pass
def loadData(path): pass oneHot = [[0, 1], [1, 0]] data = pd.read_csv(path) data = data.drop(columns=['PassengerId', 'Name', 'Ticket', 'Cabin']) data = pd.get_dummies(data, columns=['Sex', 'Embarked']) data['NonAge'] = 0 data.loc[data['Age'].isna(), 'NonAge'] = 1 data['Age'] = data['Age'].fillna(0) data = data.fillna(0) print(data) if('Survived' not in data): data['Survived']=0 Answer = data.pop('Survived') for col in data: maximum = data[col].max() if maximum > 0: data[col] /= maximum answer = [] for i in range(len(Answer)): answer.append(oneHot[Answer[i]]) data = np.array(data) Answer = np.array(Answer) data = { 'data': torch.FloatTensor(data).cuda(), 'answer': torch.FloatTensor(answer).cuda() } return data
def output(net): testData=loadData('data/test.csv') net.eval() print(net) f=open('./result.csv','w') f.write('PassengerId,Survived\n') passid=892 for data in testData['data']: pass res=net.forward(data) _,index=res.max(0) if(index==0): f.write(str(passid)+',1\n') else: f.write(str(passid)+',0\n') passid+=1 f.close() pass
if(__name__ == '__main__'): data = loadData('data/train.csv') print(data['data'][1].dtype) for s in data['data']: if(s.dtype!=data['data'][1].dtype): print(s) net = Net() net=net.cuda() E = 0.0001 O = np.random.choice([optim.SGD(net.parameters(), lr=E, momentum=np.random.random()*0.9), optim.Adam(net.parameters(), lr=E), optim.Adagrad(net.parameters(), lr=E), optim.RMSprop(net.parameters(), lr=E, momentum=np.random.random()*0.9) ])
print(net.parameters()) print(O) net.train(True,data,1, optimizer=O,lossFunc=nn.MSELoss()) output(net)
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