和凯拉一起尝试卡格尔泰坦尼克号。。损失和有效损失0.0000

Question

嗨，我得到了以下关于发布在这里的问题的代码的奇怪结果（https://www.kaggle.com/c/titanic）-

from keras.models import Sequential
from keras.layers.core import Dense, Activation, Dropout
from keras.layers.advanced_activations import PReLU, LeakyReLU
from keras.layers.recurrent import SimpleRNN, SimpleDeepRNN
from keras.layers.embeddings import Embedding
from keras.layers.recurrent import LSTM, GRU

import pandas as pd
import numpy as np 
from sklearn import preprocessing

np.random.seed(1919)

### Constants ###
data_folder = "/home/saj1919/Public/Data_Science_Mining_Study/submissions/titanic/data/"
out_folder = "/home/saj1919/Public/Data_Science_Mining_Study/submissions/titanic/output/"
batch_size = 4
nb_epoch = 10

### load train and test ###
train  = pd.read_csv(data_folder+'train.csv', index_col=0)
test  = pd.read_csv(data_folder+'test.csv', index_col=0)
print "Data Read complete"

Y = train.Survived
train.drop('Survived', axis=1, inplace=True)

columns = train.columns
test_ind = test.index

train['Age'] = train['Age'].fillna(train['Age'].mean())
test['Age'] = test['Age'].fillna(test['Age'].mean())
train['Fare'] = train['Fare'].fillna(train['Fare'].mean())
test['Fare'] = test['Fare'].fillna(test['Fare'].mean())

category_index = [0,1,2,4,5,6,8,9]
for i in category_index:
    print str(i)+" : "+columns[i]
    train[columns[i]] = train[columns[i]].fillna('missing')
    test[columns[i]] = test[columns[i]].fillna('missing')

train = np.array(train)
test = np.array(test)

### label encode the categorical variables ###
for i in category_index:
    print str(i)+" : "+str(columns[i])
    lbl = preprocessing.LabelEncoder()
    lbl.fit(list(train[:,i]) + list(test[:,i]))
    train[:,i] = lbl.transform(train[:,i])
    test[:,i] = lbl.transform(test[:,i])

### making data as numpy float ###
train = train.astype(np.float32)
test = test.astype(np.float32)
#Y = np.array(Y).astype(np.int32)

model = Sequential()
model.add(Dense(len(columns), 512))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(512, 1))
model.add(Activation('softmax'))

model.compile(loss='categorical_crossentropy', optimizer="adam")
model.fit(train, Y, nb_epoch=nb_epoch, batch_size=batch_size, validation_split=0.20)
preds = model.predict(test,batch_size=batch_size)

pred_arr = []
for pred in preds:
    pred_arr.append(pred[0])

### Output Results ###
preds = pd.DataFrame({"PassengerId": test_ind, "Survived": pred_arr})
preds = preds.set_index('PassengerId')
preds.to_csv(out_folder+'test.csv')

我得到以下结果：

^{pr2}$

我试图创建一个简单的3层网络。完全是基本代码。 在kaggle上使用keras之前，我已经尝试过这些分类问题。但这次却犯了这个错误。在

是否由于数据较少而过度拟合。 我错过了什么？有人能帮忙吗？在

Answer 1

老帖子，但无论如何都要回答，以防有人用凯拉斯尝试泰坦尼克号。在

您的网络可能有太多的参数和太少的正则化（例如，辍学）。在

呼叫模型.摘要（）就在模型.编译它会显示你的网络有多少个参数。就在你的两个密集层之间，你应该有512×512=262144个参数。对于762个例子来说，这已经是很多了。在

另外，您可能希望在最后一层使用sigmoid激活和二进制交叉熵损失，因为您只有两个输出类。在