Question

您好我在此处发布的问题（https://www.kaggle.com/c/titanic）获得了以下代码的奇怪结果 -

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：

Train on 712 samples, validate on 179 samples
Epoch 0
712/712 [==============================] - 0s - loss: -0.0000 - val_loss: -0.0000
Epoch 1
712/712 [==============================] - 0s - loss: -0.0000 - val_loss: -0.0000
Epoch 2
712/712 [==============================] - 0s - loss: -0.0000 - val_loss: -0.0000
Epoch 3
712/712 [==============================] - 0s - loss: -0.0000 - val_loss: -0.0000
Epoch 4
712/712 [==============================] - 0s - loss: -0.0000 - val_loss: -0.0000
Epoch 5
712/712 [==============================] - 0s - loss: -0.0000 - val_loss: -0.0000
Epoch 6
712/712 [==============================] - 0s - loss: -0.0000 - val_loss: -0.0000
Epoch 7
712/712 [==============================] - 0s - loss: -0.0000 - val_loss: -0.0000
Epoch 8
712/712 [==============================] - 0s - loss: -0.0000 - val_loss: -0.0000
Epoch 9
712/712 [==============================] - 0s - loss: -0.0000 - val_loss: -0.0000

我正在尝试创建一个简单的3层网络。完全基本的代码。我在kaggle上使用keras之前尝试过这种分类问题。但这一次出现了这个错误。

由于数据较少，是否过度拟合？我错过了什么？有人可以帮忙吗？

Answer 1

老帖子，但无论如何要回答，以防其他人尝试使用Keras的泰坦尼克号。

您的网络参数太多，正规化太少（例如丢失）。

在model.compile之前调用model.summary（），它将显示您的网络有多少参数。在你的两个密集层之间，你应该有512 X 512 = 262,144个参数。对于762个例子来说，这很多。

另外，您可能希望在最后一层上使用sigmoid激活，并且因为您只有两个输出类，因此需要使用binary_cross熵丢失。

尝试Kaggle Titanic与keras ..得到损失和valid_loss -0.0000

1 个答案: