Question

我正在使用CIFAR-10数据集来训练我的神经网络，我得到的分数约为0.10，就好像我的神经网络只是在猜测（正确答案的1/10几率），CIFAR-10有10种类型的东西（汽车，飞机，猫，狗等）的32x32图像。我怀疑我的代码有问题。

顺便说一下，文件unpickling不是问题

请帮助我!!

from sklearn import datasets
from sklearn.neural_network import MLPClassifier
import numpy as np
import time

labels = ["airplane", "automobile", "bird", "cat", "deer", "dog", "frog", "horse", "ship", "truck"]

def unpickle(f):
    import cPickle
    fo = open(f, "rb")
    dict = cPickle.load(fo)
    fo.close()
    return dict

def setup_mlp(unpickled_data):
    new_mlp = MLPClassifier(hidden_layer_sizes=3072, solver='sgd', batch_size=1000, max_iter=500, random_state=1, learning_rate_init=0.01)
    return new_mlp
#hidden_layer_sizes is the number of neurons in a layer. In this case I have one hidden layer & 3072 neurons in that layer
if __name__ == "__main__":
    unpickled_batch = unpickle("./data_batch_1")
    print int(unpickled_batch["data"].shape[0])
    #time.sleep(1000)
    X_train = unpickled_batch["data"][:1000]
    Y_train = unpickled_batch["labels"][:1000]
    print "Decoded batch, now training\n"
    mlp = setup_mlp(unpickled_batch)
    mlp.fit(X_train, Y_train)
    print "Score=" + str(mlp.score(X_train, Y_train))

Answer 1

昨天我遇到了类似的问题，所以也许这可以帮到你。

如果您将数据集标准化，MLPClassifier在Cifar-10上运行良好，即添加

from sklearn import preprocessing X_train = preprocessing.scale(X_train)

加载数据后

。

但这还不够，因为你的测试集与训练集相同，你最终得分= 1.0，没有新图像预测的信息。

要解决此问题，您可以加载＆＃39; test_batch＆＃39;来自cifar-10文件或拆分unpickled_batch [＆＃34;数据＆＃34;]和unpickled_batch [＆＃34;标签＆＃34;]来训练i测试数据集。

之后你应该得到大约0.3分，这取决于测试集的大小。

多层感知器神经网络具有10％的准确度

1 个答案: