Question

我开始学习如何使用Keras。我有一个原始文件，该文件使用相应的产品名称对句子中字符的ASCII值进行编码。例如，abcd toothpaste cream将被分类为Toothpaste。该代码的前两行（约150,000行中）如下所示。该文件也可以下载here（此链接将从今天开始持续两个月）。

12,15,11,31,30,15,0,26,28,15,29,30,19,17,15,0,19,24,30,15,28,24,11,30,0,18,19,17,19,15,24,15,0,35,0,12,15,22,22,15,36,11,0,12,15,22,22,15,36,11,0,16,28,11,17,11,24,13,19,11,29,0,16,15,23,15,24,19,24,11,29,0,11,36,36,15,14,19,24,15,0,11,36,36,15,14,19,24,15,11,22,11,19,11,0,26,15,28,16,31,23,15,0,16,15,23,15,24,19,24,25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Body Care Other
12,15,19,15,28,29,14,25,28,16,0,30,18,11,19,22,11,24,14,0,13,25,0,22,30,14,0,29,21,19,24,13,11,28,15,0,26,28,15,26,11,28,11,30,19,25,24,29,0,16,11,13,19,11,22,0,13,22,15,11,24,29,15,28,29,0,24,19,32,15,11,0,16,11,13,19,11,22,0,13,22,15,11,24,29,15,28,29,0,26,28,25,14,31,13,30,29,0,24,19,32,15,11,0,23,11,21,15,0,31,26,0,13,22,15,11,28,0,23,19,13,15,22,22,11,28,0,33,11,30,15,28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Skin Care Other

我正在关注blog post，其中它使用简单的深度学习Keras模型进行多类分类。我将神经网络的配置更改为243 inputs --> [100 hidden nodes] --> 67 outputs（因为我有67个要分类的类）。代码如下：

import numpy
import pandas
from keras.models import Sequential
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasClassifier
from keras.utils import np_utils
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold
from sklearn.preprocessing import LabelEncoder


def baseline_model():
    model = Sequential()

    # I changed it to 243 inputs --> [100 hidden nodes] --> 67 outputs (because I have 67 classes to classify)
    model.add(Dense(100, input_dim=X_len, activation='relu'))
    model.add(Dense(Y_cnt, activation='softmax'))

    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
    return model


seed = 7
numpy.random.seed(seed)

# load dataset
dataframe = pandas.read_csv("./input/raw_mappings.csv", header=None)
dataset = dataframe.values
X_len = len(dataset[0,:-1])
X = dataset[:,0:X_len].astype(float)
Y = dataset[:,X_len]
Y_cnt = len(numpy.unique(Y))

# encode class values as integers
encoder = LabelEncoder()
encoder.fit(Y)
encoded_Y = encoder.transform(Y)
# convert integers to dummy variables (i.e. one hot encoded)
dummy_y = np_utils.to_categorical(encoded_Y)


estimator = KerasClassifier(build_fn=baseline_model, epochs=200, batch_size=5, verbose=0)
kfold = KFold(n_splits=10, shuffle=True, random_state=seed)
results = cross_val_score(estimator, X, dummy_y, cv=kfold)
print("Baseline: %.2f%% (%.2f%%)" % (results.mean()*100, results.std()*100))

但是当我在台式机上运行超过12个小时时，它似乎从未完成。我开始认为几乎没有任何反应。神经网络的配置或我要解决的问题是否存在我做错的事情（这意味着Sequential model并不是对>60类进行分类的正确方法？）。

任何指针或技巧都将不胜感激。谢谢。

Keras多类分类过程需要很长时间

0 个答案: