【发布时间】：2017-07-18 11:36:50
【问题描述】：
如果耐心达到我设置的数字，我希望分类器运行得更快并提前停止。在下面的代码中，它会进行 10 次迭代来拟合模型。
import numpy
import pandas
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
from keras.wrappers.scikit_learn import KerasClassifier
from keras.callbacks import EarlyStopping, ModelCheckpoint
from keras.constraints import maxnorm
from keras.optimizers import SGD
from sklearn.model_selection import cross_val_score
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import StratifiedKFold
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
# fix random seed for reproducibility
seed = 7
numpy.random.seed(seed)
# load dataset
dataframe = pandas.read_csv("sonar.csv", header=None)
dataset = dataframe.values
# split into input (X) and output (Y) variables
X = dataset[:,0:60].astype(float)
Y = dataset[:,60]
# encode class values as integers
encoder = LabelEncoder()
encoder.fit(Y)
encoded_Y = encoder.transform(Y)
calls=[EarlyStopping(monitor='acc', patience=10), ModelCheckpoint('C:/Users/Nick/Data Science/model', monitor='acc', save_best_only=True, mode='auto', period=1)]
def create_baseline():
# create model
model = Sequential()
model.add(Dropout(0.2, input_shape=(33,)))
model.add(Dense(33, init='normal', activation='relu', W_constraint=maxnorm(3)))
model.add(Dense(16, init='normal', activation='relu', W_constraint=maxnorm(3)))
model.add(Dense(122, init='normal', activation='softmax'))
# Compile model
sgd = SGD(lr=0.1, momentum=0.8, decay=0.0, nesterov=False)
model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
return model
numpy.random.seed(seed)
estimators = []
estimators.append(('standardize', StandardScaler()))
estimators.append(('mlp', KerasClassifier(build_fn=create_baseline, nb_epoch=300, batch_size=16, verbose=0, callbacks=calls)))
pipeline = Pipeline(estimators)
kfold = StratifiedKFold(n_splits=10, shuffle=True, random_state=seed)
results = cross_val_score(pipeline, X, encoded_Y, cv=kfold)
print("Baseline: %.2f%% (%.2f%%)" % (results.mean()*100, results.std()*100))
这是产生的错误-
RuntimeError: Cannot clone object <keras.wrappers.scikit_learn.KerasClassifier object at 0x000000001D691438>, as the constructor does not seem to set parameter callbacks
我在下面更改了 cross_val_score-
numpy.random.seed(seed)
estimators = []
estimators.append(('standardize', StandardScaler()))
estimators.append(('mlp', KerasClassifier(build_fn=create_baseline, nb_epoch=300, batch_size=16, verbose=0, callbacks=calls)))
pipeline = Pipeline(estimators)
kfold = StratifiedKFold(n_splits=10, shuffle=True, random_state=seed)
results = cross_val_score(pipeline, X, encoded_Y, cv=kfold, fit_params={'callbacks':calls})
print("Baseline: %.2f%% (%.2f%%)" % (results.mean()*100, results.std()*100))
现在我得到了这个错误-
ValueError: need more than 1 value to unpack
This code came from here。该代码是迄今为止我使用过的最准确的代码。问题是代码中的任何地方都没有定义model.fit()。它也需要永远适应。 fit() 操作发生在 results = cross_val_score(...) 并且没有参数可以在那里引发回调。
我该怎么做呢？ 另外，如何运行在测试集上训练的模型？
我需要能够保存经过训练的模型以供以后使用...
【问题讨论】：
-
能否也给我们提供一个回调列表定义？
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你能说明错误发生在哪里吗？更多的痕迹
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并从 KerasClassifier 构造函数中删除回调列表。
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解决了，看我的编辑:)
标签： python machine-learning scikit-learn neural-network keras