keras之dropout

1，在input第一層之間加droppout

2.在hiddenlayer中加

W_constraint=maxnorm(3)是原始論文裏面建議的認爲係數最好不要超過3

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.constraints import maxnorm
from keras.optimizers import SGD
from sklearn.cross_validation import cross_val_score
from sklearn.preprocessing import LabelEncoder
from sklearn.cross_validation import StratifiedKFold
from sklearn.preprocessing import StandardScaler
from sklearn.grid_search import GridSearchCV
from sklearn.pipeline import Pipeline
# fix random seed for reproducibility
seed = 7
numpy.random.seed(seed)
# load dataset
dataframe = pandas.read_csv("../data/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)


# baseline
def create_baseline():
	# create model
	model = Sequential()
	model.add(Dense(60, input_dim=60, init='normal', activation='relu'))
	model.add(Dense(30, init='normal', activation='relu'))
	model.add(Dense(1, init='normal', activation='sigmoid'))
	# Compile model
	sgd = SGD(lr=0.01, momentum=0.8, decay=0.0, nesterov=False)
	model.compile(loss='binary_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)))
pipeline = Pipeline(estimators)
kfold = StratifiedKFold(y=encoded_Y, n_folds=10, shuffle=True, random_state=seed)
results = cross_val_score(pipeline, X, encoded_Y, cv=kfold)
print("Accuracy: %.2f%% (%.2f%%)" % (results.mean()*100, results.std()*100))
# Accuracy: 82.68% (3.90%)


# dropout in the input layer with weight constraint
def create_model1():
	# create model
	model = Sequential()
	model.add(Dropout(0.2, input_shape=(60,)))
	model.add(Dense(60, init='normal', activation='relu', W_constraint=maxnorm(3)))
	model.add(Dense(30, init='normal', activation='relu', W_constraint=maxnorm(3)))
	model.add(Dense(1, init='normal', activation='sigmoid'))
	# Compile model
	sgd = SGD(lr=0.1, momentum=0.9, decay=0.0, nesterov=False)
	model.compile(loss='binary_crossentropy', optimizer=sgd, metrics=['accuracy'])
	return model

numpy.random.seed(seed)
estimators = []
estimators.append(('standardize', StandardScaler()))
estimators.append(('mlp', KerasClassifier(build_fn=create_model1, nb_epoch=300, batch_size=16, verbose=0)))
pipeline = Pipeline(estimators)
kfold = StratifiedKFold(y=encoded_Y, n_folds=10, shuffle=True, random_state=seed)
results = cross_val_score(pipeline, X, encoded_Y, cv=kfold)
print("Accuracy: %.2f%% (%.2f%%)" % (results.mean()*100, results.std()*100))
# Accuracy: 86.04% (6.33%)


# dropout in hidden layers with weight constraint
def create_model2():
	# create model
	model = Sequential()
	model.add(Dense(60, input_dim=60, init='normal', activation='relu', W_constraint=maxnorm(3)))
	model.add(Dropout(0.2))
	model.add(Dense(30, init='normal', activation='relu', W_constraint=maxnorm(3)))
	model.add(Dropout(0.2))
	model.add(Dense(1, init='normal', activation='sigmoid'))
	# Compile model
	sgd = SGD(lr=0.1, momentum=0.9, decay=0.0, nesterov=False)
	model.compile(loss='binary_crossentropy', optimizer=sgd, metrics=['accuracy'])
	return model

numpy.random.seed(seed)
estimators = []
estimators.append(('standardize', StandardScaler()))
estimators.append(('mlp', KerasClassifier(build_fn=create_model2, nb_epoch=300, batch_size=16, verbose=0)))
pipeline = Pipeline(estimators)
kfold = StratifiedKFold(y=encoded_Y, n_folds=10, shuffle=True, random_state=seed)
results = cross_val_score(pipeline, X, encoded_Y, cv=kfold)
print("Accuracy: %.2f%% (%.2f%%)" % (results.mean()*100, results.std()*100))
# Accuracy: 82.16% (6.16%)

幾個建議

1.dropout在0.2~0.5之間，一般0.2就挺好的

2.input也可以用，不止是hiddenlayer

3. Use a larger network. You are likely to get better performance when dropout is used on a larger network, giving the model more of an opportunity to learn independent representations.
4. Use a large learning rate with decay and a large momentum. Increase your learning rate by a factor of 10 to 100 and use a high momentum value of 0.9 or 0.99.
5. Constrain the size of network weights. A large learning rate can result in very large network weights. Imposing a constraint on the size of network weights such as max-norm regularization with a size of 4 or 5 has been shown to improve results