问2-Dence神经网络精度优化

EN

from keras.models import Sequential
from keras.layers import Dense
from sklearn.cross_validation import train_test_split
import numpy

# fix random seed for reproducibility
seed = 7
numpy.random.seed(seed)
dataset = numpy.loadtxt("sorted_output.csv", delimiter=",")
# split into input (X) and output (Y) variables
X = dataset[:,0:21]
Y = dataset[:,21]
# split into 67% for train and 33% for test
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.33, random_state=seed)
# create model
model = Sequential()
model.add(Dense(12, input_dim=21, init='orthogonal', activation='relu'))
model.add(Dense(10, init='uniform', activation='relu'))
model.add(Dense(1, init='orthogonal', activation='sigmoid'))
# Compile model
model.compile(loss='binary_crossentropy', optimizer='sgd', metrics=['accuracy'])
# Fit the model
model.fit(X_train, y_train, validation_data=(X_test,y_test), nb_epoch=150, batch_size=10)