定义模型
Dense类定义完全连接的层
from keras.models import Sequential
from keras.layers import Dense
model = Sequential()
model.add(Dense(unit=12,input_dim=8,activation='relu'))
model.add(Dense(8,activation='relu'))
model.add(Dense(1,activation='sigmoid'))
其中unit(层中神经元个数)、init(初始化方法)、activation(激活函数)
init为将网络权重初始化为均匀分布的小随机数，默认介于0和0.05之间的随机数。也可选择高斯分布产生小随机数。
activation包括ReLu（现研究表明可得更好性能）、sigmoid、tanh（首选）。
二分类的输出层通常采用sigmoid作为激活函数，单层神经网络中使用sgn，多分类使用softmax 。
回归问题的输出层不设置激活函数。
编译模型
指定训练模型时所需的一些属性。
model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])
其中loss(评估一组权重的损失函数)、optimizer(搜索网络不同权重的优化器)
loss对于二进制分类问题的对数损失函数被定义为二进制交叉熵（'binary_crossentropy'）。
optimizer优化器选取梯度下降法（'adam'），默认值。
训练模型
model.fit(x=x , y=Y , epochs=150 , batch_size=10)
其中epochs(固定迭代次数)、batch_size(执行神经网络中的权重更新的每个批次中所用实例的个数)
评估模型
scores = model.evaluate(x=x , y=Y)
print('%s: %.2f%%' % (model.metrics_names[1],scores[1]*100))
evaluate()函数用来评估模型的精确度
深度学习模型调参
from sklearn.model_selection import GridSearchCV
from keras.wrappers.scikit_learn import KerasClassifier
def create_model(optimizer='adam',init='glorot_uniform'):
model = Sequential()
model.add(Dense(12,kernel_initializer=init,input_dim=8,activation='relu'))
model.add(Dense(8,kernel_initializer=init,activation='relu'))
model.add(Dense(1,kernel_initializer=init,activation='sigmoid'))
model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])
return model
seed = 7
np.random.seed(seed)
dataset = np.loadtxt('C:\\Users\\ThinkPad\\Desktop\\Keras实践\\多层感知器\\pima-indians-diabetes.csv',delimiter=',')
x = dataset[:,0:8]
y = dataset[:,8]
model = KerasClassifier(build_fn=create_model,verbose=0)
param_grid = {}
param_grid['optimizer'] = ['rmsprop','adam']
param_grid['init'] = ['glorot_uniform','normal','uniform']
param_grid['epochs'] = [50,100,150,200]
param_grid['batch_size'] = [5,10,20]
grid = GridSearchCV(estimator=model,param_grid=param_grid)
results = grid.fit(x,y)
print('Best: %f using %s' % (results.best_score_, results.best_params_))
means = results.cv_results_['mean_test_scores']
stds = results.cv_results_['std_test_scores']
params = results.cv_results_['params']
for mean,std,param in zip(means,stds,params):
print('%f (%f) with: %r' % (mean,std,param))
Pipeline框架
便于在交叉验证的每一个折中执行数据标准化处理。
首先标准化数据集，然后创建和评估基线神经网络模型。
steps = []
steps.append(('standardize',StandardScaler()))
steps.append(('mlp',model))
pipeline = Pipeline(steps)
kfold = KFold(n_splits=10,shuffle=True,random_state=seed)
results = cross_val_score(pipeline,x,y,cv=kfold)
模型增量更新
为了保证模型的时效性，需要定期对模型进行更新，这个时间间隔通常是3~6个月，甚至1~2个月。
在数据量非常大是，若每次采用全部数据取重新训练模型，则时间开销非常大，因此可以采用增量更新模型的方式对模型进行训练。
对于时间序的预测，增量更新相当于默认给最新的数据增加了权重，提高精确度。
from sklearn.model_selection import train_test_split
x_train,x_increment,Y_train,Y_increment = train_test_split(x,Y,test_size=0.2,random_state=seed)
······
new_model.compile(loss='categorical_crossentropy',optimizer='rmsprop',metrics=['accuracy'])
Y_increment_labels =to_categorical(Y_increment,num_classes=3)
new_model.fit(x_increment,Y_increment_labels,epochs=10,batch_size=5,verbose=2)
scores = new_model.evaluate(x_increment,Y_increment_labels,verbose=0)
print('Incremrnt %s: %.2f%%' % (model.metrics_names[1],scores[1]*100))
神经网络的检查点
from keras.callbacks import ModelCheckpoint
filepath = 'weights-improvment-{epoch:02d}-{val_acc:.2f}.h5'
checkpoint = ModelCheckpoint(filepath=filepath,monitor='val_acc',verbose=1,save_best_only=False,mode='max')
''''''
#自动保存最优模型
filepath = 'weights.best.h5'
checkpoint = ModelCheckpoint(filepath=filepath,monitor='val_acc',verbose=1,save_best_only=True,mode='max')
''''''
callback_list = [checkpoint]
model.fit(x,Y_labels,validation_split=0.2,epochs=200,batch_size=5,verbose=0,callbacks=callback_list)
从检查点导入模型
def load_model(optimizer='rmsprop',init='glorot_uniform'):
model = Sequential() model.add(Dense(units=4,activation='relu',input_dim=4,kernel_initializer=init))
model.add(Dense(units=6,activation='relu',kernel_initializer=init)) model.add(Dense(units=3,activation='softmax',kernel_initializer=init))
filepath = 'weights.best.h5'
model.load_weights(filepath=filepath) model.compile(loss='categorical_crossentropy',optimizer=optimizer,metrics=['accuracy'])
return model
#导入模型
model = load_model()
scores = model.evaluate(x,Y_labels,verbose=0)
print('%s: %.2f%%' % (model.metrics_names[1],scores[1]*100))
Drop与学习率衰减
drop针对神经网络模型的正则化方法。即在训练过程中，随机地忽略部分神经元。
输入层使用Dropout
dropout率设置为20%，这意味着每个更新周期中20%的输入将被随机排除。
def create_model(optimizer='rmsprop',init='glorot_uniform'):
model = Sequential()
model.add(Dropout(rate=0.2,input_shape=(4,)))
model.add(Dense(units=4,activation='relu',input_dim=4,kernel_initializer=init))
model.add(Dense(units=6,activation='relu',kernel_initializer=init))
model.add(Dense(units=3,activation='softmax',kernel_initializer=init))
sgd = SGD(lr=0.01,momentum=0.8,decay=0.0,nesterov=False) #定义Dropout
model.compile(loss='categorical_crossentropy',optimizer=optimizer,metrics=['accuracy'])
return model
隐含层使用Dropout
def create_model(init='glorot_uniform'):
model = Sequential()
model.add(Dense(units=4,activation='relu',input_dim=4,kernel_initializer=init,kernel_constraint=maxnorm(3))) #约束权重最大限度不超过3
model.add(Dropout(rate=0.2))
model.add(Dense(units=6,activation='relu',input_dim=4,kernel_initializer=init,kernel_constraint=maxnorm(3)))
model.add(Dropout(rate=0.2))
model.add(Dense(units=3,activation='softmax',kernel_initializer=init))
sgd = SGD(lr=0.01,momentum=0.8,decay=0.0,nesterov=False)
model.compile(loss='categorical_crossentropy',optimizer=sgd,metrics=['accuracy'])
return model
学习率衰减
# 学习率线性衰减
def create_model(init='glorot_uniform'):
model = Sequential()
model.add(Dense(units=4,activation='relu',input_dim=4,kernel_initializer=init))
model.add(Dense(units=6,activation='relu',kernel_initializer=init))
model.add(Dense(units=3,activation='softmax',kernel_initializer=init))
learningRate = 0.1
momentum = 0.9
decay_rate = 0.005
sgd = SGD(lr=learningRate,momentum=momentum,decay=decay_rate,nesterov=False)
model.compile(loss='categorical_crossentropy',optimizer=sgd,metrics=['accuracy'])
return model
epochs = 200
model = KerasClassifier(build_fn = create_model,epochs=epochs,batch_size=5,verbose=1)
model.fit(x,Y)
# 学习率指数衰减
def step_decay(epoch):
init_lrate = 0.1
drop = 0.5
epochs_drop = 10
lrate = init_lrate*pow(drop,floor(1+epoch)/epochs_drop)
return lrate
def create_model(init='glorot_uniform'):
model = Sequential()
model.add(Dense(units=4,activation='relu',input_dim=4,kernel_initializer=init))
model.add(Dense(units=6,activation='relu',kernel_initializer=init))
model.add(Dense(units=3,activation='softmax',kernel_initializer=init))
learningRate = 0.1
momentum = 0.9
decay_rate = 0.0
sgd = SGD(lr=learningRate,momentum=momentum,decay=decay_rate,nesterov=False)
model.compile(loss='categorical_crossentropy',optimizer=sgd,metrics=['accuracy'])
return model
lrate = LearningRateScheduler(step_decay)
epochs = 200
model = KerasClassifier(build_fn = create_model,epochs=epochs,batch_size=5,verbose=1,callbacks=[lrate])
model.fit(x,Y)
