我想用keras建立非线性回归模型来预测+ ve连续变量。 对于以下模型，如何选择以下超参数？
代码
def dnn_reg():
model = Sequential()
#layer 1
model.add(Dense(40, input_dim=13, kernel_initializer='normal'))
model.add(Activation('tanh'))
model.add(Dropout(0.2))
#layer 2
model.add(Dense(30, kernel_initializer='normal'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Dropout(0.4))
#layer 3
model.add(Dense(5, kernel_initializer='normal'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Dropout(0.4))
model.add(Dense(1, kernel_initializer='normal'))
model.add(Activation('relu'))
# Compile model
model.compile(loss='mean_squared_error', optimizer='adam')
return model
我考虑过随机网格搜索但是想要使用hyperopt，我相信会更快。我最初使用https://github.com/maxpumperla/hyperas实现了调优。 Hyperas不使用最新版本的keras。我怀疑keras正在快速发展，维护者很难使其兼容。所以我认为直接使用hyperopt将是一个更好的选择。
PS：我是超级参数调整和hyperopt的贝叶斯优化的新手。
答案 0 :(得分：9)
我在Hyperas上取得了很大的成功。以下是我学会使其发挥作用的事情。
1）从终端运行它作为python脚本（而不是从Ipython笔记本） 2）确保您的代码中没有任何注释（Hyperas不喜欢评论！） 3）将数据和模型封装在一个函数中，如hyperas自述文件中所述。
以下是适合我的Hyperas脚本示例（按照上述说明）。
from __future__ import print_function
from hyperopt import Trials, STATUS_OK, tpe
from keras.datasets import mnist
from keras.layers.core import Dense, Dropout, Activation
from keras.models import Sequential
from keras.utils import np_utils
import numpy as np
from hyperas import optim
from keras.models import model_from_json
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.layers.convolutional import Convolution2D, MaxPooling2D
from keras.optimizers import SGD , Adam
import tensorflow as tf
from hyperas.distributions import choice, uniform, conditional
__author__ = 'JOnathan Hilgart'
def data():
"""
Data providing function:
This function is separated from model() so that hyperopt
won't reload data for each evaluation run.
"""
import numpy as np
x = np.load('training_x.npy')
y = np.load('training_y.npy')
x_train = x[:15000,:]
y_train = y[:15000,:]
x_test = x[15000:,:]
y_test = y[15000:,:]
return x_train, y_train, x_test, y_test
def model(x_train, y_train, x_test, y_test):
"""
Model providing function:
Create Keras model with double curly brackets dropped-in as needed.
Return value has to be a valid python dictionary with two customary keys:
- loss: Specify a numeric evaluation metric to be minimized
- status: Just use STATUS_OK and see hyperopt documentation if not feasible
The last one is optional, though recommended, namely:
- model: specify the model just created so that we can later use it again.
"""
model_mlp = Sequential()
model_mlp.add(Dense({{choice([32, 64,126, 256, 512, 1024])}},
activation='relu', input_shape= (2,)))
model_mlp.add(Dropout({{uniform(0, .5)}}))
model_mlp.add(Dense({{choice([32, 64, 126, 256, 512, 1024])}}))
model_mlp.add(Activation({{choice(['relu', 'sigmoid'])}}))
model_mlp.add(Dropout({{uniform(0, .5)}}))
model_mlp.add(Dense({{choice([32, 64, 126, 256, 512, 1024])}}))
model_mlp.add(Activation({{choice(['relu', 'sigmoid'])}}))
model_mlp.add(Dropout({{uniform(0, .5)}}))
model_mlp.add(Dense({{choice([32, 64, 126, 256, 512, 1024])}}))
model_mlp.add(Activation({{choice(['relu', 'sigmoid'])}}))
model_mlp.add(Dropout({{uniform(0, .5)}}))
model_mlp.add(Dense(9))
model_mlp.add(Activation({{choice(['softmax','linear'])}}))
model_mlp.compile(loss={{choice(['categorical_crossentropy','mse'])}}, metrics=['accuracy'],
optimizer={{choice(['rmsprop', 'adam', 'sgd'])}})
model_mlp.fit(x_train, y_train,
batch_size={{choice([16, 32, 64, 128])}},
epochs=50,
verbose=2,
validation_data=(x_test, y_test))
score, acc = model_mlp.evaluate(x_test, y_test, verbose=0)
print('Test accuracy:', acc)
return {'loss': -acc, 'status': STATUS_OK, 'model': model_mlp}
enter code here
if __name__ == '__main__':
import gc; gc.collect()
with K.get_session(): ## TF session
best_run, best_model = optim.minimize(model=model,
data=data,
algo=tpe.suggest,
max_evals=2,
trials=Trials())
X_train, Y_train, X_test, Y_test = data()
print("Evalutation of best performing model:")
print(best_model.evaluate(X_test, Y_test))
print("Best performing model chosen hyper-parameters:")
print(best_run)
你可以通过以下方式强制python进行del session：
del session
或者如果您使用keras，则无法获取会话实例，您可以在代码末尾运行以下代码：
import gc; gc.collect()
答案 1 :(得分：3)
这也可以是另一种方法：
from hyperopt import fmin, tpe, hp, STATUS_OK, Trials
from sklearn.metrics import roc_auc_score
import sys
X = []
y = []
X_val = []
y_val = []
space = {'choice': hp.choice('num_layers',
[ {'layers':'two', },
{'layers':'three',
'units3': hp.uniform('units3', 64,1024),
'dropout3': hp.uniform('dropout3', .25,.75)}
]),
'units1': hp.uniform('units1', 64,1024),
'units2': hp.uniform('units2', 64,1024),
'dropout1': hp.uniform('dropout1', .25,.75),
'dropout2': hp.uniform('dropout2', .25,.75),
'batch_size' : hp.uniform('batch_size', 28,128),
'nb_epochs' : 100,
'optimizer': hp.choice('optimizer',['adadelta','adam','rmsprop']),
'activation': 'relu'
}
def f_nn(params):
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.optimizers import Adadelta, Adam, rmsprop
print ('Params testing: ', params)
model = Sequential()
model.add(Dense(output_dim=params['units1'], input_dim = X.shape[1]))
model.add(Activation(params['activation']))
model.add(Dropout(params['dropout1']))
model.add(Dense(output_dim=params['units2'], init = "glorot_uniform"))
model.add(Activation(params['activation']))
model.add(Dropout(params['dropout2']))
if params['choice']['layers']== 'three':
model.add(Dense(output_dim=params['choice']['units3'], init = "glorot_uniform"))
model.add(Activation(params['activation']))
model.add(Dropout(params['choice']['dropout3']))
model.add(Dense(1))
model.add(Activation('sigmoid'))
model.compile(loss='binary_crossentropy', optimizer=params['optimizer'])
model.fit(X, y, nb_epoch=params['nb_epochs'], batch_size=params['batch_size'], verbose = 0)
pred_auc =model.predict_proba(X_val, batch_size = 128, verbose = 0)
acc = roc_auc_score(y_val, pred_auc)
print('AUC:', acc)
sys.stdout.flush()
return {'loss': -acc, 'status': STATUS_OK}
trials = Trials()
best = fmin(f_nn, space, algo=tpe.suggest, max_evals=50, trials=trials)
print 'best: '
print best