如何保存最佳的hyperopt优化的keras模型及其权重? [英] How to save the best hyperopt optimized keras models and its weights?

问题描述

我使用hyperopt优化了我的keras模型.现在，我们如何将最佳优化的keras模型及其权重保存到磁盘.

I optimized my keras model using hyperopt. Now how do we save the best optimized keras model and its weights to disk.

我的代码:

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.choice('units1', [64,1024]),
            'units2': hp.choice('units2', [64,1024]),

            'dropout1': hp.uniform('dropout1', .25,.75),
            'dropout2': hp.uniform('dropout2',  .25,.75),

            'batch_size' : hp.uniform('batch_size', 20,100),

            '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=100, trials=trials)
print 'best: '
print best

推荐答案

我不知道如何将某些变量发送到f_nn或另一个hyperopt目标专有性.但是我使用两种方法来完成相同的任务.
第一种方法是一些全局变量(因为它不清楚，所以不喜欢它)，第二种方法是将度量值保存到文件中，然后读取并与当前度量进行比较.在我看来，最后一种方法更好.

I don't know how to send some variable to f_nn or another hyperopt target explicilty. But I've use two approaches to do the same task.
First approach is some global variable (don't like it, because it's non-clear) and the second is to save the metric value to the file, then read and compare with a current metric. The last approach seems to me better.

def f_nn(params):
    ...
    # I omit a part of the code   
    pred_auc =model.predict_proba(X_val, batch_size = 128, verbose = 0)
    acc = roc_auc_score(y_val, pred_auc)

    try:
        with open("metric.txt") as f:
            min_acc = float(f.read().strip())  # read best metric,
    except FileNotFoundError:
            min_acc = acc  # else just use current value as the best

    if acc < min_acc:
         model.save("model.hd5")  # save best to disc and overwrite metric
         with open("metric.txt", "w") as f:
             f.write(str(acc))

    print('AUC:', acc)
    sys.stdout.flush() 
    return {'loss': -acc, 'status': STATUS_OK}

trials = Trials()
best = fmin(f_nn, space, algo=tpe.suggest, max_evals=100, trials=trials)
print 'best: '
print best

from keras.models import load_model
best_model = load_model("model.hd5")

这种方法有几个优点:您可以将指标和模型保持在一起，甚至可以将其应用于某些版本或数据版本控制系统-这样您就可以在将来恢复实验结果.

This approach has several advantages: you can keep metric and model together, and even apply to it some version or data version control system - so you can restore results of an experiment in the future.

修改
如果先前的运行中有一些指标，但可能会导致意外的行为，但是您不要删除它.因此，您可以采用该代码-在优化后删除指标或使用时间戳记等来区分实验者的数据.

Edit
It can cause an unexpected behaviour, if there's some metric from a previous run, but you don't delete it. So you can adopt the code - remove the metric after the optimization or use timestamp etc. to distinguish your experimets' data.

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