predict_2017_07_06_1.py 文件源码

def keras_common(train3, y, test3, v, z, num_splits, cname, build_model, seed = 1234, batch_size = 128):
    v[cname], z[cname] = 0, 0
    np.random.seed(seed)
    build_model().summary(line_length=120)
    model_path = '../data/working/' + cname + '_keras_model.h5'
    ss = model_selection.ShuffleSplit(n_splits=num_splits, random_state=11, test_size=1/num_splits)
    scores = list()
    for n, (itrain, ival) in enumerate(ss.split(train3, y)):
        xtrain, xval = train3[itrain], train3[ival]
        ytrain, yval = y[itrain], y[ival]
        model = build_model()
        model.fit(
                xtrain, ytrain,
                batch_size = batch_size,
                epochs = 10000,
                validation_data = (xval, yval),
                verbose = 0,
                callbacks = build_keras_fit_callbacks(model_path),
                shuffle = True
            )
        model.load_weights(model_path)
        p = model.predict(xval)
        v.loc[ival, cname] += pconvert(p).ravel()
        score = metrics.log_loss(y[ival], p)
        print(cname, 'fold %d: '%(n+1), score, now())
        scores.append(score)
        z[cname] += pconvert(model.predict(test3)).ravel()
        del model
        for i in range(3): gc.collect(i)
    os.remove(model_path)

    cv=np.array(scores)
    print(cv, cv.mean(), cv.std())
    z[cname] /= num_splits