Created
February 29, 2020 17:04
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| class PearsonrRankCallback(Callback): | |
| def __init__(self, loader, filepaths, name=None, verbose=False, | |
| sim_model=None, savemodel=None, savepath=None): | |
| self.savemodel = savemodel | |
| self.savepath = savepath | |
| self.sim_model = sim_model | |
| self.loader = loader | |
| self.verbose = verbose | |
| self.name = name | |
| self.samples, self.labels = self.load_datasets(filepaths) | |
| self.best = 0 | |
| super(PearsonrRankCallback, self).__init__() | |
| def on_epoch_begin(self, epoch, logs=None): | |
| pred = self.sim_model.predict(self.samples, batch_size=128, | |
| verbose=self.verbose).reshape(-1,) | |
| coef, p = pearsonr(self.labels, pred) | |
| coef = np.round(coef, 4) | |
| if coef > self.best: | |
| self.best = coef | |
| print("*** New best: {} = {}".format(self.name, coef)) | |
| if self.savemodel and self.savepath: | |
| self.savemodel.save_weights(self.savepath) | |
| else: | |
| print("mean: {}".format(coef)) |
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