train_catastrophe_model_human.py 文件源码

def fit(self, X_train, y_train, X_valid, y_valid, X_test, y_test, steps=400):
        tf.global_variables_initializer().run()
        redirect=FDRedirector(STDERR)
        for i in range(steps):
            redirect.start()
            feed_dict = {self.labels:y_train}
            for key, tensor in self.features.items():
                feed_dict[tensor] = X_train[key]
            predictions, loss = sess.run([self.prediction, self.train_op], feed_dict=feed_dict)
            if i % 10 == 0:
                print("step:{} loss:{:.3g} np.std(predictions):{:.3g}".format(i, loss, np.std(predictions)))
                self.threshold = float(min(self.threshold_from_data(X_valid, y_valid), self.threshold_from_data(X_train, y_train)))
                tf.get_collection_ref("threshold")[0] = self.threshold
                self.print_metrics(X_train, y_train, "Training")
                self.print_metrics(X_valid, y_valid, "Validation")
            errors = redirect.stop()
            if errors:
                print(errors)
        self.print_metrics(X_test, y_test, "Test")