Python tensorflow.keras.callbacks.EarlyStopping() Examples

def train(self, batch_size=512, epochs=20):
        model = self.build_model()
        # early_stop配合checkpoint使用，可以得到val_loss最小的模型
        early_stop = EarlyStopping(patience=3, verbose=1)
        checkpoint = ModelCheckpoint(os.path.join(self.model_path, 'weights.{epoch:03d}-{val_loss:.3f}.h5'),
                                     verbose=1,
                                     monitor='val_loss',
                                     save_best_only=True)
        history = model.fit(self.x_train,
                            self.y_train,
                            batch_size=batch_size,
                            epochs=epochs,
                            verbose=1,
                            callbacks=[checkpoint, early_stop],
                            validation_data=(self.x_test, self.y_test))
        plot(history)
        return model 

def main():
    model = create_model(trainable=TRAINABLE)
    model.summary()

    if TRAINABLE:
        model.load_weights(WEIGHTS)

    train_datagen = DataGenerator(TRAIN_CSV)
    validation_datagen = Validation(generator=DataGenerator(VALIDATION_CSV))

    optimizer = Adam(lr=1e-4, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False)
    model.compile(loss=loss, optimizer=optimizer, metrics=[])
    
    checkpoint = ModelCheckpoint("model-{val_dice:.2f}.h5", monitor="val_dice", verbose=1, save_best_only=True,
                                 save_weights_only=True, mode="max")
    stop = EarlyStopping(monitor="val_dice", patience=PATIENCE, mode="max")
    reduce_lr = ReduceLROnPlateau(monitor="val_dice", factor=0.2, patience=5, min_lr=1e-6, verbose=1, mode="max")

    model.fit_generator(generator=train_datagen,
                        epochs=EPOCHS,
                        callbacks=[validation_datagen, checkpoint, reduce_lr, stop],
                        workers=THREADS,
                        use_multiprocessing=MULTI_PROCESSING,
                        shuffle=True,
                        verbose=1) 

def main():
    model = create_model()

    train_datagen = DataGenerator(TRAIN_CSV)
    validation_datagen = Validation(generator=DataGenerator(VALIDATION_CSV))

    optimizer = Adam(lr=1e-3, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False)
    model.compile(loss={"coords" : log_mse, "classes" : focal_loss()}, loss_weights={"coords" : 1, "classes" : 1}, optimizer=optimizer, metrics=[])
    checkpoint = ModelCheckpoint("model-{val_iou:.2f}.h5", monitor="val_iou", verbose=1, save_best_only=True,
                                 save_weights_only=True, mode="max")
    stop = EarlyStopping(monitor="val_iou", patience=PATIENCE, mode="max")
    reduce_lr = ReduceLROnPlateau(monitor="val_iou", factor=0.2, patience=10, min_lr=1e-7, verbose=1, mode="max")

    model.summary()

    model.fit_generator(generator=train_datagen,
                        epochs=EPOCHS,
                        callbacks=[validation_datagen, checkpoint, reduce_lr, stop],
                        workers=THREADS,
                        use_multiprocessing=MULTI_PROCESSING,
                        shuffle=True,
                        verbose=1) 

def main():
    model = create_model()
    model.summary()

    train_datagen = DataGenerator(TRAIN_CSV)
    validation_datagen = Validation(generator=DataGenerator(VALIDATION_CSV))

    model.compile(loss="mean_squared_error", optimizer="adam", metrics=[])

    checkpoint = ModelCheckpoint("model-{val_iou:.2f}.h5", monitor="val_iou", verbose=1, save_best_only=True,
                                 save_weights_only=True, mode="max")
    stop = EarlyStopping(monitor="val_iou", patience=PATIENCE, mode="max")
    reduce_lr = ReduceLROnPlateau(monitor="val_iou", factor=0.2, patience=10, min_lr=1e-7, verbose=1, mode="max")

    model.fit_generator(generator=train_datagen,
                        epochs=EPOCHS,
                        callbacks=[validation_datagen, checkpoint, reduce_lr, stop],
                        workers=THREADS,
                        use_multiprocessing=MULTI_PROCESSING,
                        shuffle=True,
                        verbose=1) 

def _callbacks(
            self,
            *,
            es_params={
                'patience': 20,
                'monitor': 'val_loss'
            },
            lr_params={
                'monitor': 'val_loss',
                'patience': 4,
                'factor': 0.2
            }
    ):
        early_stopping = EarlyStopping(**es_params)
        learning_rate_reduction = ReduceLROnPlateau(**lr_params)
        return {
            'forecaster': [],
            'embedder': [],
            'combined': [
                early_stopping, learning_rate_reduction
            ]
        } 

def train(self, weights_only=True, call_back=False):
        model = self._build_model()

        if call_back:
            early_stopping = EarlyStopping(monitor='val_loss', patience=30)
            stamp = 'lstm_%d' % self.n_hidden
            checkpoint_dir = os.path.join(
                self.model_path, 'checkpoints/' + str(int(time.time())) + '/')
            if not os.path.exists(checkpoint_dir):
                os.makedirs(checkpoint_dir)

            bst_model_path = checkpoint_dir + stamp + '.h5'
            if weights_only:
                model_checkpoint = ModelCheckpoint(
                    bst_model_path, save_best_only=True, save_weights_only=True)
            else:
                model_checkpoint = ModelCheckpoint(
                    bst_model_path, save_best_only=True)
            tensor_board = TensorBoard(
                log_dir=checkpoint_dir + "logs/{}".format(time.time()))
            callbacks = [early_stopping, model_checkpoint, tensor_board]
        else:
            callbacks = None
        model_trained = model.fit([self.x_train['left'], self.x_train['right']],
                                  self.y_train,
                                  batch_size=self.batch_size,
                                  epochs=self.epochs,
                                  validation_data=([self.x_val['left'], self.x_val['right']], self.y_val),
                                  verbose=1,
                                  callbacks=callbacks)
        if weights_only and not call_back:
            model.save_weights(os.path.join(self.model_path, 'weights_only.h5'))
        elif not weights_only and not call_back:
            model.save(os.path.join(self.model_path, 'model.h5'))
        self._save_config()
        plot(model_trained)
        return model 

def main():
    model = create_model(trainable=TRAINABLE)
    model.summary()

    if TRAINABLE:
        model.load_weights(WEIGHTS)

    train_datagen = DataGenerator(TRAIN_CSV)

    val_generator = DataGenerator(VALIDATION_CSV, rnd_rescale=False, rnd_multiply=False, rnd_crop=False, rnd_flip=False, debug=False)
    validation_datagen = Validation(generator=val_generator)

    learning_rate = LEARNING_RATE
    if TRAINABLE:
        learning_rate /= 10

    optimizer = SGD(lr=learning_rate, decay=LR_DECAY, momentum=0.9, nesterov=False)
    model.compile(loss=detection_loss(), optimizer=optimizer, metrics=[])

    checkpoint = ModelCheckpoint("model-{val_iou:.2f}.h5", monitor="val_iou", verbose=1, save_best_only=True,
                                 save_weights_only=True, mode="max")
    stop = EarlyStopping(monitor="val_iou", patience=PATIENCE, mode="max")
    reduce_lr = ReduceLROnPlateau(monitor="val_iou", factor=0.6, patience=5, min_lr=1e-6, verbose=1, mode="max")

    model.fit_generator(generator=train_datagen,
                        epochs=EPOCHS,
                        callbacks=[validation_datagen, checkpoint, reduce_lr, stop],
                        workers=THREADS,
                        use_multiprocessing=MULTITHREADING,
                        shuffle=True,
                        verbose=1) 

def fit_model_softmax(dsm: DeepSpeakerModel, kx_train, ky_train, kx_test, ky_test,
                      batch_size=BATCH_SIZE, max_epochs=1000, initial_epoch=0):
    checkpoint_name = dsm.m.name + '_checkpoint'
    checkpoint_filename = os.path.join(CHECKPOINTS_SOFTMAX_DIR, checkpoint_name + '_{epoch}.h5')
    checkpoint = ModelCheckpoint(monitor='val_accuracy', filepath=checkpoint_filename, save_best_only=True)

    # if the accuracy does not increase by 0.1% over 20 epochs, we stop the training.
    early_stopping = EarlyStopping(monitor='val_accuracy', min_delta=0.001, patience=20, verbose=1, mode='max')

    # if the accuracy does not increase over 10 epochs, we reduce the learning rate by half.
    reduce_lr = ReduceLROnPlateau(monitor='val_accuracy', factor=0.5, patience=10, min_lr=0.0001, verbose=1)

    max_len_train = len(kx_train) - len(kx_train) % batch_size
    kx_train = kx_train[0:max_len_train]
    ky_train = ky_train[0:max_len_train]
    max_len_test = len(kx_test) - len(kx_test) % batch_size
    kx_test = kx_test[0:max_len_test]
    ky_test = ky_test[0:max_len_test]

    dsm.m.fit(x=kx_train,
              y=ky_train,
              batch_size=batch_size,
              epochs=initial_epoch + max_epochs,
              initial_epoch=initial_epoch,
              verbose=1,
              shuffle=True,
              validation_data=(kx_test, ky_test),
              callbacks=[early_stopping, reduce_lr, checkpoint]) 

def train():
    with open('config.json', 'r') as f:
        cfg = json.load(f)

    save_dir = cfg['save_dir']
    shape = (int(cfg['height']), int(cfg['width']), 3)
    n_class = int(cfg['class_number'])
    batch = int(cfg['batch'])

    if not os.path.exists(save_dir):
        os.mkdir(save_dir)

    if cfg['model'] == 'large':
        from mobilenet_v3_large import MobileNetV3_Large
        model = MobileNetV3_Large(shape, n_class).build()
    if cfg['model'] == 'small':
        from mobilenet_v3_small import MobileNetV3_Small
        model = MobileNetV3_Small(shape, n_class).build()

    opt = Adam(lr=float(cfg['learning_rate']))
    earlystop = EarlyStopping(monitor='val_acc', patience=5, verbose=0, mode='auto')
    model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy'])

    train_generator, validation_generator, count1, count2 = generate(batch, shape[:2], cfg['train_dir'], cfg['eval_dir'])

    hist = model.fit_generator(
        train_generator,
        validation_data=validation_generator,
        steps_per_epoch=count1 // batch,
        validation_steps=count2 // batch,
        epochs=cfg['epochs'],
        callbacks=[earlystop])

    df = pd.DataFrame.from_dict(hist.history)
    df.to_csv(os.path.join(save_dir, 'hist.csv'), encoding='utf-8', index=False)
    model.save_weights(os.path.join(save_dir, '{}_weights.h5'.format(cfg['model']))) 

def train(self, training_data, cfg, **kwargs):
        classifier_model = eval("clf." + self.classifier_model)

        epochs = self.component_config.get('epochs')
        batch_size = self.component_config.get('batch_size')
        validation_split = self.component_config.get('validation_split')
        patience = self.component_config.get('patience')
        factor = self.component_config.get('factor')
        verbose = self.component_config.get('verbose')

        X, Y = [], []
        for msg in training_data.intent_examples:
            X.append(self.tokenizer.tokenize(msg.text))
            Y.append(msg.get('intent'))

        train_x, validate_x, train_y, validate_y = train_test_split( X, Y, test_size=validation_split, random_state=100)

        self.bert_embedding.processor.add_bos_eos = False

        self.model = classifier_model(self.bert_embedding)

        checkpoint = ModelCheckpoint(
            'intent_weights.h5',
            monitor='val_loss',
            save_best_only=True,
            save_weights_only=False,
            verbose=verbose)
        early_stopping = EarlyStopping(
            monitor='val_loss',
            patience=patience)
        reduce_lr = ReduceLROnPlateau(
            monitor='val_loss',
            factor=factor,
            patience=patience,
            verbose=verbose)

        self.model.fit(
            train_x,
            train_y,
            validate_x,
            validate_y,
            epochs=epochs,
            batch_size=batch_size,
            callbacks=[checkpoint, early_stopping, reduce_lr]
        ) 

def train(self, training_data, cfg, **kwargs):
        labeling_model = eval("labeling." + self.labeling_model)

        epochs = self.component_config.get('epochs')
        batch_size = self.component_config.get('batch_size')
        validation_split = self.component_config.get('validation_split')
        patience = self.component_config.get('patience')
        factor = self.component_config.get('factor')
        verbose = self.component_config.get('verbose')

        filtered_entity_examples = self.filter_trainable_entities(training_data.training_examples)

        X, Y = self._create_dataset(filtered_entity_examples)

        train_x, validate_x, train_y, validate_y = train_test_split( X, Y, test_size=validation_split, random_state=100)

        self.model = labeling_model(self.bert_embedding)

        checkpoint = ModelCheckpoint(
            'entity_weights.h5',
            monitor='val_loss',
            save_best_only=True,
            save_weights_only=False,
            verbose=verbose)
        early_stopping = EarlyStopping(
            monitor='val_loss',
            patience=patience)
        reduce_lr = ReduceLROnPlateau(
            monitor='val_loss',
            factor=factor,
            patience=patience,
            verbose=verbose)

        self.model.fit(
            train_x,
            train_y,
            validate_x,
            validate_y,
            epochs=epochs,
            batch_size=batch_size,
            callbacks=[checkpoint, early_stopping, reduce_lr]
        )