elephas/worker.py
def train(self, data_iterator):
        """Train a keras model on a worker
        """
        optimizer = get_optimizer(self.master_optimizer)
        self.model = model_from_yaml(self.yaml, self.custom_objects)
        self.model.compile(optimizer=optimizer,
                           loss=self.master_loss, metrics=self.master_metrics)
        self.model.set_weights(self.parameters.value)

        feature_iterator, label_iterator = tee(data_iterator, 2)
        x_train = np.asarray([x for x, y in feature_iterator])
        y_train = np.asarray([y for x, y in label_iterator])

        # self.model.compile(optimizer=self.master_optimizer,
        #                    loss=self.master_loss, metrics=self.master_metrics)
        weights_before_training = self.model.get_weights()
        if x_train.shape[0] > self.train_config.get('batch_size'):
            self.model.fit(x_train, y_train, **self.train_config)
        weights_after_training = self.model.get_weights()
        deltas = subtract_params(
            weights_before_training, weights_after_training)
        yield deltas, weights_after_training
Train a keras model on a worker
        
Train a keras model on a worker
Train a keras model on a worker
def train(self, data_iterator):
        optimizer = get_optimizer(self.master_optimizer)
        self.model = model_from_yaml(self.yaml, self.custom_objects)
        self.model.compile(optimizer=optimizer,
                           loss=self.master_loss, metrics=self.master_metrics)
        self.model.set_weights(self.parameters.value)
        feature_iterator, label_iterator = tee(data_iterator, 2)
        x_train = np.asarray([x for x, y in feature_iterator])
        y_train = np.asarray([y for x, y in label_iterator])
        weights_before_training = self.model.get_weights()
        if x_train.shape[0] > self.train_config.get('batch_size'):
            self.model.fit(x_train, y_train, **self.train_config)
        weights_after_training = self.model.get_weights()
        deltas = subtract_params(
            weights_before_training, weights_after_training)
        yield deltas, weights_after_training
def train ( self , data_iterator ) : optimizer = get_optimizer ( self . master_optimizer ) self . model = model_from_yaml ( self . yaml , self . custom_objects ) self . model . compile ( optimizer = optimizer , loss = self . master_loss , metrics = self . master_metrics ) self . model . set_weights ( self . parameters . value ) feature_iterator , label_iterator = tee ( data_iterator , 2 ) x_train = np . asarray ( [ x for x , y in feature_iterator ] ) y_train = np . asarray ( [ y for x , y in label_iterator ] ) weights_before_training = self . model . get_weights ( ) if x_train . shape [ 0 ] > self . train_config . get ( 'batch_size' ) : self . model . fit ( x_train , y_train , ** self . train_config ) weights_after_training = self . model . get_weights ( ) deltas = subtract_params ( weights_before_training , weights_after_training ) yield deltas , weights_after_training
Train a keras model on a worker
Train a keras model on a worker
"""Train a keras model on a worker
        """ # self.model.compile(optimizer=self.master_optimizer, #                    loss=self.master_loss, metrics=self.master_metrics)
self
data_iterator