elephas/worker.py
def train(self, data_iterator):
        """Train a keras model on a worker and send asynchronous updates
        to parameter server
        """
        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])

        if x_train.size == 0:
            return

        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)

        epochs = self.train_config['epochs']
        batch_size = self.train_config.get('batch_size')
        nb_train_sample = x_train.shape[0]
        nb_batch = int(np.ceil(nb_train_sample / float(batch_size)))
        index_array = np.arange(nb_train_sample)
        batches = [
            (i * batch_size, min(nb_train_sample, (i + 1) * batch_size))
            for i in range(0, nb_batch)
        ]

        if self.frequency == 'epoch':
            for epoch in range(epochs):
                weights_before_training = self.client.get_parameters()
                self.model.set_weights(weights_before_training)
                self.train_config['epochs'] = 1
                if x_train.shape[0] > batch_size:
                    self.model.fit(x_train, y_train, **self.train_config)
                self.train_config['epochs'] = epochs
                weights_after_training = self.model.get_weights()
                deltas = subtract_params(
                    weights_before_training, weights_after_training)
                self.client.update_parameters(deltas)
        elif self.frequency == 'batch':
            for epoch in range(epochs):
                if x_train.shape[0] > batch_size:
                    for (batch_start, batch_end) in batches:
                        weights_before_training = self.client.get_parameters()
                        self.model.set_weights(weights_before_training)
                        batch_ids = index_array[batch_start:batch_end]
                        x = slice_arrays(x_train, batch_ids)
                        y = slice_arrays(y_train, batch_ids)
                        self.model.train_on_batch(x, y)
                        weights_after_training = self.model.get_weights()
                        deltas = subtract_params(
                            weights_before_training, weights_after_training)
                        self.client.update_parameters(deltas)
        else:
            raise ValueError(
                'frequency parameter can be `epoch` or `batch, got {}'.format(self.frequency))
        yield []
Train a keras model on a worker and send asynchronous updates
        to parameter server
        
Train a keras model on a worker and send asynchronous updates
to parameter server
Train a keras model on a worker and send asynchronous updates to parameter server
def train(self, data_iterator):
        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])
        if x_train.size == 0:
            return
        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)
        epochs = self.train_config['epochs']
        batch_size = self.train_config.get('batch_size')
        nb_train_sample = x_train.shape[0]
        nb_batch = int(np.ceil(nb_train_sample / float(batch_size)))
        index_array = np.arange(nb_train_sample)
        batches = [
            (i * batch_size, min(nb_train_sample, (i + 1) * batch_size))
            for i in range(0, nb_batch)
        ]
        if self.frequency == 'epoch':
            for epoch in range(epochs):
                weights_before_training = self.client.get_parameters()
                self.model.set_weights(weights_before_training)
                self.train_config['epochs'] = 1
                if x_train.shape[0] > batch_size:
                    self.model.fit(x_train, y_train, **self.train_config)
                self.train_config['epochs'] = epochs
                weights_after_training = self.model.get_weights()
                deltas = subtract_params(
                    weights_before_training, weights_after_training)
                self.client.update_parameters(deltas)
        elif self.frequency == 'batch':
            for epoch in range(epochs):
                if x_train.shape[0] > batch_size:
                    for (batch_start, batch_end) in batches:
                        weights_before_training = self.client.get_parameters()
                        self.model.set_weights(weights_before_training)
                        batch_ids = index_array[batch_start:batch_end]
                        x = slice_arrays(x_train, batch_ids)
                        y = slice_arrays(y_train, batch_ids)
                        self.model.train_on_batch(x, y)
                        weights_after_training = self.model.get_weights()
                        deltas = subtract_params(
                            weights_before_training, weights_after_training)
                        self.client.update_parameters(deltas)
        else:
            raise ValueError(
                'frequency parameter can be `epoch` or `batch, got {}'.format(self.frequency))
        yield []
def train ( self , data_iterator ) : 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 ] ) if x_train . size == 0 : return 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 ) epochs = self . train_config [ 'epochs' ] batch_size = self . train_config . get ( 'batch_size' ) nb_train_sample = x_train . shape [ 0 ] nb_batch = int ( np . ceil ( nb_train_sample / float ( batch_size ) ) ) index_array = np . arange ( nb_train_sample ) batches = [ ( i * batch_size , min ( nb_train_sample , ( i + 1 ) * batch_size ) ) for i in range ( 0 , nb_batch ) ] if self . frequency == 'epoch' : for epoch in range ( epochs ) : weights_before_training = self . client . get_parameters ( ) self . model . set_weights ( weights_before_training ) self . train_config [ 'epochs' ] = 1 if x_train . shape [ 0 ] > batch_size : self . model . fit ( x_train , y_train , ** self . train_config ) self . train_config [ 'epochs' ] = epochs weights_after_training = self . model . get_weights ( ) deltas = subtract_params ( weights_before_training , weights_after_training ) self . client . update_parameters ( deltas ) elif self . frequency == 'batch' : for epoch in range ( epochs ) : if x_train . shape [ 0 ] > batch_size : for ( batch_start , batch_end ) in batches : weights_before_training = self . client . get_parameters ( ) self . model . set_weights ( weights_before_training ) batch_ids = index_array [ batch_start : batch_end ] x = slice_arrays ( x_train , batch_ids ) y = slice_arrays ( y_train , batch_ids ) self . model . train_on_batch ( x , y ) weights_after_training = self . model . get_weights ( ) deltas = subtract_params ( weights_before_training , weights_after_training ) self . client . update_parameters ( deltas ) else : raise ValueError ( 'frequency parameter can be `epoch` or `batch, got {}' . format ( self . frequency ) ) yield [ ]
Train a keras model on a worker and send asynchronous updates
to parameter server
Train a keras model on a worker and send asynchronous updates to parameter server
"""Train a keras model on a worker and send asynchronous updates
        to parameter server
        """
self
data_iterator