pyspark/bigdl/nn/keras/topology.py
def fit(self, x, y=None, batch_size=32, nb_epoch=10, validation_data=None, distributed=True):
        """
        Train a model for a fixed number of epochs on a dataset.

        # Arguments
        x: Input data. A Numpy array or RDD of Sample or Image DataSet.
        y: Labels. A Numpy array. Default is None if x is already RDD of Sample or Image DataSet.
        batch_size: Number of samples per gradient update.
        nb_epoch: Number of iterations to train.
        validation_data: Tuple (x_val, y_val) where x_val and y_val are both Numpy arrays.
                         Or RDD of Sample. Default is None if no validation is involved.
        distributed: Boolean. Whether to train the model in distributed mode or local mode.
                     Default is True. In local mode, x and y must both be Numpy arrays.
        """
        if distributed:
            if isinstance(x, np.ndarray) and isinstance(y, np.ndarray):
                training_data = to_sample_rdd(x, y)
                if validation_data:
                    validation_data = to_sample_rdd(*validation_data)
            elif (isinstance(x, RDD) or isinstance(x, DataSet)) and not y:
                training_data = x
            else:
                raise TypeError("Unsupported training data type: %s" % type(x))
            callBigDlFunc(self.bigdl_type, "fit",
                          self.value,
                          training_data,
                          batch_size,
                          nb_epoch,
                          validation_data)
        else:
            if validation_data:
                val_x = [JTensor.from_ndarray(x) for x in to_list(validation_data[0])]
                val_y = JTensor.from_ndarray(validation_data[1])
            else:
                val_x, val_y = None, None
            callBigDlFunc(self.bigdl_type, "fit",
                          self.value,
                          [JTensor.from_ndarray(x) for x in to_list(x)],
                          JTensor.from_ndarray(y),
                          batch_size,
                          nb_epoch,
                          val_x,
                          val_y,
                          multiprocessing.cpu_count())
def fit(self, x, y=None, batch_size=32, nb_epoch=10, validation_data=None, distributed=True):
        if distributed:
            if isinstance(x, np.ndarray) and isinstance(y, np.ndarray):
                training_data = to_sample_rdd(x, y)
                if validation_data:
                    validation_data = to_sample_rdd(*validation_data)
            elif (isinstance(x, RDD) or isinstance(x, DataSet)) and not y:
                training_data = x
            else:
                raise TypeError("Unsupported training data type: %s" % type(x))
            callBigDlFunc(self.bigdl_type, "fit",
                          self.value,
                          training_data,
                          batch_size,
                          nb_epoch,
                          validation_data)
        else:
            if validation_data:
                val_x = [JTensor.from_ndarray(x) for x in to_list(validation_data[0])]
                val_y = JTensor.from_ndarray(validation_data[1])
            else:
                val_x, val_y = None, None
            callBigDlFunc(self.bigdl_type, "fit",
                          self.value,
                          [JTensor.from_ndarray(x) for x in to_list(x)],
                          JTensor.from_ndarray(y),
                          batch_size,
                          nb_epoch,
                          val_x,
                          val_y,
                          multiprocessing.cpu_count())
def fit ( self , x , y = None , batch_size = 32 , nb_epoch = 10 , validation_data = None , distributed = True ) : if distributed : if isinstance ( x , np . ndarray ) and isinstance ( y , np . ndarray ) : training_data = to_sample_rdd ( x , y ) if validation_data : validation_data = to_sample_rdd ( * validation_data ) elif ( isinstance ( x , RDD ) or isinstance ( x , DataSet ) ) and not y : training_data = x else : raise TypeError ( "Unsupported training data type: %s" % type ( x ) ) callBigDlFunc ( self . bigdl_type , "fit" , self . value , training_data , batch_size , nb_epoch , validation_data ) else : if validation_data : val_x = [ JTensor . from_ndarray ( x ) for x in to_list ( validation_data [ 0 ] ) ] val_y = JTensor . from_ndarray ( validation_data [ 1 ] ) else : val_x , val_y = None , None callBigDlFunc ( self . bigdl_type , "fit" , self . value , [ JTensor . from_ndarray ( x ) for x in to_list ( x ) ] , JTensor . from_ndarray ( y ) , batch_size , nb_epoch , val_x , val_y , multiprocessing . cpu_count ( ) )
Train a model for a fixed number of epochs on a dataset.

        # Arguments
        x: Input data. A Numpy array or RDD of Sample or Image DataSet.
        y: Labels. A Numpy array. Default is None if x is already RDD of Sample or Image DataSet.
        batch_size: Number of samples per gradient update.
        nb_epoch: Number of iterations to train.
        validation_data: Tuple (x_val, y_val) where x_val and y_val are both Numpy arrays.
                         Or RDD of Sample. Default is None if no validation is involved.
        distributed: Boolean. Whether to train the model in distributed mode or local mode.
                     Default is True. In local mode, x and y must both be Numpy arrays.
Train a model for a fixed number of epochs on a dataset .