default=[100, 128, 12800, 25, 100, 2500, 5, 25, 125])
parser.add_argument('--vectors',
required=False,
type=int,
help='number of vectors',
default=300)
parser.add_argument('--mtx',
required=False,
help='path to mtx file',
nargs="+",
default='none')
args = parser.parse_args()
xclbin_opt = gemx.parse_cfg(args.cfg)
if args.engine == 'spmv':
gemx.createSPMVHandle(args, xclbin_opt)
else:
gemx.createFCNHandle(args, xclbin_opt)
A_buf = []
B_buf = []
C_buf = []
bias_buf = []
nnz_size = []
if args.engine == 'spmv':
min_row = int(xclbin_opt["GEMX_spmvMacGroups"]) * int(
xclbin_opt["GEMX_spmvWidth"])
min_col = int(xclbin_opt["GEMX_ddrWidth"])
if args.mtx == 'none':
#read matrix size from args.matrix
parser.add_argument('--model', required = True, help='model')
parser.add_argument('--xclbin', required = True, help='file path to FPGA bitstream')
parser.add_argument('--cfg', required = True, help='file describing properties of .xclbin')
parser.add_argument('--gemxlib', required = True, help='file path to GEMX host code shared library')
parser.add_argument('--engine', default = 'fcn', choices=['fcn','spmv','uspmv'],help='choose fcn, spmv, uspmv engine')
parser.add_argument('--train', default = False, help='set to True if retrain the model')
args = parser.parse_args()
xclbin_prop = gemx.parse_cfg(args.cfg)
#load xclbin
if args.engine == 'fcn':
gemx.createFCNHandle( args, xclbin_prop )
elif args.engine == 'uspmv':
gemx.createUSPMVHandle( args, xclbin_prop )
else:
gemx.createSPMVHandle( args, xclbin_prop )
train_fd = pd.read_csv(args.data) # Load training data.
IDcol = 'Run' # A column used to identified the run for data collection; not an independent variable.
target = 'Class' # The column name for our dependent variable.
predictors = [x for x in train_fd.columns if x not in [target, IDcol]] # Define column names to use as independent variables.
# Encode class values as integers
encoder = LabelEncoder()
encoder.fit(train_fd[target])
encoded_Y = encoder.transform(train_fd[target])
# Convert integers to dummy variables (i.e. one hot encoded)
train_y = np_utils.to_categorical(encoded_Y)
num_classes = len(train_fd[target].unique())
model = create_keras_model(train_fd[predictors].values.shape[1],num_classes)
