post_scale[1], 1, 0)
gemx.execute()
gemx.clearInstrBuf()
gemx.getMat(mat_C[0])
gemx.getMat(mat_C[1])
gemx.getMat(mat_C[2])
gemx.getMat(mat_C[3])
test.multiply_and_cmp(mat_C[3], mat_A[3], mat_C[2], mat_bias[3], m_size[3],
n_size[3], post_scale)
if __name__ == '__main__':
np.random.seed(123) # for reproducibility
test = FcnTest()
args, xclbin_opts = gemx.processCommandLine()
gemx.createFCNHandle(args, xclbin_opts)
if xclbin_opts["GEMX_dataType"] == "short":
for j in range(1, 3):
for k in range(1, 8):
for i in range(int(xclbin_opts["GEMX_numKernels"])):
for m, n in ([0, 0], [1, 0]):
test.test_basic_randint(i, xclbin_opts, [j, k], [m, n],
2048)
test.test_basic_size(512, 512, 512, xclbin_opts)
size = 256
while size < 8192:
test_perf_fcn(size, size, size,
xclbin_opts) # run performance measurement
size = size * 2
parser.add_argument('--gemxlib',
required=True,
help='file path to GEMX host code shared library')
parser.add_argument('--engine',
default='fcn',
choices=['fcn', 'uspmv'],
help='choose fcn, 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)
else:
gemx.createUSPMVHandle(args, xclbin_prop)
(x_train, y_train), (x_test, y_test) = reuters.load_data(num_words=1000,
test_split=0.2)
tokenizer = Tokenizer(num_words=1000)
num_classes = np.max(y_train) + 1
model = create_keras_model(num_classes)
model.load_weights(args.model)
if args.train:
train(model, x_train, y_train, x_test, y_test)
x_test = tokenizer.sequences_to_matrix(x_test, mode='binary')