use_gpu = True cuda.check_cuda_available() cuda.get_device(args.gpu).use() batchsize = 60 n_epoch = 100 n_units = 30 #loding dataset data = convert.mfcc_convert() data2 = convert.mfcc_convert2(data) mfcc = {} mfcc['data'] = np.array(data2) mfcc['data'] = mfcc['data'].astype(np.float32) mfcc['data'] /= 255 mfcc['target'] = convert.gen_target() mfcc['target'] = mfcc['target'].astype(np.int32) #data 1800 N_train = 1500 x_train, x_test = np.split(mfcc['data'], [N_train]) y_train, y_test = np.split(mfcc['target'], [N_train]) N_test = y_test.size state = make_initial_state(batchsize, n_units) model = LSTMmodel(len(x_train[0]), n_units, 10) if use_gpu: model.to_gpu() x_train = cuda.to_gpu(x_train)
cuda.get_device(args.gpu).use() batchsize = 30 n_epoch = 100 n_units = 400 #loding dataset imgdata = np.loadtxt("./img.csv",delimiter=",") img = {} img['data'] = imgdata img['data'] = img['data'].astype(np.float32) img['data'] /= 255 img['target'] = convert.gen_target() img['target'] = img['target'].astype(np.int32) N_train = 1500 x_train, x_test = np.split(img['data'], [N_train]) y_train, y_test = np.split(img['target'], [N_train]) N_test = y_test.size state = make_initial_state(batchsize,n_units) model = LSTMmodel(len(x_train[0]),n_units,10) if use_gpu:
cuda.check_cuda_available() cuda.get_device(args.gpu).use() batchsize = 60 n_epoch = 100 n_units = 30 #loding dataset data = convert.mfcc_convert() data2 = convert.mfcc_convert2(data) mfcc = {} mfcc['data'] = np.array(data2) mfcc['data'] = mfcc['data'].astype(np.float32) mfcc['data'] /= 255 mfcc['target'] = convert.gen_target() mfcc['target'] = mfcc['target'].astype(np.int32) #data 1800 N_train = 1500 x_train, x_test = np.split(mfcc['data'], [N_train]) y_train, y_test = np.split(mfcc['target'], [N_train]) N_test = y_test.size state = make_initial_state(batchsize,n_units) model = LSTMmodel(len(x_train[0]),n_units,10) if use_gpu: model.to_gpu()
cuda.check_cuda_available() cuda.get_device(args.gpu).use() batchsize = 30 n_epoch = 100 n_units = 400 #loding dataset imgdata = np.loadtxt("./img.csv", delimiter=",") img = {} img['data'] = imgdata img['data'] = img['data'].astype(np.float32) img['data'] /= 255 img['target'] = convert.gen_target() img['target'] = img['target'].astype(np.int32) N_train = 1500 x_train, x_test = np.split(img['data'], [N_train]) y_train, y_test = np.split(img['target'], [N_train]) N_test = y_test.size state = make_initial_state(batchsize, n_units) model = LSTMmodel(len(x_train[0]), n_units, 10) if use_gpu: model.to_gpu() x_train = cuda.to_gpu(x_train) x_test = cuda.to_gpu(x_test)