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)
