print("WARNING: You have a CUDA device, so you should probably run with --cuda")
batch_size = args.batch_size
seq_len = args.seq_len # The size to memorize
epochs = args.epochs
iters = args.iters
T = args.blank_len
n_steps = T + (2 * seq_len)
n_classes = 10 # Digits 0 - 9
n_train = 10000
n_test = 1000
print(args)
print("Preparing data...")
train_x, train_y = data_generator(T, seq_len, n_train)
test_x, test_y = data_generator(T, seq_len, n_test)
channel_sizes = [args.nhid] * args.levels
kernel_size = args.ksize
dropout = args.dropout
model = TCN(1, n_classes, channel_sizes, kernel_size, dropout=dropout)
if args.cuda:
model.cuda()
train_x = train_x.cuda()
train_y = train_y.cuda()
test_x = test_x.cuda()
test_y = test_y.cuda()
"WARNING: You have a CUDA device, so you should probably run with --cuda"
)
batch_size = args.batch_size
seq_len = args.seq_len # The size to memorize
epochs = args.epochs
iters = args.iters
T = args.blank_len
n_steps = T + (2 * seq_len)
n_classes = 10 # Digits 0 - 9
n_train = 10000
n_test = 1000
print(args)
print("Preparing data...")
train_x, train_y = data_generator(T, seq_len, n_train)
test_x, test_y = data_generator(T, seq_len, n_test)
channel_sizes = [args.nhid] * args.levels
kernel_size = args.ksize
dropout = args.dropout
model = TCN(1, n_classes, channel_sizes, kernel_size, dropout=dropout)
if args.cuda:
model.cuda()
train_x = train_x.cuda()
train_y = train_y.cuda()
test_x = test_x.cuda()
test_y = test_y.cuda()
criterion = nn.CrossEntropyLoss()