batch_size = args.batch_size
seq_length = args.seq_len
epochs = args.epochs
print(args)
print("Producing data...")
X_train, Y_train = data_generator(50000, seq_length)
X_test, Y_test = data_generator(1000, seq_length)
# Note: We use a very simple setting here (assuming all levels have the same # of channels.
# default param: nhid=30; levels=8 -> include 2**8 = 254; k_size=7;
channel_sizes = [args.nhid]*args.levels
kernel_size = args.ksize
dropout = args.dropout
model = TCN(input_channels, n_classes, channel_sizes, kernel_size=kernel_size, dropout=dropout)
if args.cuda:
model.cuda()
X_train = X_train.cuda()
Y_train = Y_train.cuda()
X_test = X_test.cuda()
Y_test = Y_test.cuda()
lr = args.lr
optimizer = getattr(optim, args.optim)(model.parameters(), lr=lr)
def train(epoch):
global lr
model.train()
n_classes = 1
batch_size = args.batch_size
seq_length = args.seq_len
epochs = args.epochs
print(args)
print("Producing data...")
X_train, Y_train = data_generator(50000, seq_length)
X_test, Y_test = data_generator(1000, seq_length)
# Note: We use a very simple setting here (assuming all levels have the same # of channels.
channel_sizes = [args.nhid]*args.levels
kernel_size = args.ksize
dropout = args.dropout
model = TCN(input_channels, n_classes, channel_sizes, kernel_size=kernel_size, dropout=dropout)
if args.cuda:
model.cuda()
X_train = X_train.cuda()
Y_train = Y_train.cuda()
X_test = X_test.cuda()
Y_test = Y_test.cuda()
lr = args.lr
optimizer = getattr(optim, args.optim)(model.parameters(), lr=lr)
def train(epoch):
global lr
model.train()
n_classes = 1
batch_size = args.batch_size
seq_length = args.seq_len
epochs = args.epochs
print(args)
print("Producing data...")
X_train, Y_train = data_generator(50000, seq_length)
X_test, Y_test = data_generator(1000, seq_length)
# Note: We use a very simple setting here (assuming all levels have the same # of channels.
channel_sizes = [args.nhid]*args.levels
kernel_size = args.ksize
dropout = args.dropout
model = TCN(input_channels, n_classes, channel_sizes, kernel_size=kernel_size, dropout=dropout)
if args.cuda:
model.cuda()
X_train = X_train.cuda()
Y_train = Y_train.cuda()
X_test = X_test.cuda()
Y_test = Y_test.cuda()
lr = args.lr
optimizer = getattr(optim, args.optim)(model.parameters(), lr=lr)
def train(epoch):
global lr
model.train()