def __init__(self,
size=256,
depth=3,
batch_size=32,
keep_prob=0.1,
max_nsteps=1000,
checkpoint_dir="checkpoint",
forward_only=False):
"""Initialize the parameters for an Deep LSTM model.
Args:
size: int, The dimensionality of the inputs into the Deep LSTM cell [32, 64, 256]
learning_rate: float, [1e-3, 5e-4, 1e-4, 5e-5]
batch_size: int, The size of a batch [16, 32]
keep_prob: unit Tensor or float between 0 and 1 [0.0, 0.1, 0.2]
max_nsteps: int, The max time unit [1000]
"""
super(DeepLSTM, self).__init__()
self.size = int(size)
self.depth = int(depth)
self.batch_size = int(batch_size)
self.output_size = self.depth * self.size
self.keep_prob = float(keep_prob)
self.max_nsteps = int(max_nsteps)
self.checkpoint_dir = checkpoint_dir
start = time.clock()
print(" [*] Building Deep LSTM...")
self.cell = LSTMCell(size, forget_bias=0.0)
if not forward_only and self.keep_prob < 1:
d = nn.Dropout(p=keep_prob)
self.cell = d(self.cell)
self.stacked_cell = MultiRNNCellWithSkipConn([self.cell] * depth)
self.initial_state = self.stacked_cell.zero_state(
batch_size, torch.float32)
