def _get_rnn_cell(mode, num_layers, input_size, hidden_size, dropout,
weight_dropout, var_drop_in, var_drop_state, var_drop_out):
"""create rnn cell given specs"""
rnn_cell = rnn.SequentialRNNCell()
with rnn_cell.name_scope():
for i in range(num_layers):
if mode == 'rnn_relu':
cell = rnn.RNNCell(hidden_size, 'relu', input_size=input_size)
elif mode == 'rnn_tanh':
cell = rnn.RNNCell(hidden_size, 'tanh', input_size=input_size)
elif mode == 'lstm':
cell = rnn.LSTMCell(hidden_size, input_size=input_size)
elif mode == 'gru':
cell = rnn.GRUCell(hidden_size, input_size=input_size)
if var_drop_in + var_drop_state + var_drop_out != 0:
cell = contrib.rnn.VariationalDropoutCell(
cell, var_drop_in, var_drop_state, var_drop_out)
rnn_cell.add(cell)
if i != num_layers - 1 and dropout != 0:
rnn_cell.add(rnn.DropoutCell(dropout))
if weight_dropout:
apply_weight_drop(rnn_cell, 'h2h_weight', rate=weight_dropout)
return rnn_cell
def __init__(self, voc, **kwargs):
super(Model, self).__init__(**kwargs)
with self.name_scope():
self.conv1 = nn.Conv2D(channels=n_nodes_cnn,
kernel_size=5,
strides=2)
self.conv2 = nn.Conv2D(channels=n_nodes_cnn,
kernel_size=5,
strides=2)
self.pool1 = nn.MaxPool2D(pool_size=2)
self.pool2 = nn.MaxPool2D(pool_size=2)
self.norm1 = nn.BatchNorm()
self.norm2 = nn.BatchNorm()
self.dense1 = nn.Dense(n_nodes_hidden)
self.dense2 = nn.Dense(voc)
self.encoder = rnn.SequentialRNNCell()
with self.encoder.name_scope():
self.encoder.add(rnn.LSTMCell(n_nodes_rnn))
self.decoder = rnn.SequentialRNNCell()
with self.decoder.name_scope():
self.decoder.add(rnn.LSTMCell(n_nodes_rnn))